A review on conventional and nonconventional machining of SiC particle-reinforced aluminium matrix composites

AbstractAmong the various types of metal matrix composites, SiC particle-reinforced aluminum matrix composites (SiCp/Al) are finding increasing applications in many industrial fields such as aerospace, automotive, and electronics. However, SiCp/Al composites are considered as difficult-to-cut materials due to the hard ceramic reinforcement, which causes severe machinability degradation by increasing cutting tool wear, cutting force, etc. To improve the machinability of SiCp/Al composites, many techniques including conventional and nonconventional machining processes have been employed. The purpose of this study is to evaluate the machining performance of SiCp/Al composites using conventional machining, i.e., turning, milling, drilling, and grinding, and using nonconventional machining, namely electrical discharge machining (EDM), powder mixed EDM, wire EDM, electrochemical machining, and newly developed high-efficiency machining technologies, e.g., blasting erosion arc machining. This research not only presents an overview of the machining aspects of SiCp/Al composites using various processing technologies but also establishes optimization parameters as reference of industry applications

Parametric Optimization of Taper Cutting Process using Wire Electrical Discharge Machining (WEDM)

Significant technological advancement of wire electrical discharge machining (WEDM) process has been observed in recent times in order to meet the requirements of various manufacturing fields especially in the production of parts with complex geometry in precision die industry. Taper cutting is an important application of WEDM process aiming at generating complex parts with tapered profiles. Wire deformation and breakage are more pronounced in taper cutting as compared with straight cutting resulting in adverse effect on desired taper angle and surface integrity. The reasons for associated problems may be attributed to certain stiffness of the wire. However, controlling the process parameters can somewhat reduce these problems. Extensive literature review reveals that effect of process parameters on various performance measures in taper cutting using WEDM is also not adequately addressed. Hence, study on effect of process parameters on performance measures using various advanced metals and metal matrix composites (MMC) has become the predominant research area in this field. In this context, the present work attempts to experimentally investigate the machining performance of various alloys, super alloys and metal matrix composite during taper cutting using WEDM process. The effect of process parameters such as part thickness, taper angle, pulse duration, discharge current, wire speed and wire tension on various performance measures such as angular error, surface roughness, cutting rate and white layer thickness are studied using Taguchi’s analysis. The functional relationship between the input parameters and performance measures has been developed by using non-linear regression analysis. Simultaneous optimization of the performance measures has been carried out using latest nature inspired algorithms such as multi-objective particle swarm optimization (MOPSO) and bat algorithm. Although MOPSO develops a set of non-dominated solutions, the best ranked solution is identified from a large number of solutions through application of maximum deviation method rather than resorting to human judgement. Deep cryogenic treatment of both wire and work material has been carried out to enhance the machining efficiency of the low conductive work material like Inconel 718. Finally, artificial intelligent models are proposed to predict the various performance measures prior to machining. The study offers useful insight into controlling the parameters to improve the machining efficiency

Multi-Objective Optimization of Wire Electro Discharge Machining (WEDM) Process Parameters Using Grey-Fuzzy Approach

Wire electro discharge machining (WEDM) is a versatile non-traditional machining process that is extensively in use to machine the components having intricate profiles and shapes. In WEDM, it is very important to select the optimal process parameters so as to enhance the machine performance. This paper emphasizes the selection of optimal parametric combination of WEDM process while machining on EN31 steel, using grey-fuzzy logic technique. Process parameters such as servo voltage, wire tension, pulse-on-time and pulse-off-time were considered while taking into account several multi-responses such as material removal rate (MRR) and surface roughness (SR). It was found that pulse-on-time of 115 µs, pulse-off-time of 35 µs, servo voltage of 40 V and wire tension of 5 kgf results in a larger value of grey fuzzy reasoning grade (GFRG) which tends to maximize MRR and improve SR. Finally, analysis of variance (ANOVA) is applied to check the influence of each process parameters in the estimation of GFRG

Parametric Optimization of Taper Cutting Process using Wire Electrical Discharge Machining (WEDM)

