LITERATURE REVIEW
Natural fiber reinforced polymer composite
Biocomposites are created by integrating two or more materials with different properties, typically consisting of a matrix that binds reinforcements, which can be fibers or particles These biocomposites can be categorized as either partly eco-friendly or green, depending on the nature of their components (Peỗas et al., 2018).
Figure 1.1 Classification of biocomposites (Peỗas et al., 2018)
Biocomposites are primarily composed of natural fibers and a matrix, offering numerous advantages including low density, excellent mechanical properties, and a beneficial environmental impact Their applications vary across different fields, influenced by production costs and functional characteristics (Nassar et al., 2017).
Natural fibers are categorized by their origin into three types: animal, mineral, and plant, with plant-based fibers being the most widely utilized in various technical applications These fibers possess complex cellular structures primarily composed of cellulose, hemicellulose, and lignin The mechanical properties of plant fibers are influenced by factors such as the species of the plant, its growth conditions, age, and the extraction methods used Understanding the mechanical, thermal, physical, and chemical properties of these natural fibers is crucial, as they significantly impact the machinability of biocomposites A summary of the mechanical properties of plant-based fibers is provided in Table 1.1.
Figure 1.2 Images of plant-based fibers (Salit et al., 2015)
Table 1.1 Mechanical properties of natural fibers and particles used in biocomposites
1.1.2 Biocomposites reinforced with miscanthus fibers
Miscanthus, a member of the Poaceae grass family, can be cultivated in diverse climates Its chemical composition typically consists of 40-60% cellulose, 20-40% hemicellulose, and 10-30% lignin However, factors such as weather conditions during the growth period, soil quality, and harvesting time significantly impact these chemical properties (Moll et al., 2020).
Chopped miscanthus fibers, as illustrated in Figure 1.3 (Chen et al., 2017), exhibit a variety of shapes and sizes, necessitating sieving to achieve uniform fiber dimensions Specifically, Figures 1.3a and 1.3b depict chopped fibers measuring 0-2 mm and 2-4 mm, respectively, while Figure 1.3c showcases miscanthus powder.
Chopped miscanthus fibers, typically less than 5 mm in size, are frequently utilized in biocomposites when mixed with a polymer matrix through injection and compression molding processes Research by Bourmaud and Pimbert (2008) indicates that incorporating miscanthus fibers significantly enhances the tensile modulus of polypropylene (PP) and poly(L-lactic acid) (PLLA) Additionally, Nagarajan et al (2013) demonstrated that miscanthus fiber-reinforced biocomposites improve the tensile strength and Young's modulus of the PHBV/PBAT matrix while also providing superior thermal stability.
Muthuraj et al (2016) studied the effects of processing parameters on the impact strength of the PBS/PBAT biocomposite reinforced with miscanthus fibers It is showed that the
The addition of miscanthus fibers significantly enhances the stiffness and flexural strength of the PBS/PBAT blend matrix Notably, the fiber length plays a crucial role, with shorter miscanthus fibers demonstrating the highest impact strength in the biocomposite, outperforming other processing parameters.
Muthuraj et al (2017) investigated a blend matrix of PBS/PBAT reinforced with chopped miscanthus fibers, finding that the tensile and flexural modulus of the biocomposites increased with miscanthus fiber content from 30wt% to 50wt% However, the tensile and impact strength of the PBS/PBAT/miscanthus biocomposite remained lower than that of the pure blend matrix due to insufficient interfacial interaction among the components To address this issue, an interactive compatibilizer (MAHgPBS/PBAT) was introduced, enhancing the overall performance The study concluded that miscanthus fiber reinforcement in PBS/PBAT biocomposites could significantly boost both economic viability and functional performance.
Table 1.2 Biocomposites reinforced with miscanthus fibers
Injection molding PVA - (Kirwan et al., 2007)
Injection molding PLLA, and PP MA (Bourmaud & Pimbert, 2008) Injection molding PHBV/PBAT - (Nagarajan et al., 2013)
Injection molding PP MA (Girones et al., 2016)
Injection molding PBS/PBAT MA (Muthuraj et al., 2017)
Co-Injection molding PBS/PHBV - (Zhang et al., 2014)
Thermo-compression PP/PLA - (Ragoubi et al., 2012)
Injection molding PBS/PBAT - (Muthuraj et al., 2016)
1.1.3 Biocomposites reinforced with biocarbon particles
Biocarbon is generated through the pyrolysis of plant-based fibers, with temperatures typically ranging from 500 °C to 900 °C The pyrolysis temperature significantly influences the mechanical properties of the resulting biocarbon Additionally, the size of biocarbon particles varies based on the milling (such as ball milling and hammer milling) and sieving processes SEM images of these biocarbon particles are illustrated in Figure 1.4 (Behazin et al., 2017b).
Biocomposites are created by combining biocarbon particles with a matrix through injection or compression molding processes As illustrated in Table 1.3, various biocomposites incorporate these biocarbon particles The mechanical properties of these composites are significantly influenced by the size and weight ratio of the biocarbon particles utilized.
Research indicates that biocomposites featuring smaller particle sizes exhibit enhanced strength and modulus, albeit with reduced impact toughness (Behazin et al., 2017b) Additionally, the shape and size of biocarbon particles significantly influence the properties of biocomposites, with smaller particles contributing to improved impact strength (Ogunsona et al., 2017b) This suggests that biocarbon holds considerable promise as a viable alternative in composite materials.
Recent studies, including those by Behazin et al (2017b) and Codou et al (2018), highlight the significance of biocarbon particles in enhancing the mechanical properties of biocomposites Specifically, a reduction in the size of these particles has been shown to improve material strength, suggesting their potential as effective reinforcing fillers in future applications.
Li et al (2020) demonstrated that incorporating biocarbon particles into the PHBV matrix enhances Young’s modulus while reducing tensile and flexural strength Additionally, the study revealed that the inclusion of biocarbon particles raises the heat deflection temperature and decreases the coefficient of linear thermal expansion, resulting in improved dimensional stability of the matrix.
Behazin et al (2017a) explored biocomposites enhanced with biocarbon particles derived from the pyrolysis of chopped miscanthus fibers at varying temperatures (LTBioC at 500°C and HTBioC at 900°C) Their findings revealed that the pyrolysis temperature significantly influences the surface properties and the interaction between biocarbon and the matrix Notably, biocomposites reinforced with high-temperature pyrolyzed biocarbon (HTBioC) exhibit a superior balance of stiffness and toughness compared to those reinforced with low-temperature pyrolyzed biocarbon (LTBioC).
Wang et al (2018) found that increasing milling time reduces biocarbon particle size, which enhances the impact strength of biocomposites The impact strength of biocarbon/PP composites matches that of Talc/PP composites, indicating that biocarbon/PP composites can significantly contribute to the automotive industry’s objective of reducing vehicle weight.
