International Journal of Machining and Machinability of Materials (10 papers in press)

Regular Issues

• Prediction of heat removal rate and surface finish in MQL with coconut oil-based NCF for Inconel machining  
  by Subhash Khetre, Arunkumar Bongale, Satish Kumar 
  Abstract: The environment-friendly minimum quantity lubrication (MQL) technology is used to extend tool life at a low cost. The flow rate in MQL is lower and produces good results when compared to existing cooling techniques. The novel CBN TNMG TNMG cutting tool insert for MQL with coconut oil-based SiC-MWCNT nano cutting fluid is proposed in which cubic boron nitride (CBN) insert is used for machining of Inconel alloy 718 that has high resistance to wear, friction, and compressive stress thereby avoid the BUE formation in the workpiece and silicon carbide (SiC) and multi-walled carbon nano tubes (MWCNT) are added to coconut oil with an appropriate volume fraction for better heat removal rate from the workpiece, and tool during turning. The thermal properties of the proposed nano cutting fluid are compared with those of some existing nano MQL cutting fluids and it was found that the proposed MQL cutting fluid has a high heat removal rate and reduces friction and it improves the surface finish of the workpiece. The result obtained by the proposed model efficiently solved the existing problems with high thermal conductivity, less temperature gradient, and low surface roughness in the workpiece.
  Keywords: minimum quantity lubrication; MQL; CBN TNMG insert; Inconel 718 alloy; MATLAB Simulink; nano cutting; fluid; silicon carbide; SiC; multi-walled carbon nano tubes; MWCNT; nanoparticle; heat removal rate.
  DOI: 10.1504/IJMMM.2026.10076494
   
  
• Optimisation of wire EDM parameters for Ti-Ni SMA using multi-objective approach  
  by Rakesh Ramchandra Kolhapure, Duradundi Sawant Badkar 
  Abstract: This study investigates the optimisation of WEDM parameters of Ti-Ni SMAs for improved machining efficiency, surface integrity, and biocompatibility for biomedical implant use. Tool wear and unsatisfactory surface finish occur during conventional machining due to Ti-Ni SMA; hence, WEDM with Mo wire and deionised water was employed. A multi-objective optimisation strategy involving Taguchis L18 orthogonal array, ANOVA, and GRA was employed for the optimisation of four significant responses: MRR, SR, DD, and KW. The optimised parameter setting of 100 V, 80 us Ton, 10 us Toff, 4 A I, and 30 Hz WF improved the grey relational grade by 6.26%. SEM and EDS analysis confirmed that superior minimisation of microcracks, blowholes, and recast layer thickness (from 13.793-34.497 um to 5.306-10.959 um) took place with the retention of elemental biocompatibility. Supervised machine learning was leveraged for response prediction, with random forest (R2 = 0.59) being superior to XGBoost (R2 = 0.40). The optimised setting of 1.82% improved MRR and reduced SR by 5.01%, DD by 0.46%, and KW by 0.43%. A Ti-Ni SMA recon plate was successfully fabricated, with improved dimensional accuracy and mechanical reliability, confirming the process for biomedical implant application.
  Keywords: WEDM; shape memory alloy; SMA; Taguchi; GRA; ANOVA; SEM; EDS; supervised machine learning; SML.
  DOI: 10.1504/IJMMM.2026.10076729
   
  
• Investigation of nanoparticle-enhanced vegetable oils with minimum quantity lubrication on sustainable machining of titanium alloys  
  by Samir D. Jariwala, Shashank Thanki 
  Abstract: Titanium alloys require eco-friendly machining alternatives to hazardous conventional lubricants. This study investigates titanium machining using nanoparticle-enriched vegetable oils under minimum quantity lubrication. Cutting forces, surface roughness, and temperature are analysed with varying machining and lubrication parameters. Enhanced thermal conductivity and protective nanoparticle films promote efficient heat dissipation, reduced cutting temperature, smoother tool-workpiece interaction, and superior surface finish. Experimental results reveal cutting force initially rises with velocity due to work hardening, then decreases from thermal softening. Optimal lubrication pressure was 3 kg/cm2, while nozzle angle effects were nonlinear, peaking at 30 and minimising at 45. Nanofluid-assisted MQL reduced cutting temperature by 15%-20% and significantly improved surface finish, with palm oil + MWCNT achieving up to 52% roughness reduction. Depth of cut dominated machining response, followed by feed rate and pressure. These findings highlight that Sustainable machining can be achieved using nanoparticle-based MQL oils.
  Keywords: titanium alloys; minimum quantity lubrication; MQL; vegetable oils; nano particles.
  DOI: 10.1504/IJMMM.2026.10076730
   
