Multi-objective optimization of machining parameters considering energy consumption

Wang, Qiulian; Liu, Fei; Wang, Xianglian

doi:10.1007/s00170-013-5547-z

Cited by 116 publications

(50 citation statements)

References 22 publications

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“…The power consumed during the cutting power, P cutting , as shown in Fig. 2a, consists of P au , P u , P removal and P ad (Wang et al 2013) as follows. P cutting = P au + P u + P removal + P ad (11)…”

Section: Temporal Characteristics Of Power Consumption In Cnc Millingmentioning

confidence: 99%

A comprehensive approach to parameters optimization of energy-aware CNC milling

Tang

et al. 2016

J Intell Manuf

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Cutting parameters are important components in the process of computer numerical control (CNC) machining, and reasonable choice of cutting parameters can significantly affect the energy efficiency. This paper presents a multi-objective parameter optimization method for energy efficiency in CNC milling process. Firstly, the energy consumption composition characteristics and temporal characteristics in CNC milling are analyzed, respectively. The energy model of CNC milling is then established, of which the correlation coefficient is obtained through nonlinear regression fitting. Then a multi-objective optimization model is proposed to take the highest energy efficiency and the minimum production time as the optimization objectives, which is solved based on Tabu search algorithm. Finally, a case study is conducted to validate the proposed multi-objective optimization model and the optimal parameter solutions of maximum energy efficiency and minimum production time is obtained. Moreover, the parametric influence on specific energy consumption and production time are explicitly analyzed. The experiment results show that cutting depth and width are the most influential parameters for specific energy consumption, and spindle speed ranks the first for the production time.

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Section: Temporal Characteristics Of Power Consumption In Cnc Millingmentioning

confidence: 99%

A comprehensive approach to parameters optimization of energy-aware CNC milling

Tang

et al. 2016

J Intell Manuf

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“…The area of material removed (AMR) in cutting each shoulder is 1492.3mm 2 [= π{ (50) 2 (50 5) 2 }] and for three shoulders is 4477mm 2 . The total volume of material removed by each insert of 2, 3, and 4mm width is 8954 (= 2 × 4477), 13431, and 17908 mm 3 , respectively. The flank wear was measured, using 10× toolmaker's microscope, after cutting each of the three assigned shoulders.…”

Section: Experimental Workmentioning

confidence: 99%

“…Diaz et al have compared demand of power in micromachining of steel with those of polycarbonate and aluminum at various productivity levels [2]. The authors in another work have presented a multi-objective analysis of energy consumption, process cost, and work surface roughness of a machining process based on optimization of cutting speed, feed rate, and depth of cut [3]. In a recent work, a comparison between tool life, process cost, productivity, and specific energy consumption has been provided [4].…”

Section: Introductionmentioning

confidence: 99%

Revamping Grooving Process for Sustainability using Fuzzy Expert System

Iqba

2016

MATEC Web of Conferences

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Abstract. The article presents an application of a fuzzy expert system for renovating a metal cutting process to cope with the sustainability requirements. The work seeks a sustainable balance between energy consumption, productivity and tool damage. Cylindrical grooving experiments were performed to generate data related to quantification of the effects of material hardness, cutting speed, width of cut and feed rate on the aforementioned sustainability measures. A fuzzy knowledge-base was developed that suggests the most suitable adjustments of the controlled variables that would lead to achievement of various combinations of the objectives.

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“…If too large it would affect the efficiency of evolution. According to literature (Wang et al, 2014), the population size generally ranges from dozens to hundreds. Note that there is a variety of encoding schemas whose choice is contingent on application features.…”

Section: Population Size and Coding Choicesmentioning

confidence: 99%