Significant technological advancement of wire electrical discharge machining (WEDM) process has been observed in recent times in order to meet the requirements of various manufacturing fields especially in the production of parts with complex geometry in precision die industry. Taper cutting is an important application of WEDM process aiming at generating complex parts with tapered profiles. Wire deformation and breakage are more pronounced in taper cutting as compared with straight cutting resulting in adverse effect on desired taper angle and surface integrity. The reasons for associated problems may be attributed to certain stiffness of the wire. However, controlling the process parameters can somewhat reduce these problems. Extensive literature review reveals that effect of process parameters on various performance measures in taper cutting using WEDM is also not adequately addressed. Hence, study on effect of process parameters on performance measures using various advanced metals and metal matrix composites (MMC) has become the predominant research area in this field. In this context, the present work attempts to experimentally investigate the machining performance of various alloys, super alloys and metal matrix composite during taper cutting using WEDM process. The effect of process parameters such as part thickness, taper angle, pulse duration, discharge current, wire speed and wire tension on various performance measures such as angular error, surface roughness, cutting rate and white layer thickness are studied using Taguchi’s analysis. The functional relationship between the input parameters and performance measures has been developed by using non-linear regression analysis. Simultaneous optimization of the performance measures has been carried out using latest nature inspired algorithms such as multi-objective particle swarm optimization (MOPSO) and bat algorithm. Although MOPSO develops a set of non-dominated solutions, the best ranked solution is identified from a large number of solutions through application of maximum deviation method rather than resorting to human judgement. Deep cryogenic treatment of both wire and work material has been carried out to enhance the machining efficiency of the low conductive work material like Inconel 718. Finally, artificial intelligent models are proposed to predict the various performance measures prior to machining. The study offers useful insight into controlling the parameters to improve the machining efficiency

Applications of optimization techniques for parametric analysis of non-traditional machining processes: A Review

The constrained applications of conventional machining processes in generating complex shape ge-ometries with the desired degree of tolerance and surface finish in various advanced engineering materials are being gradually compensated by the non-traditional machining (NTM) processes. These NTM processes usually have higher procurement, maintenance, operating and tooling cost. Hence, in order to attain their maximum machining performance, they are usually operated at their optimal or near optimal parametric settings which can easily be determined by the application of dif-ferent optimization techniques. In this paper, 133 international research papers published during 2012-16 on parametric optimization of NTM processes are extensively reviewed to have an idea on the selected process parameters, observed responses, work materials machined and optimization techniques employed in those processes while generating varying part geometries for their industrial use. It is observed that electro discharge machining is the mostly employed NTM process, applied voltage is the identified process parameter with maximum importance, surface roughness and material removal rate are the two maximally preferred responses, different steel grades are the mostly machined work materials and grey relational analysis is the most popular tool utilized for para-metric optimization of NTM processes. These observations would help the process engineers to attain the machining performance of the NTM processes at their fullest extents for different work material and shape feature combinations

Experimental investigation and optimisation in EDM process of AISI P20 tool steel

Electro Discharge Machining (EDM) is an extremely prominent machining process among newly developed non -traditional machining techniques for “difficult to machine” conducting materials such as heat treated tool steels, composites, super alloys, ceramics, hastelloys, nitralloy, nemonic alloys, carbides, heat resistant steels etc. In EDM, the material removal of the electrode is achieved through high frequency sparks between the tool and the work-piece immersed into the dielectric. The Material Removal Rate (MRR), Tool Wear Rate (TWR) and surface integrity are some of the important performance attributes of EDM process. The objective of EDM is to get high MRR along with achieving reasonably good surface quality of machined component.The machining parameters that achieve the highest MRR strongly depend on the size of the machining surface i.e. the engaged electrode and work-piece surface.With upcoming worldwide applications of AISI P20 machining has become an important issue which needs to be investigated in detail. The AISI P20 steel is applied by the tooling industry as material for injection molding tools. These steel are categorized as “difficult to machine” materials, since they posses greater strength and toughness. Therefore, AISI P20 steel is usually known to create major challenges during conventional and non- conventional machining.Keeping this in view, an experimental investigation to explore the productivity, quality, surface integrity, and accuracy on the EDM surface. The work has been carried out by conducting a set of experiments using AISI P20 tool steel work-piece with copper electrode. Important machining parameters like Discharge current (Ip), Pulse on Time(Ton), Pulse off Time (Toff ), Lift Time (Tup) and Work Time (Tw) are considered for investigation. The effect of the machining parameters on the responses such as MRR, TWR, Surface Roughness (SR), and Micro hardness were investigated. Now-a-days optimization and modeling of EDM process is a highly demanding r

Optimization of EDM Process Parameters through Teaching Learning Based Optimization Algorithm

Electrical Discharge Machining (EDM) is a non-traditional machining process where intricate and complex shapes can be machined. Only electrically conductive materials can be machined by this process and is one of the important machining processes for machining high strength, temperature-resistant (HSTR) alloys. For achieving the best performance of the EDM process, it is crucial to carry out parametric design responses such as Material Removal Rate, Tool Wear Rate, Gap Size etc. It is essential to consider most number of input parameters to get the better result. In the present work Teaching-Learning-Based optimization (TLBO) algorithm has been applied for multi-objective optimization of the responses of EDM process. The optimization performance of the TLBO algorithm is compared with that of other population-based algorithms, e.g., genetic algorithm (GA), ant colony optimization (ACO), and artificial bee colony (ABC) algorithm. It is observed that the TLBO algorithm performs better than the others with respect to the optimal process response values