  
• Experimental investigation and optimisation of machining parameters in CNC end milling of 17-4PH stainless steel  
  by Aamir Khan, Nilesh P. Ghongade, Mahesh S. Kavre 
  Abstract: This study examines the effects of cutting speed, feed rate, and depth of cut on material removal rate (MRR) and surface roughness during CNC end milling of 17-4PH (AISI 630) stainless steel using TiAlN-coated solid carbide end mills. Experiments were designed using the Taguchi L9 orthogonal array, and analysis of variance (ANOVA) identified depth of cut as the dominant factor for MRR, while cutting speed most significantly influenced surface roughness. Linear regression models were developed to predict performance, achieving prediction errors within 10% in confirmation tests. The results demonstrate that proper selection of machining parameters can simultaneously enhance productivity and surface quality. This work also addresses a notable research gap, as CNC end milling of 17-4PH stainless steel with TiAlN-coated carbide tools has received limited attention. The findings provide practical guidelines for optimising machining of precipitation-hardening stainless steels in high-precision manufacturing.
  Keywords: surface roughness; CNC end-milling; optimisation; 17-4PH stainless steel; Taguchi method; analysis of variance; ANOVA.
  DOI: 10.1504/IJMMM.2026.10077548
   
  
• Parametric analysis and multi-response optimisation of drilling of CFRP laminates  
  by K. Shunmugesh, Brijesh Paul, Partha Protim Das, Shankar Chakraborty 
  Abstract: Due to superior mechanical performance of carbon fibre-reinforced polymers (CFRPs), like higher strength-to-weight ratio, fatigue strength, temperature and corrosion resistance; they are replacing metals in many of the manufacturing industries. However, because of their poor machinability, rigorous process optimisation is required for efficient mass production and waste minimisation. This paper proposes optimisation of drilling of CFRP laminates using multi-criteria decision making (MCDM) techniques. While drilling quasi-isotropic CFRP laminates using tungsten carbide twist drills, it studies the effects of spindle speed, feed rate and drill diameter on material removal rate, and delamination factor, circularity and cylindricity of the drilled holes. The said drilling process is optimised employing four MCDM methods, i.e., weighted aggregated sum product assessment (WASPAS), combined compromise solution (CoCoSo), mixed aggregation by comprehensive normalisation technique (MACONT) and alternative ranking order method accounting for two-step normalisation (AROMAN). Criteria importance through intercriteria correlation (CRITIC) is employed for measuring the importance of the responses. It is noticed that almost all the MCDM techniques suggest higher spindle speed and drill diameter, and lower feed rate for having the desired response values. These methods appear to be quite robust against changing values of the response weights, providing almost accurate ranking results.
  Keywords: carbon fibre-reinforced polymer; CFRP; drilling; optimisation; multi-criteria decision making; hybrid.
  DOI: 10.1504/IJMMM.2026.10077690
   
  
• EN-1563-GJS-400-15 estimation of surface roughness and wear values in milling of cast iron by using Taguchi and neural network algorithms  
  by G. Samtaş 
  Abstract: EN-1563-GJS-400-15 is the most widely used nodular cast iron alloy with a predominantly ferritic structure. This material has excellent machinability, good impact resistance, high electrical conductivity, good elongation properties, and magnetic permeability. This extremely tough material makes it suitable for equipment subject to large forces. In this study, the surface milling process was applied to EN-1563-GJS-400-15 cast iron using two cutting inserts (cryogenically treated and untreated, uncoated), three cutting speeds (280, 360, and 480 m/min), and three feed rates (0.15, 0.35, and 0.55 mm/rev). After the experiments, surface roughness and cutting insert wear amounts were measured. Prediction values of experimental results were found using Taguchi prediction equations and neural network algorithms. In the Taguchi-predicted values, the average prediction error for surface roughness was 10.69%, and for wear, 56.19%. In the estimated values obtained using neural network algorithms, the average error for surface roughness was 1.77%, and the average error for wear was 60.85%. In addition, optimum values for minimum surface roughness and minimum wear were obtained with Taguchi analysis within the scope of the study. In the variance analysis of the percentage of effect of cutting parameters, the most effective parameter affecting surface roughness was the feed rate (74.99%), and the most effective parameter affecting wear was the cutting edge (54.90%).
  Keywords: Taguchi; neural network; surface roughness; wear; GGG40; EN-1563-GJS-400-15.
  DOI: 10.1504/IJMMM.2026.10078202
   
