Process Optimization in Non-Conventional Processes

2017 ◽  
pp. 82-119
Author(s):  
Milan Kumar Das ◽  
Tapan Kumar Barman ◽  
Prasanta Sahoo ◽  
Kaushik Kumar
Keyword(s):  
Process Parameters ◽  
Morphological Study ◽  
Artificial Bee Colony ◽  
Removal Rate ◽  
Work Piece ◽  
Bee Colony ◽  
Hard Metals ◽  
Optimal Values ◽  
Grey Relational ◽  
Conventional Machining

Conventional machining becomes non-efficient and non-effective in case of intricate shape and also while working with hard metals and alloys due to excessive tool wear. In such situations non-conventional machining, in contrast becomes more appropriate due to non-contact between tool and work-piece. In the present study, EN31 steel was machined using Plasma Arc Cutting with pre-defined process parameters. Material Removal Rate and Surface roughness were considered as responses for the study. The responses were optimized both as single and multi-response. Considering the complexities of this present problem, experimental data were generated and the results were analyzed by using Taguchi, Grey Relational Analysis and Artificial Bee Colony (ABC) Algorithm. Responses variances with the variation of process parameters were thoroughly studied and analyzed and ‘best optimal values' were identified. The result were verified by the morphological study. It was observed that there was an improvement in responses from mean to optimal values of process parameters.

Optimization of Process Parameters Using Soft Computing Techniques

2017 ◽  
pp. 177-220
Author(s):  
Supriyo Roy ◽  
Kaushik Kumar ◽  
J. Paulo Davim
Keyword(s):  
Soft Computing ◽  
Process Parameters ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Principal Component ◽  
Optimization Of Process Parameters ◽  
Hard Metals ◽  
Optimal Values ◽  
Grey Relational ◽  
Conventional Machining

Machining of hard metals and alloys using Conventional machining involves increased demand of time, energy and cost. It causes tool wear resulting in loss of quality of the product. Non-conventional machining, on the other hand produces product with minimum time and at desired level of accuracy. In the present study, EN19 steel was machined using CNC Wire Electrical discharge machining with pre-defined process parameters. Material Removal Rate and Surface roughness were considered as responses for this study. The present optimization problem is single and as well as multi-response. Considering the complexities of this present problem, experimental data were generated and the results were analyzed by using Taguchi, Grey Relational Analysis and Weighted Principal Component Analysis under soft computing approach. Responses variances with the variation of process parameters were thoroughly studied and analyzed; also ‘best optimal values' were identified. The result shows an improvement in responses from mean to optimal values of process parameters.

Multi-Objective Optimization of Machining Process Parameters in Wire-Cut Electric Discharge Machining of Inconel X750 Alloy by Combinatorial Approach

2019 ◽  
Vol 969 ◽  
pp. 781-786 ◽  
Author(s):  
G. Venkata Ajay Kumar ◽  
M. Shilpa ◽  
Udagave Shital Purander ◽  
G. Madhoo ◽  
V. Asokan
Keyword(s):  
Process Parameters ◽  
Electric Discharge ◽  
Removal Rate ◽  
High Hardness ◽  
Machining Process ◽  
Machining Processes ◽  
Grey Relational ◽  
Pulse On Time ◽  
Pulse Off Time ◽  
Conventional Machining

Difficult-to-cut materials, generally high hardness, strength and toughness, are generally difficult to machine in conventional machining. Also tool wear is high in conventional machining processes. Wire Cut Electric Discharge (WEDM) machining is particularly used for machining complex profiles, demanding very high accuracy. The current work focuses on the optimization of roughness of a surface that is machined using WEDM; the process parameters considered for optimization are pulse-on-time (Pon), pulse-off time (Poff), wire feedrate (WFR) and spark gap voltage (SGV). One of the surface integrity aspect is considered as surface roughness (SR) and other related to machining output considered as material removal rate (MRR) for the output responses. The paper presents, a multi-criteria decision making technique, with Grey Relational Analysis (GRA) integrated with Particle Swarm Optimization (PSO) for optimizing the process parameters. Further, confirmation tests that were conducted also validated the improvement in SR and MRR.

