A framework for simulation-based multi-objective optimization and knowledge discovery of machining process

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

doi:10.1007/s00170-018-2360-8

Cited by 16 publications

(9 citation statements)

References 40 publications

(34 reference statements)

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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%

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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Section: Proposed Modified Spea2 (M-spea2)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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“…The large variation of computational time is caused by different variable combinations for each simulation. Thus, by considering the capability of running several simulations at the same time within the developed framework in Amouzgar et al (2018a), the computation time for obtaining the responses of the 100 DoEs, used for constructing the metamodel, was less than 48 hours.…”

Section: Mesh Convergencementioning

confidence: 99%

“…A simulation-based MOO on the same problem with identical settings was carried out to find the Pareto-optimal front in a recent study by Amouzgar et al (2018a). In that study, the optimization algorithm converged after 17 generations.…”

Section: Comparison Of Simulation-moo and Metamodel-moomentioning

confidence: 99%

“…In the present research work, the intention is to fill this gap by analysing the performance and advantages of a metamodel-based MOO of a turning operation in comparison with an FE simulationbased MOO of the same operation. A recent study by Amouzgar et al (2018a) developed a new framework for the automated MOO of a machining process based on FE simulations. The framework was demonstrated by optimizing a metal cutting process in turning AISI-1045, using an uncoated K10 tungsten carbide tool.…”

Section: Introductionmentioning

confidence: 99%

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

Amouzgar

Bandaru

Andersson

et al. 2019

Engineering Optimization

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This study investigates the advantages and potentials of the metamodelbased multi-objective optimization (MOO) of a turning operation through the application of finite element simulations and evolutionary algorithms to a metal cutting process. The objectives are minimizing the interface temperature and tool wear depth obtained from FE simulations using DEFORM-2D software, and maximizing the material removal rate. Tool geometry and process parameters are considered as the input variables. Seven metamodelling methods are employed and evaluated, based on accuracy and suitability. Radial basis functions with a priori bias and Kriging are chosen to model tool-chip interface temperature and tool wear depth, respectively. The non-dominated solutions are found using the strength Pareto evolutionary algorithm SPEA2 and compared with the non-dominated front obtained from pure simulation-based MOO. The metamodel-based MOO method is not only advantageous in terms of reducing the computational time by 70%, but is also able to discover 31 new non-dominated solutions over simulation-based MOO. ARTICLE HISTORY

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“…Innovative design principles obtained in [17] are highly motivating for design engineers and practitioners to further apply it to more complex optimization problems. In literature, few other researchers had applied innovization to real world optimization problem [19][20][21][22][23].…”

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confidence: 99%

Exploring Innovative Design Principles using Innovization in Multi- Objective Optimal Reactive Power Dispatch Problem

Shah*,

Chatterjee

2019

IJEAT

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Recently, researchers in the field of Evolutionary Multi-Objective Optimization give a systematic approach for exploring innovative design principles in a conflicting multi-objective optimization problem by analyzing pareto optimal solution using either manual or automated approach. They call it “Innovization” and defined as: “innovation through optimization”. This paper applies manual innovization to multi-objective Optimal Reactive Power Dispatch Problem using Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) as multi-objective optimization algorithm and manually searches for all possible innovative design principles by analyzing multiple pareto optimal solutions. Standard IEEE 30 bus test system is considered for the current work. Simulation results reveal number of innovative design principles. Innovative design principles includes function approximation of relationship between conflicting objectives, characteristics of decision variable with respect to different objectives and actual range of decision variables. Simulation results clearly show much faster convergence when decision variables were obtained using innovative design principles for specified desired objectives as compared to normal case. Results also show some decision variables can be eliminated by setting it to a fixed value, which leads to simplification of optimization problem as the values of these variables remains constant with respect to the values of objective function.

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A framework for simulation-based multi-objective optimization and knowledge discovery of machining process

Cited by 16 publications

References 40 publications

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

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

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

Exploring Innovative Design Principles using Innovization in Multi- Objective Optimal Reactive Power Dispatch Problem

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