Micro-Electro Discharge Machining: Principles, Recent Advancements and Applications

Micro electrical discharge machining (micro-EDM) is a thermo-electric and contactless process most suited for micro-manufacturing and high-precision machining, especially when difficult-to-cut materials, such as super alloys, composites, and electro conductive ceramics, are processed. Many industrial domains exploit this technology to fabricate highly demanding components, such as high-aspect-ratio micro holes for fuel injectors, high-precision molds, and biomedical parts.Moreover, the continuous trend towards miniaturization and high precision functional components boosted the development of control strategies and optimization methodologies specifically suited to address the challenges in micro- and nano-scale fabrication.This Special Issue showcases 12 research papers and a review article focusing on novel methodological developments on several aspects of micro electrical discharge machining: machinability studies of hard materials (TiNi shape memory alloys, Si3N4–TiN ceramic composite, ZrB2-based ceramics reinforced with SiC fibers and whiskers, tungsten-cemented carbide, Ti-6Al-4V alloy, duplex stainless steel, and cubic boron nitride), process optimization adopting different dielectrics or electrodes, characterization of mechanical performance of processed surface, process analysis, and optimization via discharge pulse-type discrimination, hybrid processes, fabrication of molds for inflatable soft microactuators, and implementation of low-cost desktop micro-EDM system

Multi-objective optimisation and analysis of EDM of AISI P20 tool steel

Electric Discharge Machining (EDM) is one of the non traditional machining processes used to produce critical shape on hard or brittle conductive materials and it can also be successfully applied on materials that are extremely difficult-to-machine using traditional machining processes. The experimental investigation of EDM process parameters is of utter importance in order to improve the productivity, surface integrity and quality characteristics. An efficient method for determining the optimum process parameters for multiple performance characteristics, through various multi-optimisation techniques from the experiment trials, is a necessity of the present industry. The work piece material for the current research work was AISI P20 tool steel and a cylindrical copper electrode was used with lateral flushing of dielectric fluid during the first phase of the study. AISI P20 tool steel has growing range of applications like in plastic moulds, frames for plastic pressure dies, hydro forming tools, which offer difficulty in conventional machining in hardened condition. Influence of various process parameters on MRR, TWR and OC has been investigated during EDMof AISI P20 tool steel. Different multi-objective optimisation techniques such as grey-Taguchi and fuzzy logic combined with Response Surface Methodology (RSM) have been utilized in order to achieve optimal combinations of EDM parameters like discharge current, pulse-on time, work time, lift time, and inter electrode gap which would result in maximum MRR as well as minimum TWR and OC. Working time did not have any influence on performance measures of EDM, while other parameters had significant effect. Both grey relation analysis and fuzzy logic technique have been implemented to convert multiple responses in EDM into a single one and optimise the above responses. Finally, respective confirmation tests were carried out to obtain optimal process parameters

Principles and Characteristics of Different EDM Processes in Machining Tool and Die Steels

Electric discharge machining (EDM) is one of the most efficient manufacturing technologies used in highly accurate processing of all electrically conductive materials irrespective of their mechanical properties. It is a non-contact thermal energy process applied to a wide range of applications, such as in the aerospace, automotive, tools, molds and dies, and surgical implements, especially for the hard-to-cut materials with simple or complex shapes and geometries. Applications to molds, tools, and dies are among the large-scale initial applications of this process. Machining these items is especially difficult as they are made of hard-to-machine materials, they have very complex shapes of high accuracy, and their surface characteristics are sensitive to machining conditions. The review of this kind with an emphasis on tool and die materials is extremely useful to relevant professions, practitioners, and researchers. This review provides an overview of the studies related to EDM with regard to selection of the process, material, and operating parameters, the effect on responses, various process variants, and new techniques adopted to enhance process performance. This paper reviews research studies on the EDM of different grades of tool steel materials. This article (i) pans out the reported literature in a modular manner with a focus on experimental and theoretical studies aimed at improving process performance, including material removal rate, surface quality, and tool wear rate, among others, (ii) examines evaluation models and techniques used to determine process conditions, and (iii) discusses the developments in EDM and outlines the trends for future research. The conclusion section of the article carves out precise highlights and gaps from each section, thus making the article easy to navigate and extremely useful to the related research communit