Table 1.3 Biocomposites reinforced with biocarbon particles
Injection molding PA6 - (Emmanuel O Ogunsona et al., 2017a)
Injection molding PA 6,10 - (Emmanuel O Ogunsona et al., 2017b)
Compression molding PLA - (Snowdon et al., 2019)
Injection molding PHBV - (Li et al., 2020)
Injection molding PP - (Abdelwahab et al., 2019)
Co-Injection molding PA6/PP MAPP (Codou et al., 2018)
Injection molding PP/POE MAPP (Behazin et al., 2017b)
Injection molding PP/POE - (Behazin et al., 2017a)
Injection molding PP - (Wang et al., 2018)
Drilling of NFRPs (Natural fiber reinforced polymer composites)
Hole-making is a crucial machining operation for natural fiber-reinforced polymers (NFRPs), essential for achieving dimensional accuracy and surface finish, which in turn facilitates the assembly of components Unlike metallic materials, the machinability of NFRPs is influenced by their anisotropic and heterogeneous properties, as well as the variety of natural fibers used The cutting forces and temperatures generated during the drilling process can lead to machining-induced failures, including debonding, fiber pull-out, delamination, and thermal damage Therefore, it is vital to thoroughly investigate the factors involved in the drilling process to optimize outcomes.
Drilling is the most common material removal operation in the machining of the composite
Drilling is a process essential for creating holes in assembly, characterized by two fundamental movements: primary rotary motion and auxiliary linear feed motion The spindle speed generates the primary rotational motion of the drill bit, enabling effective cutting, while the spindle axis's linear movement facilitates feeding into the workpiece As illustrated in Figure 1.5, the cutting geometry of a two-flute twist drill features two primary cutting edges that form the drill bit's point angle, with each edge functioning as an individual point cutting tool.
1.2.2 Effects of reinforcement and matrix of NFRPs
The machinability of natural fiber reinforced polymers (NFRPs) is significantly influenced by the types of natural fibers and matrix used in the composite A study by Ismail et al (2016) examined the drilling characteristics of carbon fiber reinforced polymer (CFRP) and hemp fiber reinforced polymer (HFRP) The findings revealed that drilling parameters have a considerable impact on the damage to the machined surfaces of both composites, with CFRP exhibiting greater damage compared to HFRP under identical cutting conditions Additionally, CFRP demonstrated lower surface roughness and higher delamination rates than HFRP The chip formation during machining also differed, with CFRP producing discontinuous, powder-like chips, while HFRP resulted in continuous, coiled, and longer chips.
When drilling HFRP composites with an HSS twist drill, minimal tool wear is observed, in contrast to CFRP composites, which exhibit significant tool wear under the same cutting conditions (Sheikh-Ahmad, 2009).
Pailoor et al (2019) investigated the impact of fiber type, coupling agent, and fiber weight ratio on thrust force and delamination during the drilling of jute/PP biocomposites Their findings revealed that chopped jute fiber reinforced polypropylene experienced greater delamination damage compared to long fibers, with delamination increasing as the fiber weight ratio rose in composites lacking a coupling agent Additionally, thrust force was notably higher when drilling long jute fiber reinforced polypropylene, particularly at a fiber weight ratio of 30% Ismail et al (2016) further emphasized that the fiber aspect ratio significantly influences the machinability of hemp fiber reinforced polycaprolactone (HFRP), with both delamination factors and surface roughness escalating alongside the fiber aspect ratio.
Chegdani et al (2020) examined how fiber orientation affects the machinability of UDF/PP (polypropylene reinforced with unidirectional flax fibers) during orthogonal cutting Their findings revealed that fiber orientation significantly influences the machined surface quality, with the optimal surface roughness occurring at a 45° orientation and the poorest at 0° Additionally, the study noted that the least cutting energy was required at 45°, while the highest was observed at 90° Overall, the research indicates that a 45° fiber orientation enhances the machinability of UDF/PP.
A study conducted in 2014 investigated the machinability of two natural fiber reinforced polymers (NFRPs), sisal/PP and sisal/epoxy, utilizing three different drill geometries: parabolic, four-facet, and step drill The findings revealed that the chips produced during drilling were continuous for sisal/PP and discontinuous for sisal/epoxy, likely due to the differing polymer matrices Additionally, drilling sisal/PP resulted in less damage compared to sisal/epoxy Notably, the thrust force generated when drilling sisal/PP was greater than that of sisal/epoxy with a step drill, while the lowest thrust force was observed with a parabolic drill on sisal/PP, indicating a complex interaction between drill geometry and the NFRP composite.
Machining parameters significantly influence the machinability of non-fiber-reinforced plastics (NFRPs), as highlighted in various studies Abilash and Sivapragash (2016) examined the drilling of bamboo/polyester composites, revealing that drill diameter and feed rate are crucial factors that can lead to delamination failure Their findings indicate that a smaller drill diameter and lower feed rate result in a reduced delamination factor Similarly, Venkateshwaran and ElayaPerumal (2013) found that increased feed rate and spindle speed during the drilling of banana/epoxy composites correspond to a higher delamination factor.
Research by Manickam and Gopinath (2017) revealed that in the machining of sisal/glass fiber reinforced polymer composites, the feed rate significantly influences thrust force, with spindle speed and drill diameter following in importance An increase in thrust force is observed with larger drill diameters and higher feed rates, while lower spindle speeds contribute to this increase Additionally, Jayabal et al (2011) found that when drilling glass-coir-polyester hybrid composites with HSS twist drills, the feed rate most notably affects thrust force and torque, whereas drill diameter plays a critical role in tool wear.
Research by Roy Choudhury et al (2018) highlights that drilling parameters significantly influence the machinability of woven nettle/epoxy composites, with increased feed rates leading to higher thrust force, torque, delamination, and surface roughness Additionally, higher spindle speeds raise drilling temperatures and surface roughness while decreasing thrust force Similarly, Vinayagamoorthy (2017) found that cutting parameters greatly impact the machinability of jute-steel-polyester hybrid composites, where increased feed rates and drill diameters result in higher thrust force and surface roughness, along with increased machined-surface damage Conversely, reducing spindle speed can enhance thrust force and surface roughness while minimizing damage Debnath et al (2017) also reported that increased feed rates during drilling of woven nettle/PP composites lead to greater thrust force and torque, underscoring the critical role of drilling parameters in composite machining.
Research indicates that feed rate significantly influences thrust force during drilling operations across various composite materials Maleki et al (2019) found that increasing the feed rate enhances thrust force when drilling jute/epoxy composites, a trend also observed in flax/epoxy composites Similarly, Chaudhary and Gohil (2016) demonstrated that in cotton-polyester composites, thrust force increases with higher feed rates Furthermore, Palanikumar and Valarmathi (2016) studied drilling in medium-density fiberboard (MDF) and reported that while feed rate notably affects thrust force, spindle speed does not have a statistically significant impact.
1.2.4 Effects of tool material and geometry
Research indicates that the tool material and geometry significantly affect the machinability of non-fibrous reinforced polymer (NFRP) composites Debnath et al (2014) found that a parabolic drill generates lower thrust force and torque compared to step and four-facet drills when drilling sisal/PP and sisal/epoxy composites, with the step drill producing the highest values Similar findings were reported for nettle/PP composites (Debnath et al., 2017), highlighting that tool geometry has a more substantial impact on thrust force and torque than feed rate and spindle speed Therefore, using a parabolic drill is recommended for drilling NFRP composites.
A study conducted in 2016 explored the machinability of woven jute/PP during drilling with various drill geometries, including twist, step, and parabolic drills The findings revealed that tool geometry significantly influences thrust force, torque, and delamination Notably, the parabolic drill demonstrated superior cutting performance, resulting in reduced drilling force and enhanced surface quality compared to the other drill types.