Surface Roughness Optimization in Turning Operation Using Hybrid Algorithm of Artificial Bee Colony with RSM

2015 ◽  
Vol 1101 ◽  
pp. 393-396
Author(s):  
Mohammad Ahsan Habib ◽  
Md. Anayet U. Patwari ◽  
Koushik Alam Khan ◽  
A.N.M. Amanullah Tomal
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Artificial Bee Colony ◽  
Removal Rate ◽  
Machining Parameters ◽  
Turning Operation ◽  
Bee Colony ◽  
Optimal Value ◽  
Intelligent Behavior ◽  
Desirability Analysis

For cost reduction and quality improvement of machining products, optimum output machining parameters such as material removal rate, tool wear ratio and surface roughness is very essential. Moreover, these output parameters are strongly depends on the precision of the machine tool as well as the input machining parameters. In this paper, a hybrid model of Artificial Bee Colony (ABC), which is motivated by the intelligent behavior of honey bees with Response Surface Methodology (RSM), has been developed for optimizing the surface roughness of stainless steel during turning operation. The predicted optimal value of surface roughness of stainless steel is further confirmed by conducting supplementary experiments. Finally, the performance of this algorithm is evaluated in comparison with desirability analysis. The performance of ABC is at par with that of desirability analysis for different parametric conditions.

scholarly journals Multi Performance Optimizationof Shoulder Milling Process Parameters of AA6063 T6 Aluminium Alloy by Taguchi Based GRA

2019 ◽  
Vol 8 (10S) ◽  
pp. 420-425
Keyword(s):  
Process Parameters ◽  
Grey Relational Analysis ◽  
Removal Rate ◽  
Analysis Data ◽  
Milling Process ◽  
Depth Of Cut ◽  
Analysis Method ◽  
Relational Analysis ◽  
Grey Relational Grade ◽  
Grey Relational

In this paper, a grey relational analysis method based on Taguchi is proposed to improve the multi-performance characteristics of VMC shoulder milling process parameters in the processing of AA6063 T6. Taking into account four process parameters such as coolant, depth of cut,speed and feed, there are three level of each process parameter in addition to two levels of coolant. 18 experiments were used by L18 orthogonal array using the taguchi method. Multi-performance features like surface roughness and material removal rate are used. Grey Relational Analysis method is used to obtain the Grey Relational Grade, and the multiperformance characteristics of the process are pointed out. Then, the Taguchi response table method and ANOVA are used to analysis data. In order to ensure the validity of the test results, a confirmation test was conducted. The study also shows that this method can effectively improve the multi-function characteristics of shoulder milling process.In his work microstructure and mechanical properties of AA6063 T6before and after shoulder milling have been investigated.

scholarly journals Optimization of advanced manufacturing processes using socio inspired cohort intelligence algorithm

2022 ◽  
Vol 13 ◽  
pp. 6
Author(s):  
Ishaan R. Kale ◽  
Mayur A. Pachpande ◽  
Swapnil P. Naikwadi ◽  
Mayur N. Narkhede
Keyword(s):  
Process Parameters ◽  
Removal Rate ◽  
Harmony Search ◽  
Optimization Techniques ◽  
Technological Advancement ◽  
Machining Processes ◽  
Bee Colony ◽  
Optimization Of Process Parameters ◽  
Advanced Machining ◽  
Linear And Nonlinear Constraints

The demand of Advanced Machining Processes (AMP) is continuously increasing owing to the technological advancement. The problems based on AMP are complex in nature as it consisted of parameters which are interdependent. These problems also consisted of linear and nonlinear constraints. This makes the problem complex which may not be solved using traditional optimization techniques. The optimization of process parameters is indispensable to use AMP's at its aptness and to make it economical to use. This paper states the optimization of process parameters of Ultrasonic machining (USM) and Abrasive water jet machining (AWJM) processes to maximize the Material Removal Rate (MRR) using a socio inspired Cohort Intelligent (CI) algorithm. The constraints involved with these problems are handled using static penalty function approach. The solutions are compared with other contemporary techniques such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Modified Harmony Search (HS_M) and Genetic Algorithm (GA).

scholarly journals Improving the process performance of magnetic abrasive finishing of SS304 material using multi-objective artificial bee colony algorithm

2020 ◽  
Vol 41 (1) ◽  
pp. 34-49
Author(s):  
Sandip B. Gunjal ◽  
Padmakar J. Pawar
Keyword(s):  
Surface Roughness ◽  
Material Removal Rate ◽  
Material Removal ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Removal Rate ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Bee Colony ◽  
Abrasive Finishing

Magnetic abrasive finishing is a super finishing process in which the magnetic field is applied in the finishing area and the material is removed from the workpiece by magnetic abrasive particles in the form of microchips. The performance of this process is decided by its two important quality characteristics, material removal rate and surface roughness. Significant process variables affecting these two characteristics are rotational speed of tool, working gap, weight of abrasive, and feed rate. However, material removal rate and surface roughness being conflicting in nature, a compromise has to be made between these two objective to improve the overall performance of the process. Hence, a multi-objective optimization using an artificial bee colony algorithm coupled with response surface methodology for mathematical modeling is attempted in this work. The set of Pareto-optimal solutions obtained by multi-objective optimization offers a ready reference to process planners to decide appropriate process parameters for a particular scenario.

