Kumar Abhishek

A simulation approach for estimating flank wear and material removal rate in turning of Inconel 718

Abstract The present work attempts to study the effect of important machining variables on performance characteristics such as material removal rate and tool wear in turning of Inconel 718 using chemical vapour deposition (CVD) coated tungsten carbide (WC) tool. A three dimensional machining model using Lagrangian approach has been developed using DEFORM 3D. The machining simulation is carried out to predict the flank wear and material removal rate (MRR). Flank wear is calculated using Usui’s wear model in the simulation model. The results from simulation model are compared with experimental data generated by the use of Taguchi’s L 16 orthogonal array for reducing the experimental runs. Analysis of variance (ANOVA) is performed to identify the most influencing variables for both the performance characteristics. It is found that simulation results are in good agreement with experimental results. A valid simulation models helps the tool engineers to gather relevant process related information without resorting to costly and time consuming experimentation.

Parametric appraisal and optimization in machining of CFRP composites by using TLBO (teaching-learning based optimization algorithm)

The present paper focuses on machining (turning) aspects of CFRP (epoxy) composites by using single point HSS cutting tool. The optimal setting i.e. the most favourable combination of process parameters (such as spindle speed, feed rate, depth of cut and fibre orientation angle) has been derived in view of multiple and conflicting requirements of machining performance yields viz. material removal rate, surface roughness, SR

Optimization of drilling process parameters by harmony search algorithm

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Taguchi approach followed by fuzzy linguistic reasoning for quality‐productivity optimization in machining operation

(Ra) (of the turned product) and cutting force. This study initially derives mathematical models (objective functions) by using statistics of nonlinear regression for correlating various process parameters with respect to the output responses. In the next phase, the study utilizes a recently developed advanced optimization algorithm teaching–learning based optimization (TLBO) in order to determine the optimal machining condition for achieving satisfactory machining performances. Application potential of TLBO algorithm has been compared to that of genetic algorithm (GA). It has been observed that exploration of TLBO appears more fruitful in contrast to GA in the context of this case experimental research focused on machining of CFRP composites.

Machining performance optimisation during EDM of Inconel 718: a case experimental investigation

With the widespread application of carbon fibre-reinforced polymer (CFRP) composites, mostly in defence, automotive, and aerospace industries, the machining of those materials has become a major concern today. As the machinability of those composites differs from the conventional metals, a proper understanding of process behaviour and identification of the favourable machining environment (optimal setting of process parameters) are indeed necessary to improve product quality. The present work highlights the application potential of a multi-response optimization route by integrating nonlinear regression modelling, fuzzy inference system (FIS) in combination with the JAYA optimization algorithm, for the selection of optimal process parameter setting during the machining (turning) of carbon fibre-reinforced (epoxy) composites. Experiments have been carried out in consideration with spindle speed, feed rate, and depth of cut as process control parameters, whereas material removal rate (MRR), roughness average (Ra), and net cutting force have been treated as machining performance characteristics. Attempt has been made to identify the best setting of process parameters for optimizing aforesaid output responses, simultaneously. The result of the JAYA algorithm has also been compared to that of TLBO (teaching–learning-based optimization) algorithm. In addition to this, the result obtained thereof has also been compared to that of two evolutionary optimization algorithms viz., GA (genetic algorithm) and ICA (imperialist competitive algorithm). Good agreement has been observed amongst the obtained results. The aforesaid case experimental study thus exhibits the application potential of a newly developed JAYA algorithm in the context of machining performance optimization during the turning of CFRP composites. The JAYA algorithm is basically a parameter-less optimization algorithm which does not require any algorithm-specific parameter and hence easy to implement.

Multi-objective optimisation during drilling of CFRP composites: a PCA-fuzzy Taguchi integrated approach

Titanium alloy find widespread applications in different fields such as in aerospace, bio-medical and electronics due to its superior physical properties (high strength, toughness, corrosion resistance and durability and low density). Further, its biological compatibility makes it useful in a variety of applications in bio-medical engineering. Drilling is one of the important machining processes involved in most of its application fields. In drilling process, it is desirable to obtain good hole quality at minimum thrust. The present work investigates the influence of various control parameters on circularity at entry and thrust force in drilling of titanium alloy using face centered central composite design. The optimal parameters that maximize circularity and minimize thrust force have been obtained by harmony search (HS) algorithm. The fitness value of 2.8878 and 1.0171 for thrust obtain from GA and HS algorithm respectively and fitness of 0.0895 and 0.1143 for circularity at entry according to GA and HS algorithm respectively. The result suggests that HS algorithm is a robust technique as compared to GA in solving the machining problems.

Fuzzy embedded imperialist competitive algorithm (ICA) for multi-response optimization during machining of CFRP (Epoxy) composites

Carbon Fiber Reinforced Polymer (CFRP) composite materials find varied engineering applications especially in automotive, aircraft and locomotive industries. Hence, it has become essential to study machining and machinability aspects of these composites. This paper reports an application of harmony research (HS) algorithm through a case experimental research in order to obtain optimal parametric combination in turning of CFRP (epoxy) composites. Taguchi’s L9 orthogonal array has been used for experimentation. The performance indices such as surface roughness and cutting force have been chosen; and corresponding machining parameters that have been studied like spindle speed, feed rate and depth of cut. Optimal results have also been compared with genetic algorithm (GA); it has been revealed that harmony search method provided better result as compared to genetic algorithm.

Multi-response Optimization in Machining of GFRP (Epoxy) Composites: An Integrated Approach

Purpose – The study has been aimed to search an appropriate process environment for simultaneous optimization of quality‐productivity favorably. Various surface roughness parameters (of the machined product) have been considered as product quality characteristics whereas material removal rate (MRR) has been treated as productivity measure for the said machining process.Design/methodology/approach – In this study, three controllable process parameters, cutting speed, feed, and depth of cut, have been considered for optimizing material removal rate (MRR) of the process and multiple surface roughness features for the machined product, based on L9 orthogonal array experimental design. To avoid assumptions, limitation, uncertainty and imprecision in application of existing multi‐response optimization techniques documented in literature, a fuzzy inference system (FIS) has been proposed to convert such a multi‐objective optimization problem into an equivalent single objective optimization situation by adapting F...