Experimental Evaluation on Corner Accuracy in WEDM for Aluminium 6061 Alloy

An important electro-thermal process known as wire electrical discharge machining (WEDM) is applied for machining of conductive materials to generate most precisely. All cutting inaccuracies of WEDM arise out of the major cause of wire bending. At the time of cutting a sharp corner or cut profile, bending of the wire leads to a geometrical error on the workpiece. Though this type of error may be of a few hundred microns, it is not suitable for micro applications. In this research study, an experimental investigation based on response surface methodology (RSM) has been done on wire EDM of Aluminium 6061 t6 alloy. This chapter studies the outcome of input process variables (i.e., wire feed rate, pulse on time, pulse off time, and gap voltage) on machining output responses (i.e., corner inaccuracy) extensively. Experimental validation of the proposed model shows that corner inaccuracy value may be reduced by modification of input parameters.

MCDM-Based Optimization of Performance Characteristics During µEDMing of SS 304

Micro-EDM is most widely used for developing perfect drilled micro features/parts. Research was carried out to improve the material removal and tool wear of any conductive machined product by EDM and micro-EDM process. In this chapter, RSM was used for designing the experiments with 20 set of experiments. In this present research work, performance characteristics like MRR and Overcut have got a different level of importance. Here the stress was given on MRR rather than on OC. In this MCDM analysis, the weight of MRR is considered to be maximum (i.e., larger is better), and other weights of other responses are considered to be the minimum (i.e., smaller is better). Finally, in the midst of all the combinations of process parameters considered one that acquires the highest grey relational grade is the best parametric combination. The research findings in the area of machining of stainless steel 304 will be helpful to manufacturing engineers for selecting the optimized parametric combinations of micro-EDM process with stainless steel.

Experimental Investigation on Laser Transmission Welding of Polycarbonate and Acrylic

This chapter presents the effect of various process parameters, namely laser power, pulse frequency, and welding speed, on the weld shear strength and weld width using a diode laser system. Here, laser transmission welding of transparent polycarbonate and black carbon filled acrylic each of 2.8 mm thickness have been performed to create lap joint by using low power laser. Response surface methodology is applied to develop the mathematical model between the laser welding process parameters and the responses of weld joint. The developed mathematical model is tested for its adequacy using analysis of variance and other adequacy measures. It has been observed that laser power and welding speed are the dominant factor followed by frequency. A confirmation test has also been conducted to validate the experimental results at optimum parameter setting. Results show that weld strength of 34.3173 N/mm and weld width of 2.61547 mm have been achieved at optimum parameter setting using desirability function-based optimization technique.

Application of Evolutionary Optimization Techniques Towards Non-Traditional Machining for Performance Enhancement

Electro-chemical machining is a non-conventional machining method that is used for machining of very complicated shape. In this chapter an attempt has been made to carry out multi-objective optimization of the surface roughness (SR) and material removal rate (MRR) for the ECM process of EN 19 on a CNC ECM machine using copper electrode through evolutionary optimization techniques like teaching-learning-based optimization (TLBO) technique and biogeography-based optimization (BBO) technique. The input parameters considered are electrolyte concentration, voltage, feed rate, inter-electrode gap. TLBO and BBO techniques were used to obtain maximum MRR and minimum SR. In addition, obtained optimized values were validated for testing the significance of the TLBO and BBO techniques, and a very small error value of MRR and SR was found. BBO outperformed TLBO in every aspect like less percentage error and better-optimized values; however, TLBO took less computation time than the BBO.

Evaluation of Surface Roughness in Wire Electrical Discharge Turning Process

This investigation presents an experimental investigation in developing small cylindrical pins in electrolytic tough pitch copper (ETP Cu) material using wire electrical discharge turning (WEDT) to evaluate surface roughness of the cylindrical turning faces. The material ETP Cu is soft in nature and has growing range of application in the field of aerospace and electronics industries for advanced applications. In this process, a customized rotary spindle has been developed and added to five-axis CNC wire electrical discharge machine (WEDM) and straight turning of the cylindrical pin has been done up to a length of 15mm with 0.5mm diameter. Under this investigation, 31 experiments along with two confirmation tests have been carried out to study the influence of four design factors—pulse on time, pulse off time, spindle speed, and servo voltage—on the machining performance of surface roughness by means the technique of design of experiment (DOE).