Roy Choudhury et al (2018) examined the drilling of woven nettle/epoxy composites using various drill geometries, including dagger, 4-facet, 8-facet, step, and parabolic drills Their findings revealed that the 8-facet and dagger drills produced lower thrust force, drilling temperature, surface roughness, and drilling-induced damage compared to other drill types Conversely, the step drill resulted in higher temperatures, increased surface roughness, and greater drilling-induced damage Additionally, Bajpai and Singh (2013) noted that hollow drills exhibited superior cutting performance over twist drills, generating less thrust force when drilling sisal/PP composites The machinability of non-fiber-reinforced plastics (NFRP) is significantly influenced by the drill's point angle, with higher thrust forces and drilling-induced damage occurring as the point angle increases (Vinayagamoorthy, 2017; Palanikumar & Valarmathi, 2016).
Chegdani and El Mansori (2018) explored the drilling of flax/PP composites using identical drill geometries with varying coating materials, including uncoated, TiB2 coated, and diamond coated drills Their findings revealed that specific cutting energy rises when using coated tools, with diamond-coated drills exhibiting the highest specific cutting energy under identical cutting conditions This increase is attributed to the thicker coating, which enhances the cutting edge radius and alters the drill's intrinsic friction by modifying its surface morphology.
Milling of NFRP composites
NFRP composites are typically manufactured close to their final shape, but secondary processing is essential for achieving the necessary dimensional accuracy and surface finish required for assembly Milling is a common machining technique used to create slots, pockets, and trims, ensuring precise geometric and dimensional specifications (Nassar et al., 2017) Research has shown that the machinability of NFRP composites during milling is significantly influenced by various factors, including cutting parameters, tool geometry and materials, as well as the properties of the reinforcement and matrix, and the processing method of the NFRP composite (Rajmohan et al., 2019).
Babu et al (2013) studied the impact of cutting parameters on the machinability of slot milling in glass fiber reinforced polyester (GFRP) and various natural fiber reinforced polymer (NFRP) composites, such as hemp, jute, and banana fiber reinforced polyester (HFRP, JFRP, BFRP) Their findings indicate that feed rate and spindle speed significantly affect delamination and surface roughness, with increased feed rate leading to greater delamination and roughness In contrast, higher spindle speeds reduce both delamination and surface roughness Additionally, NFRP composites demonstrate superior cutting performance compared to GFRP, exhibiting less delamination and smoother surfaces.
Research by Balasubramanian et al (2016) indicates that spindle speed and depth of cut significantly influence cutting force during the milling of woven jute/polyester composites, with cutting force increasing alongside feed rate and depth of cut, while decreasing with higher spindle speeds Azmi et al (2018) found that feed rate has the most substantial impact on surface roughness in kenaf/epoxy composite milling, with increased feed rates and decreased spindle speeds leading to rougher surfaces Additionally, Faissal Chegdani et al (2016) highlighted that the helix angle of the end mill cutter affects surface quality, as uncut fiber damage increases surface roughness with higher helix angles Çelik et al (2019) examined the effects of cutting parameters, flute numbers, and fiber orientation on machinability in jute/epoxy composites, revealing that a ±45° fiber orientation results in lower cutting forces, reduced delamination factors, and increased surface roughness They also noted that higher feed rates correlate with increased delamination, cutting force, and surface roughness, while elevated spindle speeds decrease cutting force and surface roughness but increase delamination Furthermore, an increased number of flutes in the cutter leads to lower cutting force and surface roughness Lastly, Çelik and Alp (2020) compared milling outcomes of jute/epoxy and flax/epoxy composites using various end mills, finding that jute/epoxy exhibited lower delamination factors and surface roughness, but higher cutting forces under identical conditions.
The machinability of jute/epoxy and flax/epoxy composites is significantly influenced by cutting parameters and milling tools An increase in feed rate correlates with heightened surface roughness, cutting force, and delamination factor Conversely, higher spindle speeds yield lower cutting force and surface roughness while enhancing the delamination factor Among the tools tested, the WC end mill produced the lowest cutting force, delamination factor, and surface roughness, whereas the HSS end mill resulted in the highest values for these parameters.
Dust emission during machining
Songmene et al (2008a) studied dust formation during drilling of metal The drilling process is described in Figure 1.6 Sources of dust generation are presented as follows
- Q1 is the amount of dust generated in the primary deformation zone, which is the shearing plane where the materials undergo high deformation
- Q2 is the amount of dust created in the secondary deformation zone at the chip-tool interface where the friction between the tool and the chip occurs
- Q3 is the amount of dust produced by little friction in the workpiece-tool interface
- Q4 is the amount of dust emitted by compressive bending deformation and chip breaking in the chip's outer surface
- Q5 is the amount of dust created by the friction, deformation, and segmentation of the cut chip in the drill's helical surface
- Q6 is the amount of dust produced by the chisel edge of the drill rubbing on the workpiece The total amount of dust emitted (Q) is calculated as follows
According to Songmene et al (2008a), the generation of fine particles during machining is significantly influenced by cutting speed, feed rate, and the type of workpiece material Specifically, an increase in feed rate and cutting speed leads to a higher production of fine dust, with ductile materials producing more fine particles compared to brittle materials.
1.4.2 Particles emission during machining of metallic workpiece
Numerous studies highlight the significance of fine dust produced during machining due to its direct impact on the health of machine-tool operators and the environment According to Songmene et al (2008a), fine particles measuring less than 2.5 microns in diameter (PM2.5) can be easily inhaled into the lungs, posing serious health risks Therefore, ensuring the safety of both operators and the environment has emerged as a critical factor in evaluating machining process performance.
Research by Khettabi et al (2007) indicates that fine particles (PM2.5) generated during metal cutting are influenced by tool geometry and cutting conditions, with tools featuring a 90° lead angle producing less dust than those with 70° or 110° angles Further studies by Khettabi et al (2010) reveal that dust emissions are significantly affected by the workpiece material and cutting parameters, noting that increased cutting speeds lead to higher dust emissions in brittle materials, although these emissions remain lower than those from ductile materials Additionally, Kamguem et al (2013) explored the effects of coated tools on dust generation.
Research by Songmene et al (2008a) highlights the sources of dust formation during drilling, specifically focusing on the impact of carbide tools on fine dust generated while slot milling various materials, including 6061-T6, 2024-T351, and 7075-T6 Their findings indicate that TiCN-coated tools generate less fine dust compared to multilayer-coated tools that consist of TiCN, Al2O3, and TiN.
Zaghbani et al (2009) investigated fine dust generated from high-speed milling of aluminum alloy 6061-T6 under both dry and wet conditions, revealing that the primary dust source is the shearing zone They found that the deformation conditions during chip formation significantly influence dust emission, while lubrication conditions alter the particle forms produced Notably, dry milling results in a higher emission of particles sized 1 to 10 microns compared to wet milling, although nanoparticle emissions remain largely unaffected by lubrication and cutting conditions.
Songmene et al (2008b) explored how the initial preparation of workpieces, tool condition and geometry, and machining strategies impact fine particle emissions during the drilling of various materials Their findings highlighted that friction is a crucial factor in the formation of dust.
