Optimization of process parameters of mechanical type advanced machining processes using genetic algorithms

2007 ◽  
Vol 47 (6) ◽  
pp. 900-919 ◽  
Author(s):  
Neelesh K. Jain ◽  
V.K. Jain ◽  
Kalyanmoy Deb
Keyword(s):  
Genetic Algorithms ◽  
Process Parameters ◽  
Machining Processes ◽  
Optimization Of Process Parameters ◽  
Advanced Machining

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).

Computational Intelligence in Optimization of Process Parameters in Turning Metals and Composites – A Review

2015 ◽  
Vol 766-767 ◽  
pp. 914-920
Author(s):  
V. Sivaraman ◽  
S. Prakash
Keyword(s):  
Process Parameters ◽  
Computational Intelligence ◽  
Low Cost ◽  
Manufacturing Industries ◽  
Machining Process ◽  
Turning Process ◽  
Machining Processes ◽  
Swarm Optimization ◽  
The Past ◽  
Optimization Of Process Parameters

In the modern competitive scenario in manufacturing industries, producing products with low cost, less time and good quality are the ultimate goal of any manufacturer. To achieve the goal, several optimization tools are developed to optimize the process parameters of the machining process. Turning is one of the machining processes that cannot be avoided in any manufacturing industries. In this review, optimization of process parameters in turning process by computational intelligence (CI) paradigms for the past ten years is studied. Optimization by CI paradigms such as Fuzzy System (FS), Evolutionary Computation techniques Genetic Algorithm (GA), Swarm Intelligence including Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Neural Networks (ANN) etc., is considered. In turning process, surface roughness, tool wear, production time and cost are optimized.

scholarly journals Sustainable Machining Processes through Optimization of Process Parameters

2019 ◽  
Vol 4 (1) ◽  
pp. 22-26
Author(s):  
Temitayo Samson Ogedengbe
Keyword(s):  
Energy Consumption ◽  
Process Parameters ◽  
Machine Tools ◽  
Electrical Energy ◽  
Machining Processes ◽  
Conservation Of Energy ◽  
Optimization Of Process Parameters ◽  
Manufactured Products ◽  
Reduction Of Energy Consumption ◽  
Reducing Energy

Machining processes are a vital part of manufacturing activities in major industries that contributes to the growth of the economy. They mostly require high amount of electrical energy to power the various support modules installed on machine tools. Carrying out machining activities with a view to reducing energy consumption will therefore result in a lowered cost of production for manufactured products. Previous studies on some energy-saving methods adopted by researchers and the limitations faced in the reduction of energy consumption have been discussed. In this work, the effect of process parameters in the conservation of energy during machining processes was experimented. Results shows that much energy could be saved by optimizing parameters before machining.

scholarly journals Optimization of Process Parameters During Electrochemical Machining

2019 ◽  
Vol 8 (12) ◽  
pp. 2683-2687
Keyword(s):  
Process Parameters ◽  
Removal Rate ◽  
Research Work ◽  
Electrochemical Machining ◽  
Machining Processes ◽  
Electrode Gap ◽  
Optimization Of Process Parameters ◽  
Finishing Process ◽  
Nimonic 80A ◽  
The Right

Electrochemical machining is one of the most efficient machining processes due to its ability to produce completely stress-free machined components without any need of further finishing process. However, the right understanding of the effects of key factors during machining of various materials is very important to carry out the machining. It is one of the most efficient way of cutting present in modern era. This present paper deals with the electrochemical machining of Nimonic 80A. Design of the experiments are done by using response surface methodology to study the material removal rate and surface roughness. Process parameters such as voltage, tool feed rate, inter-electrode gap and electrolyte concentration has been optimized by using the ANOVA. The regression models are developed to be used as predictive tools. The confirmation test was conducted to validate the results achieved by GRA approach. This research work helps the industrialist for selecting parameters to attain desired outputs.

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