IJSES V8N

Manufacturing industries are trying to minimize the Total cost of machining in order to get profit.

International Journal of Advanced Manufacturing Technology, 2010

Simulated annealing, genetic algorithm, and particle swarm optimization techniques have been used for exploring optimal machining parameters for single pass turning operation, multi-pass turning operation, and surface grinding operation. The behavior of optimization techniques are studied based on various mathematical models. The objective functions of the various mathematical models are distinctly different from each other. The most affecting machining parameters are considered as cutting speed, feed, and depth of cut. Physical constraints are speed, feed, depth of cut, power limitation, surface roughness, temperature, and cutting force.

Facta Universitatis, 2017

Complex nature of machining processes requires the use of different methods and techniques for process optimization. Over the past few years a number of different optimization methods have been proposed for solving continuous machining optimization problems. In manufacturing environment, engineers are also facing a number of discrete machining optimization problems. In order to help decision makers in solving this type of optimization problems a number of multi criteria decision making (MCDM) methods have been proposed. This paper introduces the use of an almost unexplored MCDM method, i.e. performance selection index (PSI) method for solving machining MCDM problems. The main motivation for using the PSI method is that it is not necessary to determine criteria weights as in other MCDM methods. Applicability and effectiveness of the PSI method have been demonstrated while solving two case studies dealing with machinability of materials and selection of the most suitable cutting fluid for the given machining application. The obtained rankings have good correlation with those derived by the past researchers using other MCDM methods which validate the usefulness of this method for solving machining MCDM problems.

Materials and Manufacturing Processes, 2010

Journal of Neonatal Surgery, 2025

A comprehensive investigation has been conducted on the settings for the turning operation of SS 304. This study utilizes the Grey-Based Taguchi method to explore the multi-objective optimization of the turning process, aiming to determine the optimal combination of settings that results in the shortest production time and the longest tool life. The parameters examined include feed rate, cutting speed, and depth of cut. To address the multi-response optimization challenge, nine experimental runs were performed based on Taguchi's L9 orthogonal array, followed by a Grey relational analysis. The Grey relational grade value was employed to ascertain the most effective parameter levels. Additionally, the analysis of variance (ANOVA) will play a crucial role in identifying the key parameters among speed, feed, and cut depth.

2014

In CNC machining, machining parameters are usually selected prior to machining according to hand books. To assure high quality and productivity we need to optimize the machining parameters. In this study, the average surface roughness values obtained when milling EN24 grade steel with a hardness of 260 BHN using solid coated carbide tools were modelled and optimized using response surface methodology. Input variables consist of cutting speed (V), feed rate (f) and depth of cut (d).The output variables are surface roughness and Material removal rates. Variance analysis is conducted using Design Expert 8.0. The response surface methodology (RSM) has been utilized for the postulation of a second order quadratic model in terms of cutting speed, depth of cut and feed. Sufficient numbers of experiments were run based on the 3 level factorial design concept of RSM in order to generate roughness data. The ANOVA technique has been used to verify the adequacy of the model at 95% confidence in...

International Journal of Industrial Engineering Computations, 2013

The selection of optimum machining parameters plays a significant role to ensure quality of product, to reduce the manufacturing cost and to increase productivity in computer controlled manufacturing process. For many years, multi-objective optimization of turning based on inherent complexity of process is a competitive engineering issue. This study investigates multiresponse optimization of turning process for an optimal parametric combination to yield the minimum power consumption, surface roughness and frequency of tool vibration using a combination of a Grey relational analysis (GRA). Confirmation test is conducted for the optimal machining parameters to validate the test result. Various turning parameters, such as spindle speed, feed and depth of cut are considered. Experiments are designed and conducted based on full factorial design of experiment.

2023

The optimization process is applied to the machining operations in order to provide continual improvement in accuracy and quality of produced parts. The effects of machining parameters in milling operations, such as spindle speed, depth of cut and feed rate are investigated in order to minimize the surface roughness as well as the time of machining process. The effective machining parameters, such as depth of cut, feed rate and spindle speed in turning operations are investigated to minimize the surface roughness as well as the time of machining process. Also, machining parameters, such as peak current, gap voltage, duty cycle and pulse on time in Electro Discharge Machining (EDM) operations can be optimized in order to obtain the optimized material removal rate, tool wear, and surface roughness in part production process. To improve material removal rate, surface roughness, and spark gap in part production process using the wire EDM operations, machining parameters, such as spark on time, spark off time, input current are studied and optimized. To calculate optimized machining parameters, different optimization methods, such as Taguchi method, fuzzy logic algorithm, artificial intelligence, genetic algorithm, artificial neural networks, artificial bee colony algorithm, ant colony optimization and harmony search algorithm are used. As a result, time and cost of accurate production can be reduced to increase productivity in part production process using machining operations. In this paper, a review of machining parameter optimization is presented and future research works are also suggested. The main aim of the study is to review the challenges and limitations of the optimization techniques used in optimizing machining parameters. It has been observed that the research field can be moved forward by reviewing and analyzing recent achievements in the published papers.

Sustainable Production and Consumption, 2021

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.