A dull drill bit generates more fine dust compared to a sharp one, with the peak dust production occurring during the initial penetration into the workpiece Furthermore, the amount of dust created is greatly influenced by the initial preparation of the workpiece.
1.4.3 Particles emission in machining composites
Saidi et al (2015) examined how polishing conditions affect particle emission, distribution, and dispersion in granite polishing Their findings indicated that most material is removed from the granite surface during the initial phase of polishing with coarse abrasives, leading to higher fine dust production Additionally, they discovered that increasing spindle speed enhances microparticle generation, with significant concentrations found away from the particle creation area Notably, the majority of the fine dust generated is smaller than 2.5 microns in diameter.
Saidi et al (2019) investigated how cutting parameters influence fine dust emission during the dry polishing of granite Their findings revealed that both feed rate and spindle speed significantly impact dust generation, with higher spindle speeds and lower feed rates leading to increased emissions of fine and ultrafine particles Specifically, an increase in spindle speed enhances material removal, resulting in greater dust output, while a higher feed rate promotes a dilution effect that reduces emissions Notably, the concentration of fine and ultrafine particles is highest near the polishing tool, with fine particles peaking between 0.626 and 0.777 microns and ultrafine particles ranging from 14.1 to 34.6 nanometers.
According to Marani et al (2018), the emission of dust during the milling of aluminum matrix composites, specifically Al-20Mg2Si-2Cu with bismuth (Bi) and barium (Ba), is significantly influenced by machining conditions and microstructure Dust generation rises with cutting speeds between 300 and 700 m/min, but subsequently declines as cutting speeds reach 1100 m/min Additionally, ultrafine particles are emitted from these composites.
Bi and Ba smaller than that of the base composite
Haddad et al (2014) investigated how different cutting conditions and tool geometries, including uncoated carbide burrs, diamond-coated carbide burrs, and four-flute diamond-coated end mills, influence the dust produced during the trimming process of carbon fiber reinforced polymer (CFRP).
The geometry of the cutting tool significantly impacts the production of dust particles, while the coating applied has minimal effect on dust emissions Notably, a reduction in cutting speed or an increase in feed rate leads to a decrease in fine dust generation Most of the fine dust produced is smaller than 2.5 microns in size.
Fine dust generated during machining is influenced by various factors, including the machining process (such as drilling, milling, and grinding), cutting parameters, tool characteristics (geometry, coating, and material), workpiece material, and cutting conditions To minimize fine dust production, it is essential to select appropriate cutting parameters, tools, and machining strategies tailored to each specific case Notably, the generation of fine dust during the machining of biocomposites has received limited research attention, which this study aims to address.
Adaptive Neuro-Fuzzy Inference System (ANFIS)
ANFIS, or Adaptive Neuro-Fuzzy Inference System, is an advanced artificial intelligence technique that merges the strengths of adaptive networks and fuzzy inference systems It utilizes a supervised-learning adaptive network that fine-tunes its parameters based on training data to achieve a specific error tolerance Meanwhile, the fuzzy inference system effectively represents uncertain situations through rule-based decision-making By integrating the learning capabilities of artificial neural networks with the interpretative power of fuzzy logic, ANFIS addresses the common limitation of neural networks, where weight values lack explainability This unique combination makes ANFIS a valuable tool for tackling various practical problems.
Figure 1.7 illustrates the fundamental architecture of ANFIS, which includes m inputs (x1 to xm), each associated with n membership functions (MFs), a fuzzy rule base comprising R rules (where R = m.n), and a single output response (y) The ANFIS framework is organized into five distinct layers, each playing a crucial role in the overall function of the system.
- Layer 1 is the fuzzification layer It transforms the crisp inputs (xi) into linguistic labels (Aij) with a membership degree The output of node ij is determined by equations (1.2)
𝑂 𝑖𝑗 1 = 𝜇 𝑖𝑗 (𝑥 𝑖 ) ; i = 1,m; j =1,n (1.2) Where μij (xi) represents the j th membership functions for the input xi, typically, the various membership functions are used, such as triangular, generalized bell, etc
Assuming that gbellmf (general bell membership function) is applied to node ij, the corresponding output determined as follows
Where a, b, and c are the parameters of the membership function These parameters are called as premise parameters
Figure 1.7 The basic structure of ANFIS (Samanta et al., 2008)
Layer 2 functions as the product layer, where the output from node r signifies the weighting factor, or firing strength, of the r-th rule The output (wr) is calculated by multiplying all its inputs together.
- Layer 3 is the normalized layer The output of node r in this layer represents the normalized weighting factor of the r th rule as follows
- Layer 4 is the defuzzification layer The defuzzification relationship between the input and output are calculated in equation (1.6)
Where pri and qr are called the consequence parameters
- Layer 5 is the output layer The overall output of ANFIS as a sum of all weighted outputs of the rules
Where R is the number of rules
Sen et al (2017) utilized Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to create predictive models for cutting force, surface roughness, and cutting temperature in milling Inconel 690 alloy Their findings indicated that the ANFIS-based models exhibited smaller errors compared to the ANN-based models Therefore, the ANFIS model is deemed the most suitable for predicting the machinability of Inconel alloy.
Samanta et al (2008) developed predictive models for surface roughness during the milling of 6061 aluminum alloy using multivariate regression analysis (MRA) and soft computing techniques, specifically artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) The study utilized cutting parameters such as feed rate, spindle speed, and depth of cut as inputs for modeling surface roughness Results indicated that the ANFIS model outperformed the others in prediction accuracy, closely aligning with actual values, while ANN and MRA followed Consequently, both ANN and ANFIS models can effectively generate data for various machining conditions within established ranges without requiring further testing.
Kumar and Hynes (2019) created an ANFIS-based model to forecast surface roughness during the drilling of galvanized steel sheets The model utilizes key thermal drilling parameters, such as spindle speed, tool angle, and workpiece thickness, as input variables Results show that the ANFIS model's predictions align closely with experimental values, demonstrating its effectiveness in predicting drilled-surface roughness.
Savkovic et al (2019) utilized an ANFIS-based model to predict cutting temperatures during the turning of hardened steel (EN 90MnCrV8), incorporating machining parameters such as cutting speed, feed, depth of cut, and tool material as inputs The model demonstrated high predictive accuracy when comparing experimental data with predicted values Similarly, Shivakoti et al (2019) developed an ANFIS-based model for turning stainless steel 202, using cutting parameters as inputs to forecast material removal rate (MMR) and surface roughness (Ra) The findings indicated that the ANFIS models exhibited good predictability, with predicted values aligning closely with actual results.
Marani et al (2019) developed ANFIS-based models to effectively predict surface roughness and cutting force in the milling of metal matrix composites (Al-20Mg2Si) The models utilized key inputs such as feed rate, cutting speed, and particle size Their findings indicate that the choice and quantity of membership functions in the ANFIS structure significantly influence the accuracy of predictions The results demonstrated a strong correlation between the predicted values and empirical measurements.
Abbas et al (2017) created a regression analysis model and an Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict surface roughness in turning Grade-H high-strength steel, utilizing cutting parameters such as cutting speed, feed rate, and depth of cut as inputs Their findings indicated that both models achieved high predictive accuracy, with the ANFIS model demonstrating slightly superior accuracy compared to the regression analysis.
