Optimization of multi-pass turning operations using ant colony system

Vijayakumar, K.; Prabhaharan, G.; Asokan, P.; Saravanan, R.

doi:10.1016/s0890-6955(03)00081-6

Cited by 113 publications

(41 citation statements)

References 13 publications

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“…Finish machining: All the constraints other than the surface finish constraint are similar for rough and finish machining [15].…”

Section: Chip-tool Interface Temperature Constraintmentioning

confidence: 99%

Optimization of Cutting Conditions in Multi-Pass Turning Using Hybrid Genetic Algorithm-Sequential Quadratic Programming

Belloufi¹

2012

J Appl Mech Eng

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IntroductionThe selection of optimal cutting parameters, like the number of passes, depth of cut for each pass, feed and speed, is a very important issue for every machining process [1]. Several cutting constraints must be considered in machining operations. In turning operations, a cutting process can possibly be completed with a single pass or by multiple passes. Multi-pass turning is preferable over single-pass AbstractIn this paper, a new, hybrid genetic algorithm-sequential quadratic programming is used for the resolution of cutting conditions. It used for the resolution of a multi-pass turning optimization case by minimizing the production cost under a set of machining constraints. The result indicates that the proposed hybrid genetic algorithm-sequential quadratic programming is effective when compared to other techniques carried out by different researchers.turning in the industry for economic reasons [2]. The optimization problem of machining parameters in multi-pass turnings becomes very complicated when plenty of practical constraints have to be considered [3]. Traditionally, mathematical programming techniques like graphical methods [4], linear programming [5], dynamic programming [6,7] and geometric programming [8,9] had been used to solve optimization problems of machining parameters in multi-pass turnings. However, these traditional methods of optimization do not fare well over a broad spectrum of problem domains. Moreover, traditional techniques may not be robust. Numerous constraints and multiple passes make machining optimization problems complicated and hence these techniques are not ideal for solving such problems as they tend to obtain a local optimal solution. Thus, meta-heuristic algorithms have been developed to solve machining economics problems because of their power in global searching. There have been some works regarding optimization of cutting parameters [2,3,[10][11][12][13][14] for different situations, authors have been trying to bring out the utility and advantages of genetic algorithm, evolutionary approach and simulated annealing. It is proposed to use the hybrid genetic algorithm-sequential quadratic programming for the machining optimization problems.The current paper focuses on the application of a new optimization technique, hybrid genetic algorithm-sequential quadratic programming, to determine the optimal machining parameters that minimize the unit production cost in multi-pass turnings. Cutting process modelDecision variables: In the constructed optimization problem, six decision variables are considered: cutting speeds in rough and finish machining (V r , V s ), feed rates in rough and finish machining (f r , f s ) and depth of cut for each pass of rough and finish machining ( )Objective function: Based on the minimum unit production DOI: 10.4172/2168-9873.1000101 Journal of Applied Mechanical Engineering . 1000 1000 ConstraintsThere are some constraints which affect the selection of the optimal cutting conditions and will be taken into account. The constraints in ...

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“…Finish machining: All the constraints other than the surface finish constraint are similar for rough and finish machining [15].…”

Section: Chip-tool Interface Temperature Constraintmentioning

confidence: 99%

Optimization of Cutting Conditions in Multi-Pass Turning Using Hybrid Genetic Algorithm-Sequential Quadratic Programming

Belloufi¹

2012

J Appl Mech Eng

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“…Camposeco-Negrete [2] states that cost and quality are the main goals of manufacturing companies. To improve quality in this type of process, several authors have studied the turning process using mathematical strategies in order to contribute to the efficiency of these processes, such as: the Taguchi method [3], ant colony optimization [4], genetic algorithm [5] and response surface methodology (RSM) [6].…”

Section: Introductionmentioning

confidence: 99%

A Weighted Mean Square Error Approach to the Robust Optimization of the Surface Roughness in an AISI 12L14 Free-Machining Steel-Turning Process

2018

SV-JME

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Manufacturing processes are aimed at transforming materials into goods, generating wealth [1]. Camposeco-Negrete [2] states that cost and quality are the main goals of manufacturing companies. To improve quality in this type of process, several authors have studied the turning process using mathematical strategies in order to contribute to the efficiency of these processes, such as: the Taguchi method The primary input parameters in the turning process, i.e., cutting speed, feed rate and depth of cut [7], are directly responsible for the quality and productivity characteristics of the process, such as the amount of material removed, tool wear, and finishing of the product [8].Furthermore, the finishing of the machined parts can be evaluated according to the surface roughness, which are irregularities presented on the surface of the parts, characterized by grooves made by the tool during the machining process [9] of the cutting parameters (ranging from a single parameter per experiment) in the quality responses, such as tool life and surface roughness. This paper makes use of only one roughness parameter (arithmetic average roughness, R a ), considering the calculation of its metrics of the roughness characteristics (R a , R y , R q , R z and R t ). The average arithmetic roughness (R a ) is the arithmetic mean of the absolute values of the ordinates of the effective (measured) profile in relation to the midline in a sample length (Fig. 1). In addition, R a is the most used parameter for general quality control [10].The steel used in the turning process of this study was AISI 12L14 carbon steel (used in studies such as: Peruchi et al.

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“…Optimization of multi-pass turning operations using ACO was also attempted by Vijayakumar et al [7]. The turning parameters were determined by minimizing the unit production cost, subject to various practical machining constraints.…”

Section: Introductionmentioning

confidence: 99%

An Optimization Approach for Production Time Minimization in Longitudinal Turning

Madić¹,

Radovanović²,

Kovačević³

2017

J. Prod. Eng.

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Optimization of multi-pass turning operations using ant colony system

Cited by 113 publications

References 13 publications

Optimization of Cutting Conditions in Multi-Pass Turning Using Hybrid Genetic Algorithm-Sequential Quadratic Programming

Optimization of Cutting Conditions in Multi-Pass Turning Using Hybrid Genetic Algorithm-Sequential Quadratic Programming

A Weighted Mean Square Error Approach to the Robust Optimization of the Surface Roughness in an AISI 12L14 Free-Machining Steel-Turning Process

An Optimization Approach for Production Time Minimization in Longitudinal Turning

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