Analysis of Non-Traditional Machining Processes Using Machine Learning

The nature of manufacturing systems faces increasingly complex dynamics to meet the demand for high quality products efficiently. One area, which experienced rapid development in terms not only of promising results but also of usability, is machine learning. New developments in certain domains such as mathematics, computer science, and the availability of easy-to-use tools, often freely available, offer great potential to transform the non-traditional machining domain and its understanding of the increase in manufacturing data. However, the field is very broad and even confusing, which presents a challenge and a barrier that hinders wide application. Here, this chapter helps to present an overview of the available machine learning techniques for improving the non-traditional machining process area. It provides a basis for the subsequent argument that the machine learning is a suitable tool for manufacturers to face these challenges head-on in non-traditional machining processes.

Role of Non-Traditional Machining Equipment in Industry 4.0

The manufacturing industry is undergoing drastic changes owing to a steep rise in business competition and growing complexities in other business perspectives. The highly turbulent market is characterized by ever-increasing mass customization, wide volume-mix, shorter lead time, and low cost, which along with varieties of internal disturbances have complicated the business stability. The multi-agent-based systems comprising of fundamental entities called agents and characterized by autonomy, cooperation, and self-organizing abilities have already made remarkable breakthrough to deal with the challenges through increased robustness, scalability, and enhanced adaptability through their dynamic capabilities. The decision-making ability of the agents can be augmented if equipped with cognitive abilities like that of human beings. The chapter discusses cyber-physical production system (CPPS) to realize cognitive manufacturing in non-conventional machining environments.

Multi-Objective Optimization of EDM Process on AISI P-20 Tool Steel Using Multi-Criteria Decision-Making Technique

The optimization technique is introduced to overcome the problem. Here the author introduces multi-criteria decision-making technique to get the optimization value. Electrical discharge machining (EDM) in nontraditional machining process is applied for machining complicated or intricate geometries on raw materials. The present work attempts to optimize several responses of machining operation using multi-criteria decision making (MCDM) by employing different machining parameters like current, voltage, pulse. The Taguchi L9 orthogonal experimental design is followed during electrical discharge machining of AISI P20 tool steel. Four responses, namely metal removal rate, tool life, surface roughness, and over cut, are considered for optimization. The present work is aimed at multi-response optimization (i.e., higher MRR, higher tool life, lower surface roughness, and minimum overcut), which is conducted using MCDM technique.

A Novel Approach Towards Selection of Role Model Cluster Head for Power Management in WSN

In this chapter, the authors present an innovative, smart controller to sustain mobility in wireless sensor networks (WSNs). Principally, the focal point is dependent on the arrangement of fuzzy input variables (i.e., remaining battery power [RBP], mobility, and centrality solution) to crucial usages, similar to personnel safety in an industrialized atmosphere. A mobility controller dependent upon type-1 fuzzy logic (T1FL) is planned to support sensor mobile nodes (MN). Here, a role model cluster head (RMCH) is picked out among the cluster heads (CHs) that may simply convey the message to the mobile base station (BS) by determining the appropriate type-1 fuzzy (T1F) descriptors such as RBP, mobility of the sink, and the centrality of the clusters. Type-1 fuzzy inference system (Mamdani's rule) is utilized to opt for the possibility to be RMCH. The validity of the introduced model is carried out by means of multiple linear regressions.

Programming for Machining in Electrical Discharge Machine

The correct selection of manufacturing conditions is one of the most important aspects to take into consideration in most manufacturing processes and, particularly, in processes related to electrical discharge machining (EDM). It is a capable of machining geometrically complex or hard material components that are precise and difficult-to-machine such as heat-treated tool steels, composites, super alloys, ceramics, carbides, heat resistant steels, etc. being widely used in die and mold making industries, aerospace, aeronautics, and nuclear industries. This chapter highlights the programming for machining in electrical discharge machine.