Summary and conclusive remarks
Some concerned issues in the machining of the NFRP composites are drawn from the published literature as follows
- The machinability of biocomposites strongly depends on the reinforcements and matrix used in biocomposites Therefore, it is essential to understand the machining behavior of a particular biocomposite
Machining-induced damages primarily result from cutting forces and temperatures, making it essential to select suitable cutting parameters and tools, including material, coating, and geometry, to minimize failures.
Fine dust produced during the machining of materials poses significant health risks to machine operators While existing research has primarily addressed fine dust from metal and metal matrix composites, there is a critical need to investigate the fine dust generated from the machining of polymer composites.
The rapid advancement of NFRP composites necessitates the creation of predictive models to swiftly identify factors influencing machining performance for these new materials It is crucial to focus on developing predictive models, like those based on ANFIS, that utilize limited empirical data yet maintain high predictability.
METHODOLOGY AND EXPERIMENTAL PROCEDURE
Workpiece materials and drill tool
The new biocomposites, labeled M1, M2, and M3, were developed for the dry drilling process These innovative materials consist of a common matrix made from Polypropylene (PP), Polyolefin (POE), and Maleic Anhydride grafted Polypropylene (MAPP), and are reinforced with varying weight ratios of biocarbon particles and chopped miscanthus fibers.
PP (trade name, 1350N); POE (trade name, Engage 7487); MAPP (trade name, Fusabond
In Southern Ontario, Canada, the biocarbon produced from the pyrolysis of miscanthus fibers, which measure an average length of 4 mm, was developed These miscanthus fibers, utilized in biocomposites, were provided by Competitive Green Technology.
The press molding process for producing biocomposites involves several key steps Initially, the mold platens are heated to 180°C for 30 minutes Afterward, the material components are placed in the press and preheated with closed platens for about 10 minutes to facilitate melting Following this, a vacuuming process is conducted for 3 minutes to degas the material The mold platens are then closed under a pressure of 2 tons for 10 minutes Finally, the platens are cooled to below 50°C, allowing for the biocomposite to be removed from the press.
The mechanical properties and chemical composition of the biocomposites as described in Table 2.1 Figure 2.1 shows images of biocomposites
Table 2.1 Chemical composition and mechanical properties of biocomposites
Biocomposites are produced using the press molding technique, resulting in rectangular sheets measuring 300 x 120 x 5 mm These samples are derived from a larger sheet, originally sized at 500 x 500 x 5 mm, supplied by the Bioproducts Discovery and Development Centre at the University of Guelph in Ontario, Canada.
High-speed steel twist drills with diameters between 6 and 10 mm, featuring right-hand cuts and standard spiral flutes, were chosen for the experiments Detailed drill geometry can be found in Table 2.2, and a visual representation of the twist drills is illustrated in Figure 2.2.
Figure 2.1 Biocomposites used for experiments
Table 2.2 The geometric dimensions of drills (Precision twist drill, HSS, Jobber)
Shank type Length (mm) Flute length (mm) Diameter (mm) Point angle (deg)
Design of experiment
The experimental design utilized a full factorial approach, examining input factors such as drill diameter (d), spindle speed (s), feed rate (f), and three biocomposites (M1, M2, M3) Key performance indicators for the machining process included thrust force, specific cutting energy related to thrust force, surface roughness, and fine dust emissions The drilling parameters and their respective levels are detailed in Table 2.3, while Table 2.4 outlines the parameter combinations derived from the factorial design for drilling biocomposites Each drilling setup was replicated three times, resulting in a total of 243 drilled holes across various combinations, with drill diameters of 6mm, 8mm, and 10mm.
Figure 2.2 Twist drills used for experiments
When drilling biocomposites, it is essential to select appropriate parameters based on the tool manufacturer's catalog and relevant literature These biocomposites, composed of a polypropylene matrix, exhibit low thermal conductivity and softening temperature, leading to potential surface damage due to increased cutting temperatures Research indicates that machined parts should be maintained within a temperature range of 80°C to 160°C Therefore, careful consideration of cutting parameters is crucial to prevent thermal-induced damage during the drilling process of biocomposites.
Table 2.3 Factors and their levels used for experimental design
Factors Low Medium High d: drill diameter (mm) 6 8 10 s: spindle speed (rpm) 600 1500 2400 f: feed rate (mm/rev) 0.1 0.2 0.3
Table 2.4 Matrix of experiments used for the drilling of biocomposites
Material removal rate (cm 3 /min)
Equipment used for machining and measuring
The drilling process was performed using a HURON – K2X10 3-axis CNC machine, featuring a maximum power of 50 KW, a spindle speed of 28,000 rpm, and a torque of 50 Nm An image of the CNC machine tool is shown in Figure 2.3.
Figure 2.4 illustrates the equipment utilized for measuring cutting forces, which comprises a Kistler 9255B dynamometer, a Kistler 5010 charge amplifier, a DT 9836 data translation card from Data Translation Inc., and a personal computer.
The workpiece clamping device is securely mounted on the dynamometer table, which is fixed to the machine table for direct measurement of cutting forces The cutting force signals along the X, Y, and Z axes are sent to charge amplifiers (Kistler 5010) and subsequently transmitted to a data translation card (DT 9836) before reaching a personal computer These cutting forces are recorded and analyzed using Matlab software for comprehensive evaluation.
Figure 2.4 Devices used for measuring cutting forces
Surface roughness is assessed using a Mitutoyo SJ-410 surface profilometer, which is linked to a personal computer The collected data on surface roughness is analyzed with the SURFPARK-SJ software installed on the computer.
A system consists of Aerodynamic Particle Sizer (APS, model 3321, TSI Inc., Shoreview,
MN, USA) and Scanning Mobility Particle Sizer (SMPS, model 3080, TSI Inc., Shoreview,
MN, USA) was used to measure the fine dust emitted during the drilling of biocomposites APS (Figure 2.6a) was used to measure the fine particles with a diameter ranging from 0.5 to
20 microns SMPS (Figure 2.6b) was used to measure the ultrafine particles between 7 nm and 100 nm in size
The microstructure of the pre-machined and post-machined surface of biocomposite are observed on the Scanning Electron Microscope (SEM, S-3600N, HITACHI), as shown in Figure 2.7
Figure 2.6 APS (model 3321, TSI, Inc) and SMPS (model 3080, TSI, Inc)
Figure 2.7 Scanning Eletron Microscope (S-3600N, HITACHI)
EXPERIMENTAL INVESTIGATION ON MACHINABILITY
Introduction
Natural fiber reinforced polymer (NFRP) composites are increasingly utilized in construction, automotive, and sports goods due to their benefits such as low density, high strength and stiffness, environmentally friendly manufacturing, and cost-effectiveness Manufacturers are likely to adopt NFRPs as a sustainable alternative to synthetic fiber reinforced polymer (SFRP) composites in automobile production to lessen environmental impact and decrease vehicle weight To achieve the desired dimensions and facilitate assembly, products made from composite materials require machining processes like drilling, which is a widely used method for creating holes in parts assembly.
