The optimal cutting-parameter selection of production cost in HSM for SKD61 tool steels

Juan, Hu; Shu, Yu; Lee, B.Y.

doi:10.1016/s0890-6955(03)00038-5

Cited by 65 publications

(36 citation statements)

References 12 publications

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“…[1] established the model of tool wear and time variable, tool wear experiment was carried out by stainless steel (SS410), determined tool wear empirical formula by multiple linear regression. The United States, Germany and Sweden and other countries except for specific milling process parameters optimization [2,3], has formed the adaptability good cutting database. However, tool performance has failed to be fully played in the process of high speed milling of 3Cr2Mo steel mold, because there is little quantitative description of the method about the law of tool wear.…”

Section: Introductionmentioning

confidence: 99%

Tool Wear Research And Milling Parameter Optimization During High-Speed Milling 3Cr2Mo

Sun¹,

Chen²,

Jiao³

et al. 2017

dtcse

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This paper focuses on the copy measurement to measure radial wear of ball end mill. Then, the model of tool wear was established through tool wear test. The results of experiment show that the model is of higher precision and is useful to predict the amount of wear. Finally, a milling process optimization model was proposed by maximizing efficiency and minimizing cost targets. In addition, we aimed to get the reasonable high speed milling parameters by choosing nondominated sorting genetic algorithm II (NSGA-II) to solve the model.

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Section: Introductionmentioning

confidence: 99%

Tool Wear Research And Milling Parameter Optimization During High-Speed Milling 3Cr2Mo

Sun¹,

Chen²,

Jiao³

et al. 2017

dtcse

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“…TANDON established optimization model of cutting parameter with the highest production efficiency [2]. H. Juan made research on the relationships between cutting parameter (cutting speed, feed per tooth, and axial depth of cut) and tool life based on a polynomial network [3]. Reference [4] studied the relationship between material and cutting parameter.…”

Section: Introductionmentioning

confidence: 99%

Cutting Parameter Optimization Based on Experience and Rules

Luo¹,

Zhao²,

Luo³

et al. 2014

TOMEJ

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Abstract:To reduce cutting time and costs, an optimized model of cutting parameter is proposed. Constraints like lathe and cutting tool are fully considered. The objective function is aiming at the target of profit margins. Some empirical indexes are used in the constraints and the objective function since the special properties of cutting process. The Fuzzy Comprehensive Evaluation Method is used to build an evaluation model, which provides an evolutionary path for cutting parameter. The evaluation matrix R in the model is determined by operators who use the cutting parameter to cut. A cutting parameter database system of B/S structure was developed by JAVA language based on the optimization model and helped a company improved profit margins successfully.

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“…Second step provides optimal or near-optimal solution(s) to the overall optimisation problem formulated through the previous mathematical models. In the literature, the main optimisation tools and techniques applied are based on Taguchi method (Ghani, Choudhury and Hassan, 2004;Zhang, Chen and Kirby, 2007), response surface design (Suresh, Rao and Deshmukh, 2002), genetic algorithms (Liu, Zuo and Wang, 1999b;Suresh, Rao and Deshmukh, 2002;Cus and Balic, 2003;Chien and Chou, 2001) and simulated annealing (Juan, Yu and Lee, 2003).…”

mentioning

confidence: 99%

“…Ip, Lau and Chan (2003) applied fuzzy sets to optimise the material removal rate in the manufacturing of sculptured surfaces and they demonstrated a material removal rate increase of 41% in comparison with conventional constant feedrate. Juan, Yu and Lee (2003) applied polynomial networks to model a roughing milling operation, and the production cost was minimised using a simulated annealing method. Table 1 summaries recent research works related to cutting parameter optimisation.…”

mentioning

confidence: 99%

“…Table 1 summaries recent research works related to cutting parameter optimisation. (Liu and Wang, 1999a) Feed-forward NN ALM NN MRR M (Chiang et al, 1995) Feed-forward NN ALM NN MRR EM (Liu et al, 1999b) Feed-forward NN GA MRR M (Ghani et al, 2004) -TA CF, Ra EM (Zhang et al, 2007) -TA Ra EM (Suresh et al, 2002) Response Surface GA Ra T (Cus and Balic, 2003) Statistical Regressions GA Unit cost, Ra T Time (Zuperl and Cus, 2003) Feed-forward and LSO MRR, Tl T Radial Basis NN Ra (Chien and Chou, 2001) Feed-forward NN GA MRR T (Ip et al, 2003) Fuzzy Sets -MRR M (Juan et al, 2003) Polynomial Table 1), ANFIS models let understand the process through simple rules extracted from experimental data, so the expert machinist is able to learn new knowledge from a particular machining operation and confirm well-known machining practices/rules. The comparison is conducted in a finishing face-milling operation with Cubic Boron Nitride (CBN) cutting tools, which are modern and expensive cutting tools.…”

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confidence: 99%

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Cutting parameters optimisation in milling: expert machinist knowledge vs. soft computing methods

Abellán-Nebot

2010

IJMMS

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In traditional machining operations, cutting parameters are usually selected prior to machining according to machining handbooks and user's experience. However, this method tends to be conservative and sub-optimal since part accuracy and non machining failures prevail over machining process efficiency. In this paper, a comparison between traditional cutting parameter optimisation by an expert machinist and an experimental optimisation procedure based on Soft Computing methods is conducted. The optimisation procedure presented is composed of two steps: (1) modelling the process variables of interest and (2) optimising a multi-objective function to reach a trade-off among production rate, cutting costs and part accuracy. The first step applies an Adaptive Neuro-fuzzy Inference System (ANFIS) to model the process by extracting rules from experimental data. The second step applies Genetic Algorithms to optimise the multi-objective function which is defined by an overall desirability function approach. The proposed methodology increased the machining performance in 6.1% and improved the understanding of the machining operation through the ANFIS models.

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The optimal cutting-parameter selection of production cost in HSM for SKD61 tool steels

Cited by 65 publications

References 12 publications

Tool Wear Research And Milling Parameter Optimization During High-Speed Milling 3Cr2Mo

Tool Wear Research And Milling Parameter Optimization During High-Speed Milling 3Cr2Mo

Cutting Parameter Optimization Based on Experience and Rules

Cutting parameters optimisation in milling: expert machinist knowledge vs. soft computing methods

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