International Journal of Machining and Machinability of Materials (8 papers in press)

Regular Issues

• A smart swarm-based optimisation framework for computing the optimal surface finish value for machining Ti-alloy  
  by K. Srinivasulu, G. Krishna Mohana Rao 
  Abstract: High corrosion and wear resistance makes Ti-alloys the best choice for manufacturing aerospace, automobile and power plant machinery parts. Surface roughness (SR) in Ti-alloy can arise from machining processes. This paper focuses on computing the optimal value of cutting speed, feed rate, depth of cut for minimising SR of Ti-alloy under different machining conditions by uncoated, chemical vapour deposition (CVD) and physical vapour deposition (PVD) based carbide tools. For this purpose, artificial neural network (ANN) is employed to model the SR. With the help of optimal ANN parameters, the objective functions are developed which are then optimised by particle swarm optimisation (PSO) method. The result obtained from the PSO method is validated by performing a confirmatory experiment. On comparison the absolute percentage error varied in the range of 1%6% for dry, synthetic and mineral oil machining by uncoated, PVD and CVD coated carbide tools.
  Keywords: Ti-alloys; surface roughness; artificial neural network; ANN; objective functions; particle swarm optimisation; PSO.
  DOI: 10.1504/IJMMM.2025.10071383
   
  
• Machinability analysis of carbon nanotubes reinforced PMMA composites: a comparative study  
  by Prabhat Kumar Nanda, Narasingh Deep, Punyapriya Mishra, Trupti Ranjan Mahapatra, Punyatoya Mishra 
  Abstract: This study investigates the machinability criteria of carbon nanotube (CNT)-reinforced polymethyl methacrylate (PMMA) composites using ultrasonic machining (USM) and abrasive jet machining (AJM) processes. The composite materials were fabricated with varying weight percentages of CNTs, and their density, hardness, and impact strengths were acquired. The hardness improved progressively, whereas the impact strength diminished with the wt.% of CNT reinforcement. Subsequently, machining operations were carried out considering the material removal rate (MRR) and the surface roughness as the response indicators. The micrographs of the machined surfaces were also studied. The comparative analysis revealed significant differences in the machining behaviour of the CNT reinforced PMMA composites under USM and AJM. AJM exhibited higher MRRs, whereas lower surface roughness is achieved in USM.
  Keywords: carbon nanotube; CNT; PMMA composites; machinability; ultrasonic machining; USM; abrasive jet machining; AJM.
  DOI: 10.1504/IJMMM.2025.10071555
   
  
• A review of the progress in smart machining to incorporate artificial intelligence, internet of things, and data-driven approaches for precision manufacturing  
  by Milon Selvam Dennison, Kirubanidhi Jebabalan Sundarrajan, Faith Natukunda 
  Abstract: This review article investigates the significant progress in smart machining achieved through the incorporation of artificial intelligence (AI), the internet of things (IoT), and data-driven methods. It demonstrates the transformative impact of these technologies on precision manufacturing through improvements in productivity, accuracy, and adaptability. AI has facilitated advanced predictive maintenance, enhanced optimisation of machining settings, and improved the detection of defects. Concurrently, IoT has enabled real-time monitoring and the generation of actionable perceptions. The data-driven approaches have significantly enhanced the optimisation of processes and the ability to predict future outcomes through analytics. However, with these developments, it is imperative to tackle issues such as the incorporation of technology, efficient handling of data, and safeguarding against cybersecurity threats. This review highlights the potential of new technologies such as sophisticated machine learning (ML), edge computing, and next-generation IoT devices in promoting future advancements. Through promoting interdisciplinary and industry-academia collaborations, the manufacturing industry may utilise these innovations to attain more intelligent, efficient, and sustainable machining processes, ultimately improving competitiveness and contributing to broader sustainability objectives.
  Keywords: smart machining; AI in manufacturing; internet of things; IoT; data-driven manufacturing; precision machining; Industry 4.0; cyber physical systems.
  DOI: 10.1504/IJMMM.2025.10071909
   
  
• Comparative study of turning two engineering plastics (POM-C and PA-6) and optimisation using GA, SA, GRA and COPRAS with and without weighting (entropy, critic, Swara, ROC)  
  by Mounia Kaddeche, Septi Boucherit, Salim Belhadi, Mohamed Athmane Yallese 
  Abstract: This paper focuses on a comparative study of the machinability of two semi-crystalline polymers (POM-C) and (PA-6), during dry turning operations. The aim is to experimentally examine the impact of cutting parameters, namely cutting speed, feed, and depth of cut on surface roughness, cutting force, cutting power, and material removal rate. A series of experiments according to a Taguchi L18 plan was implemented. The ANOVA analysis revealed that the type of material a substantial effect on surface roughness, followed by feed, whereas cutting force and cutting power are more affected by depth of cut. Linear regression models with interactions proved effective in predicting the studied responses, and single-objective optimisation using SA and GA methods were applied to optimise each response. The GRA and COPRAS methods coupled with weighting methods CRITIC, ROC, SWARA, and Entropy are used for multi-objective optimisation of the considered responses. The results showed that the combination of the COPRAS method with the SWARA method provides a better compromise, which is of crucial interest for researchers in the field of optimisation in polymer material machining.
  Keywords: engineering plastics; turning; optimisation; ANOVA; revolutionary algorithm; MCDM methods.
  DOI: 10.1504/IJMMM.2025.10073163
   