Research on the machining of natural fiber reinforced polymers (NFRPs) is limited compared to that of synthetic fiber reinforced polymers (SFRPs), as findings from SFRPs cannot be directly applied to NFRPs due to differing mechanical properties (Nassar et al., 2017) Debnath et al (2017) examined the drilling behavior of nettle/polypropylene composites and concluded that specific drill bit geometries are only effective for certain materials, highlighting the need for tailored studies on cutting mechanisms for each composite In a study by Bajpai and Singh (2013), it was found that the thrust force generated by standard drill tools was significantly greater than that produced by trepanning tools when drilling sisal/polypropylene composites Furthermore, Maleki et al (2019) investigated the machining of woven jute fabric reinforced polymer composites and discovered that high-speed steel (HSS) drills resulted in lower delamination factors and thrust forces compared to carbide drills, while the surface roughness with the carbide CoroDrill 856 was notably higher than that produced by HSS tools.
854 They recommended using the HSS twist drill when drilling jute fiber reinforced polymer composites (Hadi Rezghi Maleki et al., 2019)
The choice of fibers in composite materials significantly affects the machinability of non-fiber reinforced polymers (NFRPs), as cutting tools interact with both the fibers and the matrix (Nassar et al., 2017) Research by Ismail et al (2016) on drilling sustainable and conventional composites revealed that cutting parameters greatly influence damage, with increased feed rates leading to higher thrust forces, roughness, and delamination Their findings indicated that carbon fiber reinforced polymers (CFRP) have lower surface roughness, while hemp fiber reinforced polycaprolactone (HFRP) shows less delamination under identical conditions Abilash and Sivapragash (2016) emphasized the impact of feed rate and drill bit diameter on delamination in bamboo fiber reinforced polyester composites Venkateshwaran and ElayaPerumal (2013) noted that higher feed rates and spindle speeds increased delamination in banana fiber reinforced epoxy composites Additionally, Chegdani and El Mansori (2018) found that coated tools raised specific cutting energy when drilling bidirectional flax fiber reinforced polypropylene Lastly, Bajpai et al (2017) observed that using twist and parabolic drills resulted in better surface quality and lower thrust forces compared to Jo drill tools in green composites like Sisal/PLA and Grewia Optiva/PLA.
Debnath et al (2014) explored the drilling characteristics of sisal reinforced epoxy and polypropylene composites, revealing that machinability is influenced by both tool geometry and matrix material They noted that parabolic drills produced lower thrust force and torque compared to step and four-facet drills, with higher torque values recorded for sisal/epoxy composites under identical cutting conditions, while no delamination occurred in either material In a separate study, Palanikumar and Valarmathi (2016) found that the thrust force during the drilling of Medium Density Fiberboard (MDF) increased with the feed rate Additionally, Prakash et al (2014) reported that roughness levels rose with larger drill bit diameters and higher feed rates when drilling MDF.
Szwajka et al (2019) noted that the tool coating type and the feed rate significantly influenced the thrust force and the roughness when machining MDF
Hybrid composite materials consist of a common matrix reinforced with multiple types of reinforcements, as defined by Fu et al (2002) Recent studies have focused on the machinability of these materials Jayabal et al (2011) found that during the drilling of glass-coir/polyester hybrid composites, the feed rate significantly influences thrust force more than drill diameter and spindle speed Similarly, Navaneethakrishnan and Athijayamani (2016) noted that in drilling vinylester reinforced with sisal fiber and coconut shell powder, the drill point angle and feed rate are crucial factors affecting thrust force, with increases in both leading to higher thrust Vinayagamoorthy (2017) further observed that reducing spindle speed or increasing feed rate raises thrust force and surface roughness in polyester composites reinforced with jute and steel fiber, with thrust force also rising with larger drill diameters and point angles.
Numerous studies have investigated the dust produced during the machining of metallic materials, revealing a significant relationship between dust emission and various cutting conditions, including workpiece material, tools, and cutting parameters Researchers have found that the small-sized dust particles emitted during machining pose serious environmental and health risks to machine operators Specifically, a substantial proportion of the dust generated during machining is less than 2.5 microns, with the quantity of emitted particles being influenced by spindle speed and feed rate Additionally, friction plays a crucial role in dust formation, and the deformation conditions in the chip formation zone are critical in determining the amount of dust generated, although cutting conditions have minimal impact on nanoparticle generation The choice of tool coating materials also affects dust production during aluminum alloy machining Furthermore, it has been shown that the initial temperature of the workpiece significantly influences fine dust emission during dry drilling, with lower dust generation observed in pre-cooled materials.
Research on dust generated during machining of composites remains limited Marani et al (2018) demonstrated that cutting parameters and the microstructure of the workpiece material significantly influence dust production during metal matrix composite milling Additionally, Songmene et al (2018) revealed that employing water Minimum Quantity Lubrication (MQL) during granite polishing notably reduces fine dust generation, although it does not affect the creation of ultrafine particles.
Research has shown that the type of coating on cutting tools significantly influences dust generation during machining processes Specifically, a study by Kremer and El Mansori (2011) found that smoother tools produce more dust compared to rougher ones when cutting metal matrix composites Additionally, Haddad et al (2014) demonstrated that dust emissions are also affected by cutting parameters and tool geometry, particularly when trimming polymer composites reinforced with carbon fibers.
This study aims to explore how different machining conditions impact key performance indicators during the dry drilling of a new hybrid biocomposite material, which consists of miscanthus fibers and biochar reinforced polypropylene The focus is on assessing specific cutting energy, thrust force, surface roughness, and the emissions of fine and ultrafine dust.
Experimental setup
The hybrid biocomposite material is composed of a polypropylene (PP) and polyolefin elastomer (POE) matrix, which is randomly reinforced with biochar and chopped miscanthus fiber, and includes a Maleic Anhydride grafted Polypropylene (MAPP) compatibilizer for enhanced compatibility The specific composition details of this hybrid biocomposite are outlined in Table 3.1.
Table 3.1 Composition of hybrid biocomposite
PP (wt%) POE (wt%) MAPP (wt%) Biochar (wt%) Miscanthus (wt%)
Pinnacle Polymers LLC in LA, USA, produces PP (trade name, 1350N), while Engage 7487 and Fusabond 613 are the trade names for POE and MAPP, respectively The biochar utilized in the hybrid composite is derived from the pyrolysis of natural miscanthus fibers, averaging 4 mm in length, developed in Southern Ontario, Canada Competitive Green Technologies provided these miscanthus fibers for manufacturing The process and characteristics of the miscanthus fibers and the resulting biochar from pyrolysis are illustrated in Figure 3.1 (Anstey et al., 2016) Additionally, the mechanical properties of the hybrid biocomposite are detailed in Table 3.2 (Dhaouadi, 2018).
Table 3.2 Mechanical properties of hybrid biocomposite (Dhaouadi, 2018)
The hybrid biocomposite material was manufactured using a French Press USA TMP Model EHV machine with a maximum tonnage of 57 tons through a press molding process Initially, the mold platens were preheated to 180 °C for 30 minutes The material was then loaded into the press and pre-heated with closed platens, without applying pressure, for approximately 10 minutes to facilitate melting Following this, vacuuming was performed for 3 minutes to degas the material, after which the platens were closed at a pressure of 2 tons for 10 minutes The final step involved cooling the platens to below a specified temperature.
