Evolutionary techniques in optimizing machining parameters: Review and recent applications (2007–2011)

Nowadays, the miniaturization of many consumer products is extending the use of micro-milling operations with high quality requirements. However, the impacts of cutting-tool wear on part dimensions, form and surface integrity are not negligible and part quality assurance for a minimum production cost is a challenging task. In fact, industrial practices usually set conservative cutting parameters and early cutting replacement policies in order to minimize the impact of cutting-tool wear on part quality. Although these practices may ensure part integrity, the production cost is far away to be minimized, especially in highly tool-consuming operations like mold and die micromanufacturing. In this paper, an Adaptive Control Optimization (ACO) system is proposed to estimate cutting-tool wear in terms of part quality and adapt the cutting conditions accordingly in order to minimize the production cost, ensuring quality specifications in hardened steel micro-parts. The ACO system is based on: i) a monitoring sensor system composed of a dynamometer; ii) an estimation module with Artificial Neural Networks models; iii) an optimization module with evolutionary optimization algorithms; and iv) a CNC interface module. In order to operate in a nearly real time basis and facilitate the implementation of the ACO system, different evolutionary optimization algorithms are evaluated such as Particle Swarm Optimization (PSO), Genetic Algorithms (GA) and Simulated Annealing (SA) in terms of accuracy, precision and robustness. The results for a given micro-milling operation showed that PSO algorithm performs better than GA and SA algorithms under computing time constraints. Furthermore, the implementation of the final ACO system reported a decrease in the production cost of 12.3% and 29% in comparison with conservative and high production strategies, respectively.

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“…An extensive review of the optimization techniques used in machining can be found in [39] and [40]. In …”

Section: Optimization Modulementioning

confidence: 99%

Adaptive control optimization in micro-milling of hardened steels—evaluation of optimization approaches

Coppel

Abellán-Nebot

Siller

et al. 2015

Int J Adv Manuf Technol

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“…Surface roughness is considered as one of the most important performances in machining process that refers to the product quality [2,3]. According to Keskin et al [4], surface roughness is the most measured performance in EDM compared with other performances such as tool wear and material removal rate [5]. In most cases, in the industries, surface roughness is a technical requirement needed for mechanical products.…”

Section: Introductionmentioning

confidence: 99%

Experimental study of surface roughness in Electric Discharge Machining (EDM) based on Grey Relational Analysis

Deris¹,

Zain²,

Sallehuddin³

et al. 2016

MATEC Web Conf.

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Abstract. Electric Discharge Machining (EDM) is one of the modern machining which is capable in handling hard and difficult-to-machine material. The successful of EDM basically depends on its performances such as surface roughness (Ra), material removal rate (MRR), electrode wear rate (EWR) and dimensional accuracy (DA). Ra is considered as the most important performance due to it role as a technological quality measurement for a product and also a factor that significantly affects the manufacturing process. This paper presents the experimental study of surface roughness in die sinking EDM using stainless steel SS316L with copper impregnated graphite electrode. The machining experimental is conducted based on the two levels full factorial design of design of experiment (DOE) with five machining parameters which are peak current, servo voltage, servo speed, pulse on time and pulse off time. The results were analyzed using grey relational analysis (GRA) and it was found that pulse on time and servo voltage give the most influence to the Ra value.

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“…Surface roughness, R a is an important aspect in evaluating the quality of products. R a is widely used as an index of product quality and in most cases a technical requirement for mechanical products [3,12]. Normally, R a value is influenced by many factors such as machining parameters, cutting phenomena, workpiece properties and cutting tool properties [15,16].…”

Section: Introductionmentioning

confidence: 99%

An experimental result of surface roughness machining performance in deep hole drilling

et al. 2016

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Abstract. This study presents an experimental result of a deep hole drilling process for Steel material at different machining parameters which are feed rate (f), spindle speed (s), the depth of the hole (d) and MQL, number of drops (m) on surface roughness, R a . The experiment was designed using two level full factorial design of experiment (DoE) with centre points to collect surface roughness, R a values. The signal to noise (S/N) ratio analysis was used to discover the optimum level for each machining parameters in the experiment.

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Evolutionary techniques in optimizing machining parameters: Review and recent applications (2007–2011)

Cited by 228 publications

References 77 publications

Adaptive control optimization in micro-milling of hardened steels—evaluation of optimization approaches

Adaptive control optimization in micro-milling of hardened steels—evaluation of optimization approaches

Experimental study of surface roughness in Electric Discharge Machining (EDM) based on Grey Relational Analysis

An experimental result of surface roughness machining performance in deep hole drilling

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