Prediction of minimum surface roughness in end milling mold parts using neural network and genetic algorithm

Öktem, Hasan; Erzurumlu, Tuncay; Erzincanlı, Fehmi

doi:10.1016/j.matdes.2005.01.010

Cited by 215 publications

(100 citation statements)

References 11 publications

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Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

“…The chromosomes are then evaluated employing a certain fitness criteria and the best ones are kept while the others are discarded. This process repeats until one chromosome has the best fitness and is taken as the optimum solution of the problem [16].The computational procedure involved in maximizing the fitness function 1. Choose a suitable string length l nq = to represent the n design variables of the design vector X .…”

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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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Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

mentioning

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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“…However, other authors consider the mapping function selection an important factor for ANN models. For example, Oktem, Erzurumlu and Erzincanli (2006) tested in their work that tangent hyperbolic function leads to a minimum training error for the prediction of surface roughness.…”

Section: Guidelines For Ann Modellingmentioning

confidence: 99%

A review of artificial intelligent approaches applied to part accuracy prediction

Abellán-Nebot

2010

IJMMM

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Nowadays, despite the large volume of worldwide academic research on various aspects of metal cutting the control of workpiece precision still relies on machine-tool operator's experience and trial and error runs. In order to increase the efficiency of machining systems, many empirical models based on Artificial Intelligent (AI) approaches have been proposed in the past, where important process improvements were reported. This paper overviews the AI approaches applied in machining operations to predict part accuracy in terms of dimensional deviations and surface roughness. Successful techniques applied in this field such as Artificial Neural Networks, Fuzzy Logic, Adaptive-Network-based Fuzzy Inference Systems and Bayesian Networks are briefly reviewed and compared to facilitate its use. For each AI approach, the most relevant research works are described and based on those works some guidelines are proposed for its implementation. In addition, advantages and drawbacks of each approach are summarised and a generic guideline for AI approaches selection is proposed.

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“…The limit value of Ra specified in the product design documentation must be attained, nevertheless tool wear may hamper these efforts at certain stages of production. In the event of such an occurrence, constant monitoring of the state of machined surfaces [3,4,5] enables spotting the defect and applying the most sensible solutioncutting tool change. However, this will inevitably lead to halting the production process and generate delay of machining time of a given lot of machined products.…”

Section: Introductionmentioning

confidence: 99%

Optimisation of milling parameters using neural network

Lipski

Zaleski

2017

ITM Web Conf.

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Abstract. The purpose of this study was to design and test an intelligent computer software developed with the purpose of increasing average productivity of milling not compromising the design features of the final product. The developed system generates optimal milling parameters based on the extent of tool wear. The introduced optimisation algorithm employs a multilayer model of a milling process developed in the artificial neural network. The input parameters for model training are the following: cutting speed vc, feed per tooth fz and the degree of tool wear measured by means of localised flank wear (VB3). The output parameter is the surface roughness of a machined surface Ra. Since the model in the neural network exhibits good approximation of functional relationships, it was applied to determine optimal milling parameters in changeable tool wear conditions (VB3) and stabilisation of surface roughness parameter Ra. Our solution enables constant control over surface roughness parameters and productivity of milling process after each assessment of tool condition. The recommended parameters, i.e. those which applied in milling ensure desired surface roughness and maximal productivity, are selected from all the parameters generated by the model. The developed software may constitute an expert system supporting a milling machine operator. In addition, the application may be installed on a mobile device (smartphone), connected to a tool wear diagnostics instrument and the machine tool controller in order to supply updated optimal parameters of milling. The presented solution facilitates tool life optimisation and decreasing tool change costs, particularly during prolonged operation.

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Prediction of minimum surface roughness in end milling mold parts using neural network and genetic algorithm

Cited by 215 publications

References 11 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 review of artificial intelligent approaches applied to part accuracy prediction

Optimisation of milling parameters using neural network

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