50 °C and then removed from the press
In 2017, the University of Guelph's Bioproducts Discovery and Development Center developed a hybrid biocomposite material specifically for internal automobile parts Research by Dhaouadi (2018) demonstrated that this hybrid biocomposite achieved a 70% increase in the Young’s modulus of the matrix (PP/POE) when reinforced with 15wt% biochar and 15wt% miscanthus Additionally, this innovative material exhibited superior tensile and flexural strength compared to the commercial Talc/PP composite (RTP132UV).
Figure 3.1 Fabrication of biochar (Anstey et al., 2016) result, hybrid biocomposite is considered the most likely material to replace the commercial Talc/PP composite
Dry drilling of hybrid biocomposite materials was performed using a 3-axis CNC machine (HURON - K2X10), featuring a maximum power of 50 KW and a spindle speed of 28,000 rpm Standard HSS twist drill bits with diameters of 6 mm, 8 mm, and 10 mm were utilized to create holes in the workpiece, with cutting parameters chosen according to the tool manufacturer's specifications and relevant literature.
The workpiece sample, measuring 300 mm × 120 mm × 5 mm, was securely attached to an aluminum back plate support of 300 mm × 120 mm × 30 mm, which featured 80 drilled holes of 12 mm diameter This subsystem was then mounted onto a dynamometer, where it was tightened using screws Key performance metrics such as drilling force, surface roughness, and particle emission were measured and analyzed The drilling forces were recorded using a Kistler 9255B dynamometer, which was clamped to the machine table and connected to Kistler 5010 charge amplifiers These amplifiers generated output signals that were transmitted to a data translation card for further analysis.
9836, Data Translation Inc., Marlborough, MA, USA) and then connected to a personal computer
The study utilized a Scanning Mobility Particle Sizer (SMPS, model #3080, TSI Inc.) equipped with a nano Differential Mobility Analyzer to measure ultrafine particles ranging from 7 nm to 100 nm generated during drilling Additionally, an Aerodynamic Particle Sizer (APS, model 3321, TSI Inc.) was employed to assess fine particles with diameters between 0.5 and 10 μm Dust samples were collected via a pump at a flow rate of 1.5 L/min through a suction tube positioned near the machining area, connecting to the dust measurement system comprising both APS and SMPS The experimental setup is detailed in Figure 3.2.
The surface roughness was measured using a Mitutoyo SJ-410 roughness profilometer, connected to a computer with SURFPAK–SJ software for data recording and analysis Measurements of the surface roughness of drilled holes were conducted in the feed direction, with each condition tested three times to ensure accuracy.
The experiments utilized a full factorial design with three input parameters, each tested at three levels To ensure reliable and accurate results, each test was conducted three times, and the average of the measured values was analyzed A summary of the investigated factors and their corresponding levels can be found in Table 3.3.
Figure 3.2 The experimental devices for machining and measurement system
Table 3.3 The design of experiments
Factors Level 1 Level 2 Level 3 f: Feed rate (mm/rev) 0.1 0.2 0.3 s: Spindle speed (rpm) 600 1500 2400 d: Drill bit diameter (mm) 6 8 10
Results and discussion
The average thrust force was calculated based on signals when the drill's main cutting edges and chisel edge were fully engaged with the workpiece An analysis of variance (ANOVA) was conducted to assess the impact of various factors on the drilling process, as detailed in Table 3.4 The primary aim of ANOVA is to evaluate the influence of individual factors, revealing that four factors exhibited P-values below 0.05, indicating statistical significance at a 95% confidence level Among these, the feed rate (f) had the most substantial effect on thrust force, followed by spindle speed (s), drill bit diameter (d), and the interaction of spindle speed and drill bit diameter (sd) Conversely, the interactions of feed rate and spindle speed (fs) and feed rate and drill bit diameter (fd) were found to be statistically insignificant for thrust force.
Table 3.4 ANOVA for thrust force
Source Sum of Squares D f Mean Square F-Ratio P-Value f: Feed rate (mm/rev) 9121.5 1 9121.5 104.28 0.0000 s: Spindle speed (rpm) 3949.53 1 3949.53 45.15 0.0000 d: Drill bit diameter (mm) 2801.01 1 2801.01 32.02 0.0000 Interaction f.s (mm/min) 126.945 1 126.945 1.45 0.2424 Interaction f.d (mm 2 /rev) 205.427 1 205.427 2.35 0.1411 Interaction s.d (rpm*mm) 1374.52 1 1374.52 15.71 0.0008
The experimental prediction model for thrust force was developed through regression analysis, focusing on factors with statistically significant effects The thrust force is influenced by various machining parameters.
The thrust force (Ft) is influenced by the feed rate (f), spindle speed (s), and drill bit diameter (d), measured in mm/rev, rpm, and mm, respectively The ANOVA analysis revealed a correlation coefficient (R²) of 89.23% and an adjusted R² of 87.27%, confirming that the model effectively predicts thrust force.
Figure 3.3 illustrates that the feed rate significantly influences thrust force, more so than spindle speed and drill bit diameter This observation aligns with previous studies (Jayabal et al., 2011), indicating that thrust force generally increases with larger drill diameters, higher feed rates, and increased spindle speeds.
An increase in feed rate leads to a rise in thrust force, as evidenced in Figure 3.4 This correlation suggests that a higher feed rate results in a larger cross-sectional area of the uncut chip, which increases resistance to chip formation and consequently elevates thrust force values This finding aligns with previous research (Debnath et al., 2017; Hadi Rezghi Maleki et al., 2019; Bajpai et al., 2017; Debnath et al., 2014; Palanikumar & Valarmathi, 2016; Basavarajappa et al., 2012; Latha et al., 2011) Notably, the thrust force experiences a rapid increase with feed rate, particularly at the maximum spindle speed of 2400 rpm using a 10 mm drill (Figure 3.4c).
Increasing the drill bit diameter leads to a rise in thrust force due to the larger cross-sectional area of the uncut chip and the chisel edge width, necessitating higher cutting forces Additionally, a larger drill diameter enhances the contact area between the workpiece material and the drill bit, resulting in increased friction and, consequently, an increase in thrust force (Vinayagamoorthy, 2017) These findings align with results from other studies (Vinayagamoorthy, 2017; Latha et al.).
Figure 3.3 The main effects for thrust force
Figure 3.4 illustrates the relationship between feed rate, spindle speed, and drill bit diameter on thrust force It shows that thrust force varies with drill bit diameters of 6 mm to 8 mm across spindle speeds of 600 rpm, 1500 rpm, and 2400 rpm, indicating a slight increase However, an unclear trend is observed for drill bit diameters from 8 mm to 10 mm due to the interaction between spindle speed and diameter Overall, thrust force gradually rises with increasing drill bit diameter at spindle speeds of 600 rpm to 1500 rpm, but shows a significant increase at a spindle speed of 2400 rpm.
From Figures 3.3 and 3.4, it can be found that the thrust force increases with the spindle speed increase Figure 3.5 indicates that the thrust force rises slowly with the spindle speed
Figure 3.4 Thrust force related to drill bit diameter during drilling of hybrid biocomposite with different feed rates, at spindle speeds:
(in the range of 600 rpm to 1500 rpm), and then increases significantly as the spindle speed rises up to 2400 rpm This can be explained as follows:
The linear feed rate (v f) in mm/min is calculated using the formula v f = f * s, where s represents the spindle speed in revolutions per minute (rev/min) and f denotes the feed rate in millimeters per revolution (mm/rev).