  
• Machinability of UHMWPE: influence of tool geometry and cutting parameters with a novel chip classification method  
  by Erick Martins De Oliveira, Claudimir Jose Rebeyka, Harrison Lourenço Corrêa, Dalberto Dias Da Costa 
  Abstract: Ultra-high molecular weight polyethylene (UHMWPE) is a high-performance engineering polymer widely used across various industries, particularly in biomedical applications such as implant manufacturing. Due to challenges associated with injection moulding, machining is often the preferred method for achieving the dimensional precision required for UHMWPE components. Despite its growing use, studies on the machinability of UHMWPE remain scarce in both industrial and academic literature. This study investigates the influence of tool geometry and cutting parameters on the machinability of UHMWPE through two experimental setups. Key machinability indicators surface roughness, cutting force components, and chip formation were evaluated. A novel chip classification method was proposed, based on detecting force fluctuations caused by chip entanglement with the workpiece and/or lathe chuck. The results validate the effectiveness of the proposed chip classification approach and suggest that higher feed can significantly reduce chip entanglement, a prevalent issue in UHMWPE turning operations.
  Keywords: ultra-high molecular weight polyethylene; UHMWPE; turning operations; machinability; polymers.
  DOI: 10.1504/IJMMM.2026.10078696
   
  
• Parametric optimisation and cutting tool performance analysis in turning of AISI 301 stainless steel  
  by M. Venkata Ramana, G. Krishna Mohana Rao, D. Mansingh 
  Abstract: AISI 301 stainless steel is used for applications demanding good ductility, strength, and corrosion resistance, excelling in automotive, aircraft parts, etc. The plan of this work is to examine the optimum of process parameters and effect of parameters in turning of AISI 301 stainless steel through experimental work on cutting force and surface roughness. Trials are conducted by employing PVD coated and uncoated tools under minimum quantity lubrication (MQLn), dry, and nano MQLn environments using analysis of mean and analysis of variance (ANOVA). Investigational results indicated that the PVD coated tool reduced the cutting force in MQLn with nano cutting fluid condition. The uncoated tool minimised the surface roughness under MQLn conditions. Based on the ANOVA results, the depth of cut is the important process parameter influencing more on cutting force. The feed is highly substantial process parameter contributing to surface roughness under dry and MQLn machining environments.
  Keywords: Taguchi’s optimisation; uncoated tools; PVD coated tools; AISI 301 stainless steel; dry machining; minimum quantity lubrication machining; nano MQLn machining.
  DOI: 10.1504/IJMMM.2026.10079283
   
  
• Statistical texture analysis of hydrophobic microstructures fabricated on CFRP using reverse -EDM  
  by Suresh Pratap, Pradyut Anand, Somak Datta 
  Abstract: This study fabricates and statistically evaluates hydrophobic micro-dimple, groove, and cross-hatched textures on T300 CFRP using reverse -EDM. Surface characterisation via microscopy, profilometry, and contact angle measurements was paired with ISO 25178 areal parameters and statistical descriptors (skewness, kurtosis). Process fidelity was high, with errors < 3% and variation < 10%. While the mean dimensions matched the designs, skewness and kurtosis revealed distributional asymmetries. Regression showed that contact angles (132 -152) were strongly correlated with statistical descriptors, such as skewness (R2 = 087), outperforming simple geometric descriptors. Statistical texture analysis is thus a robust tool for predicting hydrophobicity and ensuring reproducibility in scalable aerospace CFRP fabrication.
  Keywords: reverse µ-EDM; hydrophobic microtextures; statistical surface analysis; CFRP machining; micromachining.
  DOI: 10.1504/IJMMM.2026.10079964
   
  
• Optimisation of EDM parameters for 6061-T6 aluminium alloy using AHP-TOPSIS framework and ANOVA validation  
  by Alaa M. Ubaid, Shukry H. Aghdeab, Shakir M. Mahmood 
  Abstract: This study aims to determine the optimal EDM conditions for machining 6061-T6 aluminium alloy using a copper electrode and to identify the statistical significance of the principal machining parameters. A structured optimisation framework integrating design of experiments (DoE), the analytic hierarchy process (AHP), the technique for order preference by similarity to ideal solution (TOPSIS), and analysis of variance (ANOVA) was applied. Three input parameters, current, pulse-on time, and pulse-off time, were examined against three performance measures: material removal rate (MRR), electrode wear rate (EWR), and microhardness (MH). Based on an L27 orthogonal array and multi-criteria ranking, the optimal parameter combination was 20 A current, 150 s pulse-on time, and 50 s pulse-off time, yielding 5.5125 g/min MRR, 1.24% EWR, and 107.76 HV microhardness. ANOVA results confirmed current as the most influential factor, pulse-on time as a secondary factor with borderline significance, and pulse-off time as statistically negligible.
  Keywords: electrical discharge machining; EDM; optimisation; analytic hierarchy process; AHP; technique for order preference by similarity to ideal solution; TOPSIS; analysis of variance; ANOVA; 6061-T6 aluminium alloy.
  DOI: 10.1504/IJMMM.2026.10080206