Metamodel-based multi-objective optimization of a turning process by using finite element simulation

Amouzgar, Kaveh; Bandaru, Sunith; Andersson, Tobias; Ng, Amos H. C.

doi:10.1080/0305215x.2019.1639050

Cited by 14 publications

(9 citation statements)

References 37 publications

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“…Productivity was maximized, guaranteeing surface roughness values according to the ISO 7206-2 standard. The approach uses a new generation genetic algorithm design, in contrast to similar studies [5,19,20,27].…”

Section: Discussionmentioning

confidence: 99%

“…However, this singularity has received less attention compared to the other two regions of the wear curve [17,18]. At the same time, to evaluate this initial wear of the tool, it is necessary to apply multi-objective optimization [19] of the parameters of the cutting regime [20] in order to reduce its effects. To solve multi-objective optimization problems, a large number of tools have been developed [21].…”

Section: Introductionmentioning

confidence: 99%

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Optimization of the Cutting Regime in the Turning of the AISI 316L Steel for Biomedical Purposes Based on the Initial Progression of Tool Wear

Risco-Alfonso

Pérez‐Rodríguez

Zambrano‐Robledo

et al. 2021

Metals

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The development of biomedical devices has improved the quality of life for millions of people. The increase in life expectancy generates an increase in the demand for these devices. One of the most used materials for these purposes is 316 L austenitic stainless steel due to its mechanical properties and good biocompatibility. The objective of the present investigation was to identify the dependence between the main cutting force, the initial speed of the tool wear, the surface roughness, and the parameters of the cutting regime. Based on these dependencies, a multi-objective optimization model is proposed to minimize the energy consumed and initial wear rate, as well as to maximize productivity, maintaining the surface roughness values below those established by the ISO 5832-1 standard. The wear of the cutting tool was measured on a scanning electron microscope. For the optimization process, a genetic algorithm based on NSGA-II (Non-nominated Sorting Genetic Algorithm) was implemented. The input variables were the cutting speed and the feed in three levels. The cutting force and surface roughness were set as restrictions. It is concluded that the mathematical model allows for the optimization of the cutting regime during dry turning and with the use of MQL (Minimum Quantity Lubrication) with BIDEMICS JX1 ceramic tools (NTK Cutting Tools, Wixom, MI, USA), of AISI 316 L steel for biomedical purposes. Pareto sets and boundaries allow for choosing the most appropriate solution according to the specific conditions of the workshop where it is applied, minimizing the initial progression of tool wear and energy consumed, and maximizing productivity by guaranteeing the surface roughness values established for these types of parts according to the standard.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimization of the Cutting Regime in the Turning of the AISI 316L Steel for Biomedical Purposes Based on the Initial Progression of Tool Wear

Risco-Alfonso

Pérez‐Rodríguez

Zambrano‐Robledo

et al. 2021

Metals

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“…In this study, we modified the implementation of the well-known (SPEA2), which has been previously used to optimise real-world engineering problems (Rezaei and Davoodi 2012;Amouzgar, Rashid, and Stromberg 2013;Tang et al 2016;Amouzgar et al 2018;Rao et al 2019;Amouzgar et al 2019). Figure 5 describes the SPEA2 process workflow.…”

Section: Proposed Modified Spea2 (M-spea2)mentioning

confidence: 99%

“…Hypervolume for minimisation of MOOP considered in this study with Nadir point as referenceAmouzgar et al (2019).…”

mentioning

confidence: 99%

Multi-objective optimisation of tool indexing problem: a mathematical model and a modified genetic algorithm

Amouzgar

Nourmohammadi

2021

International Journal of Production Research

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Machining process efficiencies can be improved by minimising the non-machining time, thereby resulting in short operation cycles. In automatic-machining centres, this is realised via optimum cutting tool allocation on turret-magazine indices-the "tool-indexing problem". Extant literature simplifies TIP as a single-objective optimisation problem by considering minimisation of only the tool-indexing time. In contrast, this study aims to address the multi-objective optimisation toolindexing problem (MOOTIP) by identifying changes that must be made to current industrial settings as an additional objective. Furthermore, tool duplicates and lifespan have been considered. In addition, a novel mathematical model is proposed for solving MOOTIP. Given the complexity of the problem, the authors suggest the use of a modified strength Pareto evolutionary algorithm combined with a customised environment-selection mechanism. The proposed approach attained a uniform distribution of solutions to realise the above objectives. Additionally, a customised solution representation was developed along with corresponding genetic operators to ensure the feasibility of solutions obtained. Results obtained in this study demonstrate the realization of not only a significant (70%) reduction in non-machining time but also a set of tradeoff solutions for decision makers to manage their tools more efficiently compared to current practices.

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“…It was concluded the surface roughness was significantly affected by cutting speed while the depth of cut had a predominant effect on the tool wear and feed rate significantly impacted the material removal rate. Amouzgar et al [9] simulated a turning operation using finite element method and then applied evolutionary algorithms to minimize the interface temperature and tool wear depth. They found that the metamodel-based method reduced the computational time by 70%.…”

Section: Introductionmentioning

confidence: 99%

Modelling and Optimization of Surface Roughness and Specific Tool Wear in Milling Process

2021

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The present study has been carried out to optimize three machining parameters in the milling process to achieve minimum surface roughness and tool wear along with the maximum material removal rate. A specific tool wear factor has been defined to evaluate both tool wear and material removal rate parameters simultaneously and the surface roughness was considered as the second output parameter. A set of experiments was designed using a DOE technique and conducted on a milling machine. The experimental data then was applied to develop different mathematical models and the best model was chosen based on analysis of variance (ANOVA). Three proposed methods of optimization with different natures were used to determine optimal output parameters based on selected models. The comparison between these methods showed that Regression-response optimization was superior to Simulated Annealing (SA) algorithm and Goal-attainment method. The Simulated Annealing (SA) algorithm also represented less error function compared to goal-attainment methods. The results of optimization revealed that optimum values for cutting speed and feed rate were ranged from 312 to 314 m/min and 0.085 to 0.12 mm/rev⸱tooth, respectively, while all optimization methods reached the same value of 1.0 mm for depth of cut parameter.

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Metamodel-based multi-objective optimization of a turning process by using finite element simulation

Cited by 14 publications

References 37 publications

Optimization of the Cutting Regime in the Turning of the AISI 316L Steel for Biomedical Purposes Based on the Initial Progression of Tool Wear

Optimization of the Cutting Regime in the Turning of the AISI 316L Steel for Biomedical Purposes Based on the Initial Progression of Tool Wear

Multi-objective optimisation of tool indexing problem: a mathematical model and a modified genetic algorithm

Modelling and Optimization of Surface Roughness and Specific Tool Wear in Milling Process

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