An increase in spindle speed results in a higher linear feed rate (v f in mm/min), as the feed rate operates independently This relationship leads to an increase in thrust force, aligning with findings from previous studies (Palanikumar & Valarmathi, 2016; Basavarajappa et al., 2012; Latha et al., 2011) The peak thrust force was achieved at optimal input parameters of f = 0.3 mm/rev, s = 2400 rpm, and d = 10 mm.
3.3.2 Specific cutting energy for thrust force
Specific cutting energy, or specific cutting pressure, refers to the cutting force applied per unit area of the uncut chip In the drilling process using a standard twist drill bit, the specific cutting energy for the thrust force (K t) is measured in N/mm² and can be calculated using a specific equation (Sheikh-Ahmad, 2009).
Figure 3.5 Thrust force versus the spindle speed when drilling hybrid biocomposite using drill bit diameter of 10 mm with various feed rates
Where Ft, f, and d denote the thrust force (N), the feed rate (mm/rev), and the drill bit diameter (mm), respectively
Table 3.5 displays the analysis of variance (ANOVA) for specific cutting energy in relation to thrust force, revealing that all three tested factors have statistically significant effects with P-values less than 0.05 at a 95% confidence level Among these factors, the feed rate has the most substantial impact on specific cutting energy for thrust force This analysis facilitated the development of an empirical model for predicting specific cutting energy, achieving an R² value of 86.65% and an adjusted R² of 84.91%, confirming the model's predictive suitability The empirical model for specific cutting energy (K t in N/mm²) is provided as follows:
Where f (mm/rev) is the feed rate; s (rpm) is the spindle speed; and d (mm) is the drill diameter
Table 3.5 ANOVA for specific cutting energy
F-Ratio P-Value f: Feed rate (mm/rev) 27881.8 1 27881.8 137.47 0.0000 s: Spindle speed (rpm) 7015.2 1 7015.2 34.59 0.0000 d: Drill bit diameter (mm) 4361.16 1 4361.16 21.50 0.0002 Interaction f.s (mm/min) 768.16 1 768.16 3.79 0.0658 Interaction f.d (mm 2 /rev) 388.855 1 388.855 1.92 0.1814 Interaction s.d (rpm*mm) 833.333 1 833.333 4.11 0.0562
The analysis of Figures 3.6 and 3.7 indicates that specific cutting energy for thrust force decreases with an increased feed rate, while it rises with higher spindle speeds, aligning with findings from Faissal Chegdani & El Mansori (2018) Additionally, a larger drill diameter is associated with a reduction in specific cutting energy for thrust force, as demonstrated in Figure 3.6.
Figure 3.6 Main effects plot for specific cutting energy related to thrust force
Figure 3.7 Specific cutting energy for thrust force as a function of spindle speed with different feed rates and drill bit diameter of 10 mm
Surface roughness is a key indicator of machined surface quality In this study, surface roughness parameters were assessed in the longitudinal direction of drilled holes, with measurements taken three times per hole and a cut-off length of 0.8 mm The mean arithmetic average roughness (R a ) and maximum profile height (R t ) were analyzed to evaluate the impact of cutting conditions on surface quality Results, illustrated in Figure 3.8, show that the optimal surface finish was achieved at a cutting condition of f = 0.2 mm/rev, s = 600 rpm, d = 6 mm, while the highest roughness occurred at f = 0.3 mm/rev, s = 2400 rpm, d = 10 mm Figure 3.9 depicts the surface morphology before and after machining, confirming the absence of uncut fibers and delamination on the machined surface.
Figure 3.8 Surface roughness profile for drilled holes with different cutting conditions: (a) f = 0.2 mm/rev, d = 6 mm, s = 600 rpm; and (b) f = 0.3 mm/rev, d = 10 mm, s = 2400 rpm
Table 3.6 reveals that the factors of feed rate, spindle speed, and drill diameter all have P-values below 0.05, confirming their statistical significance at a 95% confidence level Among these, drill bit diameter exerts the most substantial effect on surface roughness However, the interactions between these factors—feed rate, spindle speed, and drill diameter—do not show statistical significance The correlation coefficient, R², is 85.46%, indicating that the models account for 85.46% of the variability in surface roughness, while the adjusted R² of 83.57% is more appropriate for comparing models with varying numbers of independent variables.
(a) SEM image of material before machining
(b) SEM image of machined hole (f = 0.3 mm/rev, s = 2400 rpm, d = 10 mm)
Figure 3.9 presents SEM images of a hybrid biocomposite, highlighting (a) the surface before machining and (b) the machined surface under specific conditions of f = 0.3 mm/rev, s = 2400 rpm, and d = 10 mm The findings indicate that the model effectively predicts the outcomes, with the empirical model derived from the analysis.
Where f (mm/rev) is the feed rate; s (rpm) is the spindle speed; and d (mm) is the drill diameter
Table 3.6 ANOVA for surface roughness R a
D f Mean Square F-Ratio P-Value f: Feed rate (mm/rev) 0.0418569 1 0.0418569 4.50 0.0465 s: Spindle speed (rpm) 0.388962 1 0.388962 41.86 0.0000 d: Drill bit diameter (mm) 0.710829 1 0.710829 76.50 0.0000 Interaction f.s (mm/min) 0.00213333 1 0.00213333 0.23 0.6370 Interaction f.d (mm 2 /rev) 0.006075 1 0.006075 0.65 0.4283 Interaction s.d (rpm*mm) 0.000133333 1 0.000133333 0.01 0.9058
Conclusions
This study examines how cutting parameters and drill bit diameter influence the machinability of a novel hybrid biocomposite during dry drilling Utilizing experimental data and statistical analysis, key conclusions have been derived regarding these effects.
Machining conditions play a crucial role in determining thrust force, with the feed rate having a more substantial impact than spindle speed and drill bit diameter Increasing cutting parameters and drill bit diameter leads to a rise in thrust force.
The study examined the specific cutting energy related to thrust force as a material property, revealing that this energy rises with higher spindle speeds while decreasing with increased feed rates and larger drill bit diameters.
The diameter of the drill bit significantly influences surface roughness, more so than the cutting parameters A reduction in the drill's diameter, spindle speed, and feed rate leads to a decrease in surface roughness.
During drilling of this new composite material, both fine particles (PM 10 ) and ultrafine particles (diameters ranging from 7–100 nm) were generated The total number concentration
Figure 3.25 Relationship between maximum number concentration of ultrafine particles related to particle size and cutting parameters (drill: 6 mm) of fine particles reached 1300 #/cm 3 , while the ultrafine particle generation reached 9000
The generation of fine dust during drilling is influenced by machining conditions, particularly the drill bit diameter and feed rate, while spindle speed has minimal impact As the feed rate, spindle speed, and drill bit diameter increase, the total number concentration of fine particles decreases, with many particles having aerodynamic diameters under 2.5 micrometers However, cutting parameters and drill bit diameter do not significantly affect ultrafine particle generation in hybrid composites at the 95% confidence level, making predictions challenging.