Forthcoming and Online First Articles

International Journal of Machining and Machinability of Materials

International Journal of Machining and Machinability of Materials (IJMMM)

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

Regular Issues

  • Experimental Investigations during Fabrication and Electrical Discharge Machining of Hybrid Al/ (SiC+ZrO2+NiTi) MMC   Order a copy of this article
    by SAURABH KUMAR MAURYA, Arindam Ghosal, Alakesh Manna 
    Abstract: Paper presents the fabrication of hybrid Al/(SiC+ZrO2+NiTi)-MMC. Metal matrix composites are hard and difficult to machine by conventional process. Therefore, electrical discharge machining (EDM) process is selected for machining. Set of experiments had been carried out based on the one factor-at-a-time approach on EDM. The effect of EDM parameters, e.g., supply voltage, peak current, pulse-on-time and duty cycle on material removal rate (MRR), tool wear rate (TWR) and surface roughness heights (Ra and Rt) were analysed. Results reveal that the MRR, recast layer, Ra and Rt increase with increase in supply voltage, peak current and pulse-on-time. The duty cycle range from 50%
    Keywords: hybrid-MMC; stir casting; EDM; material removal rate; MRR; tool wear rate; TWR; machined surface.
    DOI: 10.1504/IJMMM.2022.10045604
  • Tool Wear Condition Monitoring Method Based on graph neural network with a single sensor   Order a copy of this article
    by Chen Gao, Jie Zhou, Yuan Yang, Yukun Fang, Gaofeng Zhi, Bintao Sun 
    Abstract: Tool wear condition monitoring (TCM) is an important part of machining automation. In recent years, deep learning (DL)-based TCM methods have been widely researched. However, almost DL-based methods need sufficient learning samples to obtain good accuracy, which is hard for TCM in terms of cost and time. In order to enhance the recognition accuracy of DL-based TCM under small samples, this paper proposed a new improved multi-scale edge-labelling graph neural network (MEGNN). Firstly, the signal of cutting force sensor is expanded to multi-dimensional data through phase space reconstruction. Secondly, these multi-dimensional data are encoded into a recurrence plot (RP). Then, the RP is input to multi-scale EGNN (MEGNN) to extract features. Finally, the tool condition is estimated through the updated edge labels using a weighted voting method. Our milling TCM experiments demonstrate the proposed MEGNN-based TCM method outperforms three DL-based methods under small samples.
    Keywords: tool wear condition monitoring; TCM; small samples; recurrence plot; MEGNN; phase space reconstruction; PSR.
    DOI: 10.1504/IJMMM.2022.10046951
  • Machinability of copper-based metallic glass in micro EDM and its optimization by grey fuzzy logic   Order a copy of this article
    by Kabin Kumar Bora, Siddhartha Kar, Promod Kumar Patowari, Jatin Bhatt 
    Abstract: The present study investigates the machinability of metallic glass by drilling micro holes using the micro-electrical discharge machining process. The variation of microhole features such as material removal rate (MRR), overcut (OC), circularity error (CE), recast layer thickness (RLT) and edge deviation (ED) with respect to variable process parameters such as voltage, capacitance and tool rotation speed has been investigated by Taguchi method and analysis of variance. Multi-objective optimisation approaches such as grey relational analysis (GRA) and grey-based fuzzy logic (GFL) are implemented to optimise the process parameters to attain microholes having quality features. The studies exhibit that microholes of higher MRR and lower OC, CE, RLT, and ED can be machined by controlling the process parameters. GFL exhibits a lower error of 7.16% as compared to GRA’s 11.40% in between the experimental and predicted results.
    Keywords: micromachining; metallic glass; micro EDM; Microhole; drilling; grey fuzzy logic.
    DOI: 10.1504/IJMMM.2022.10046952
  • Boron and Graphene Nanoparticles as Solid Lubricant in Micro Milling of Nickel Titanium Shape Memory Alloys   Order a copy of this article
    by Zailani Zainal Abidin, Paul T. Mativenga 
    Abstract: Nickel titanium shape memory alloys (NiTi SMAs) are employed in a number of applications, however they are difficult to machine due to their high ductility, temperature sensitivity, and severe work hardening. Rapid tool wear and poor workpiece quality are inherent with their machining. Thus, new innovations are crucial to enhance their machinability. The usage of graphene and hexagonal boron nitride solid lubricant nanoparticles to enhance minimum quantity lubricant in micro-milling was investigated in this study. Evaluated parameters were ratio of undeformed chip thickness to cutting edge radius, composition of nanoparticles and cutting environment. Analysis of variance was employed to investigate the influence of process parameters and their interactions on flank wear, burr formation, surface roughness and cutting force. Graphene was found to be more effective than boron nitride in terms of reducing flank wear, burr size and cutting forces. Hexagonal boron nitride yielded better surface finish owing to smaller amount and size of nanoparticles. The work clearly shows the important of type and size of nanoparticles in improving machining performance. Additionally, the impact of simultaneously using chilled air and graphene nanoparticles in further improving process performance is reported.
    Keywords: shape memory alloys; micro milling; solid lubricants; minimum quantity lubricant; chilled air; Taguchi method; minimum quantity lubrication; MQL.
    DOI: 10.1504/IJMMM.2022.10048803
  • Machinability assessment of NiTinol shape memory alloy in electrochemical machining   Order a copy of this article