Multi-response optimization of CNC turning parameters of austenitic stainless steel 303 using Taguchi-based grey relational analysis

2020 ◽  
Vol 44 (4) ◽  
pp. 592-601
Author(s):  
S.R. Sundara Bharathi ◽  
D. Ravindran ◽  
A. Arul Marcel Moshi
Keyword(s):  
Stainless Steel ◽  
Process Parameters ◽  
Grey Relational Analysis ◽  
Removal Rate ◽  
Regression Equations ◽  
Turning Process ◽  
Cnc Turning ◽  
Relational Analysis ◽  
Grey Relational ◽  
Cnc Turning Process

Extensive research has been carried out to optimize the process parameters of several machining processes. Optimizing the influencing parameters of the turning operation is a precise action that determines the desired level of quality. This study focuses on the multi-criteria optimization of the CNC turning process parameters of stainless steel 303 (SS 303) material to achieve minimum surface roughness (Ra) with maximum material removal rate (MRR) by means of Taguchi-based grey relational analysis. A CNC machine was tested following Taguchi’s L9 orthogonal array design. Grey relational analysis was used as the multi-criteria optimization tool. The significance of each individual process parameter on the overall characteristics of the turned specimen was estimated using analysis of variance (ANOVA). Regression equations were generated using the input factors with the selected output parameters. In addition, a morphological study of the chips produced by the turning process was carried out using SEM images in order to relate the chip geometry with the output responses.

scholarly journals Multi-Objective Parameter Optimization for Disc Milling Process of Titanium Alloy Blisk Channels

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 173
Author(s):  
Zhishan Li ◽  
Yaoyao Shi
Keyword(s):  
Residual Stress ◽  
Process Parameters ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Milling Process ◽  
Cutter Life ◽  
Stress Layer ◽  
Grey Relational ◽  
Optimal Process Parameter

The blisk has been widely used in modern high performance aero-engines of high thrust-weight ratio. Disc milling process provides a reliable way to improve the efficiency of the blisk milling. The process parameters of disc milling have crucial effects on the milling efficiency and physical property of blisk. In this paper, material removal rate, cutter life and thickness of residual stress layer are regarded as optimization targets the key process parameters such as spindle speed, cutting depth and feed speed are optimized. Based on the grey relational analysis, the multi-objective optimization problem is transformed into a single objective optimization problem. At the same time, the problem of non-symmetry influence of key process parameters on optimization targets can be solved. And the influence weight of material removal rate, cutter life and thickness of residual stress layer on the grey relational grade (GRG) are calculated according to principal component analysis. The second order prediction model of GRA is developed by response surface method. On the basis of verifying the accuracy of the model, the influence mechanism of the process parameters coupling on the gray correlation degree is analyzed the optimal process parameter combination is obtained as spindle speed with 81.92 rpm, cutting depth with 5.88 mm and feed rate with 66.0823 mm/min. The experimental research show that the optimal process parameter combination can effectively improve the material removal rate and cutter life and reduce the thickness of residual stress layer.

scholarly journals Parametric Optimization of Nd:YAG Laser Beam Machining Process Using Artificial Bee Colony Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Rajarshi Mukherjee ◽  
Debkalpa Goswami ◽  
Shankar Chakraborty
Keyword(s):  
Genetic Algorithm ◽  
Laser Beam ◽  
Process Parameters ◽  
Nd:Yag Laser ◽  
Artificial Bee Colony ◽  
Parametric Optimization ◽  
Machining Process ◽  
Laser Beam Machining ◽  
Abc Algorithm ◽  
Bee Colony

Nd:YAG laser beam machining (LBM) process has a great potential to manufacture intricate shaped microproducts with its unique characteristics. In practical applications, such as drilling, grooving, cutting, or scribing, the optimal combination of Nd:YAG LBM process parameters needs to be sought out to provide the desired machining performance. Several mathematical techniques, like Taguchi method, desirability function, grey relational analysis, and genetic algorithm, have already been applied for parametric optimization of Nd:YAG LBM processes, but in most of the cases, suboptimal or near optimal solutions have been reached. This paper focuses on the application of artificial bee colony (ABC) algorithm to determine the optimal Nd:YAG LBM process parameters while considering both single and multiobjective optimization of the responses. A comparative study with other population-based algorithms, like genetic algorithm, particle swarm optimization, and ant colony optimization algorithm, proves the global applicability and acceptability of ABC algorithm for parametric optimization. In this algorithm, exchange of information amongst the onlooker bees minimizes the search iteration for the global optimal and avoids generation of suboptimal solutions. The results of two sample paired t-tests also demonstrate its superiority over the other optimization algorithms.

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