A review of optimization techniques in metal cutting processes

Mukherjee, Indrajit; Ray, Pradip Kumar

doi:10.1016/j.cie.2005.10.001

Cited by 442 publications

(202 citation statements)

References 101 publications

Supporting

Mentioning

197

Contrasting

Unclassified

Order By: Relevance

“…To establish the relationship between the cutting performance and the cutting parameters, few statistical models based on formal methods (optimal solutions) and non-conventional techniques (nearoptimal solutions) have been designed (Mukherjee and Pradip 2006). In conventional methods, experimental method-based approaches, such as the response surface methodology and the Taguchi methodology, were widely and successfully used.…”

Section: Methodology and Datamentioning

confidence: 99%

Robust parameter design for the optimisation of cutting conditions according to energy efficiency criteria

Macak¹,

Hron²

2016

AGR ECON-CZECH

View full text Add to dashboard Cite

An important objective in process design in the wood-processing industry is to make the system robust -or insensitive -to sources of variability that cannot be entirely controlled once the system is in use. Experimental designs can be used to solve some of these problems. Th e fi rst techniques were introduced in the 1980s by Genichi Taguchi, a Japanese engineer. Th e objective of this paper is to apply advanced methods for optimization of cutting conditions using a robust design in the wood-processing industry according to energy effi ciency criteria. By showing some numerical simulation results, the eff ectiveness of the proposed model is illustrated.

show abstract

Section: Methodology and Datamentioning

confidence: 99%

Robust parameter design for the optimisation of cutting conditions according to energy efficiency criteria

Macak¹,

Hron²

2016

AGR ECON-CZECH

View full text Add to dashboard Cite

show abstract

“…Several modeling techniques of input-output and in-process parameter relationship using ANN sets offer a distribution-free alternative and have attracted the attention of manufacturing practitioners and researchers alike when they run into difficulties in building empirical models in metal cutting process control. These techniques can offer a cost effective alternative in the field of machine tool design and manufacturing approaches, and have thus received wide attention in recent years on optimization techniques in metal cutting processes, computers & industrial engineering [7].…”

Section: Literature Surveymentioning

confidence: 99%

Optimization Technique For Surface Roughness Prediction in Turning Operation

Ojha¹,

Yadav²,

Yadav

2015

SIJSMS

View full text Add to dashboard Cite

show abstract

“…Many researchers and authors have found classical optimization methods ineffective for solving machining optimization problems because of the following: a tendency to obtain a local optimal solution (Rao & Pawar, 2010), very complex nature and inability to handle multi-objective problems effectively (Rao & Kalyankar, 2013) and lack of robustness (Rao & Pawar, 2009;Rao & Pawar 2010). As noted by Mukherjee and Ray (2006), researchers and practitioners prefer an alternative cost effective near-optimal (or approximate) solution than the exact optimal one, as it may be extremely difficult to find the exact optimal point in the high dimensional and multimodal search space. Rao et al (2008) used the PSO algorithm to determine optimal machining parameter settings for an electro-chemical machining (ECM) process and compared its performance with that obtained by other optimization methods.…”

Section: Introductionmentioning

confidence: 99%

Application of multi-stage Monte Carlo method for solving machining optimization problems

Madić¹,

Kovačević²,

Radovanović³

2014

10.5267/j.ijiec

View full text Add to dashboard Cite

Enhancing the overall machining performance implies optimization of machining processes, i.e. determination of optimal machining parameters combination. Optimization of machining processes is an active field of research where different optimization methods are being used to determine an optimal combination of different machining parameters. In this paper, multi-stage Monte Carlo (MC) method was employed to determine optimal combinations of machining parameters for six machining processes, i.e. drilling, turning, turn-milling, abrasive waterjet machining, electrochemical discharge machining and electrochemical micromachining. Optimization solutions obtained by using multi-stage MC method were compared with the optimization solutions of past researchers obtained by using meta-heuristic optimization methods, e.g. genetic algorithm, simulated annealing algorithm, artificial bee colony algorithm and teaching learning based optimization algorithm. The obtained results prove the applicability and suitability of the multi-stage MC method for solving machining optimization problems with up to four independent variables. Specific features, merits and drawbacks of the MC method were also discussed.

show abstract

A review of optimization techniques in metal cutting processes

Cited by 442 publications

References 101 publications

Robust parameter design for the optimisation of cutting conditions according to energy efficiency criteria

Robust parameter design for the optimisation of cutting conditions according to energy efficiency criteria

Optimization Technique For Surface Roughness Prediction in Turning Operation

Application of multi-stage Monte Carlo method for solving machining optimization problems

Contact Info

Product

Resources

About