    by Barsharani Dash, Soumya Ranjan Parimanik, Trupti Ranjan Mahapatra, Debadutta Mishra 
    Abstract: NiTi shape memory alloys (SMAs) attract researchers because of their myriad applications in areas such as aerospace, medicine, and robotics. The current study focuses on the machining (drilling) of high-temperature Ni56Ti44 SMA by electro chemical machining (ECM) process. Machining quality features like rate of material removal (RMR), overcut, taper angle and circularity error are investigated by considering the different electrodes (copper, brass and tungsten) and the current (I), voltage (V) and inter electrode gap (IEG) as controllable parameters. Parametric analysis of machining characteristics is performed by conducting experiments following the response surface methodology (RSM) approach-based Box-Behnken design. A multi-objective optimisation for the desired output responses is also accomplished using the utility method. It was found that better RMR is achieved via the desirability analysis using RSM whereas the Utility Method led to improved result for the all-other performance measures. The study of the scanning electron microscope (SEM) micrographs revealed that among the three electrodes under consideration, the tungsten electrode is the most suitable for the machining of the present NiTi SMA.
    Keywords: NiTi SMA; electro chemical machining; ECM; Box-Behnken design of experiment; DOE; response surface methodology; RSM; utility method.
    DOI: 10.1504/IJMMM.2022.10048816
  • Machinability Study of Austempered Ductile Iron (ADI) Using Die-sinking EDM   Order a copy of this article
    by Pranjal Sarma, Dibya Jyoti Borah, Promod Kumar Patowari, Ajay Likhite 
    Abstract: Austempered ductile iron (ADI) is a comparatively new class of material having interesting properties. But it is difficult to machine by conventional methods. Here we report the EDM processing response of ADI under various parametric conditions. The effect of peak current (IP), pulse on time (Ton) and dielectric flushing pressure (Pf) on response parameters: material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR) are investigated. Study reveals that EDM can be used to effectively machine ADI under the investigated range. Results show that, increase in Ip and Ton increases MRR, TWR as well as SR. The MRR and TWR is found to first increase with the increase in Pf and at higher values of Pf both the MRR and TWR found to decrease. Surface micrographs of machined surface using scanning electron microscope (SEM) also reveal some initiation of micro cracks on the machined surface from the graphite-matrix interface.
    Keywords: electrical discharge machining; EDM; austempered ductile iron; ADI; austempering; ductile iron; machinability; machining; SEM.
    DOI: 10.1504/IJMMM.2022.10048817
  • The effect of wet blasting as a pre-deposition treatment for thick TiAlN PVD coating on machining performance   Order a copy of this article
    by Majid Abdoos, Alireza Hemmati, Heiko Graf, Sushant Rawal, Abul Fazal Arif, Stephen Veldhuis 
    Abstract: Edge preparation techniques are known to be an effective method to adjust the microgeometry and improve the mechanical properties of cutting tools. These techniques can also be used on tools prior to coating deposition to further improve the coating quality on the cutting edge and, consequently, tool life. In this research, wet blasting was used on indexable turning inserts to adjust the cutting-edge radius in order to address quality issues associated with thick PVD coatings while investigating subsequent changes in the substrate and coating properties. For this purpose, a multilayer TiAlN coating with an approximate thickness of 10 ?m was deposited on prepared and unprepared inserts. Moreover, machining tests were conducted on compacted graphite iron (CGI) to understand the effect of pre-deposition edge preparation on cutting performance. The results show that wet blasting has minimal effects on substrate properties, such as hardness. However, it is an effective method to eliminate micro breakage along the cutting edge by reducing geometrically induced stresses. Wet blasting as a pre-deposition treatment also provided a better coating quality, lower cutting forces, longer tool life, and greater consistency in coated tool microgeometry.
    Keywords: PVD coatings; edge preparation; wet blasting; compacted graphite iron; CGI; dry turning.
    DOI: 10.1504/IJMMM.2022.10049063
  • Prediction of surface roughness in cylindrical grinding of glass fibre reinforced epoxy composite   Order a copy of this article
    by Ramesh Rudrapati 
    Abstract: In the present work traverse cut cylindrical grinding of glass fibre reinforced epoxy composite is investigated with the primary objective of identifying the individual and combined effects of grinding processing conditions on surface quality. Based on the full factorial study design, experiments have been carried out. Analysis of variance is applied to examine the experimental data of surface quality. A model equation has been developed using response surface methodology to correlate surface quality and processing conditions. Contour plots have been used as a tool to illustrate the interaction between the individual process variables and surface quality. The desirability function approach has been utilised for the prediction of surface quality. A confirmatory test is then performed to ensure that the predicted control condition is accurate.
    Keywords: cylindrical grinding process; glass fibre reinforced epoxy composite; parametric analysis; surface roughness; response surface methodology; process optimisation.
    DOI: 10.1504/IJMMM.2022.10049847