  
• A scientometric review on thin-wall machining of titanium Ti6Al4V alloys  
  by Viswajith S. Nair, Rameshkumar Krishnasamy, Saravanamurugan Sundaram, Ponnambalam S. Govindarajan 
  Abstract: The challenges in machining thin-walled components, along with the poor machinability of titanium alloys, have attracted significant research interest in the topic of titanium alloy thin-wall machining. Given the rapidly expanding body of literature on the field, apart from narrative reviews, there is a necessity for structured analyses that systematically and quantitatively map the research landscape. This study uses data-driven scientometric methods to analyse literature from the last few decades to establish dominant research areas and identify key contributors and potential future directions. The analysis is based on a database of 6,808 publications on Ti6Al4V alloy machining, 1,219 publications on thin-wall machining, and 183 publications specifically addressing thin-wall machining of Ti6Al4V. It systematically traces the changing research priorities using bibliometric analysis techniques alongside co-citation networks and keyword clustering, with particular emphasis on machining strategies for surface quality, process stability, and deformation control. The quantitative mapping of the research interests within the topic highlights the growing adoption of sustainable manufacturing practices, modelling approaches, intelligent monitoring systems, and digital twins. This review offers an evidence-based perspective of the research landscape in machining thin-walled titanium alloy components and highlights the paths for future research.
  Keywords: thin-wall machining; machinability; titanium; Ti6Al4V; digital twin; scientometrics; bibliometrics; Scopus; review; co-citation network.
  DOI: 10.1504/IJMMM.2025.10074138
   
  
• Investigation on implementing CAD/CAM/CNC systems in machining operations with a focus on tool wear and the type of chips produced  
  by Gustavo Guilherme Dos Santos Costa, Luiz Leroy Thomé Vaughan, Feliciano Cangue, José Aécio Gomes De Sousa 
  Abstract: The manufacture of parts by machining has evolved with a focus on productivity and quality improvements. In this context, this study investigates tool wear and chip formation in cavity milling of VP50 steel using carbide ball-end inserts. Two interpolation types (linear and circular) and two tolerances (0.05 mm and 0.10 mm) were analysed. The results showed that flank wear was predominant, with circular interpolation at 0.05 mm tolerance providing better performance in terms of accuracy and stability. Tool life reached approximately 27 minutes, and chips were mainly arc-shaped and loose, with finer chips obtained under linear interpolation with 0.10 mm tolerance.
  Keywords: CAD/CAM systems; machining; tool wear; chip type.
  DOI: 10.1504/IJMMM.2025.10074911
   
  
• Quality optimisation by metaphor less algorithms: a case study of abrasive waterjet industry  
  by Paramjit Thakur, Mugdha Dongre, Ajinkya Naik, D.N. Raut 
  Abstract: Abrasive waterjet cutting is a non-traditional machining process which can cut almost every material. The major problem faced by this industry is the conflicting nature of their quality responses. In order to solve this problem, overall quality optimisation is done in this work. The quality responses considered in this work are depth of cut, depth of smooth zone, grit embedment, kerf width and roughness. Firstly, these responses are converted into a single value known as multi response performance index (MRPI) by TOPSIS. The nonlinear regression equations are framed for all the responses and MRPI and fed as input to five metaphor less algorithms (TLBO, Jaya, Rao1, Rao2 and Rao3). The optimal parameters obtained from all the five algorithms are compared and TLBO was found to be the best. Further, analysis of variance was applied to find the effect of most influential parameters given variation in responses and MRPI.
  Keywords: metaphor less algorithms; optimisation; abrasive waterjet cutting; Ti6Al4V alloy; multi-response performance index; MRPI.
  DOI: 10.1504/IJMMM.2025.10075011
   
  
• Optimisation of process parameters of electro discharge drilling for micro-hole drilling on Super Ni 276 alloy  
  by Saidulu Nakarkanti, Pappula Laxminarayana, Ashok Kumar Uppari, Karrolla Buschaiah 
  Abstract: Super Ni 276, a nickel-chromium-molybdenum alloy, is widely used in aerospace and turbine industries due to its exceptional corrosion resistance and high-temperature stability. Drilling precise micro-holes in this alloy presents significant challenges. In this study, electro discharge drilling (EDD) was employed to drill micro-holes (0.5 mm, 0.6 mm, 0.7 mm, and 0.8 mm) using copper and brass tubular tools, with process parameters such as pulse on time, pulse off time, peak current, and tool diameter varied, while voltage remained constant. An L16 orthogonal array was used for the experimental design. Key performance metrics, including tool wear rate (TWR), material removal rate (MRR), hole ovality, taper, and the heat-affected zone (HAZ), were measured to identify optimal process parameters. The results indicate that brass tools generally provide better precision with lower diameter deviations and smaller taper angles (0.0013 to 0.0113), compared to copper (0.0008 to 0.0213). However, brass tools also produce a larger HAZ due to their higher thermal conductivity, potentially increasing thermal damage to the workpiece. Copper tools, with their smaller HAZ, are more suitable for applications where thermal management is critical, while brass tools offer superior accuracy and are preferable for high-precision machining with minimal taper.
  Keywords: electro discharge drilling; EDD; heat affected zone; HAZ; material removal rate; MRR; Super Ni 276; tool wear rate; TWR.
  DOI: 10.1504/IJMMM.2025.10075135