Multi-response design of Nd:YAG laser drilling of Ni-based superalloy sheets using Taguchi’s quality loss function, multivariate statistical methods and artificial intelligence

Šibalija, Tatjana V.; Petronić, Sanja; Majstorovic, Vidosav D.; Prokić-Cvetković, Radica; Milosavljević, Aleksandra

doi:10.1007/s00170-010-2945-3

Cited by 55 publications

(18 citation statements)

References 35 publications

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“…In this paper, we propose an integrated and generic approach for robust multiresponse process optimisation based on SA algorithm that extends our previous research [36][37][38][39] in terms of the improvement of the quality of a final solution obtained by the method, the robustness of the method and a feasibility of its practical application. The intention was to develop a generic framework that could be applied to various static multiresponse optimisation problems, disregarding the type of process, its parameters and/or responses and their mutual interrelations.…”

Section: Introductionmentioning

confidence: 71%

“…This research presents an extension of our previously published methodology for multiresponse process parameter optimisation [36][37][38][39] in terms of the improvement of the quality of a final solution, the robustness of a method and feasibility of application. The presented methodology consists of two stages.…”

Section: Resultsmentioning

confidence: 99%

“…The presented intelligent approach was previously based on GA application [37] to search for the optimal solution in a continual space, and it found better solution than the statistical part of the approach. The application of SA in the proposed method resulted in an equal solution (SPM value of 0.75230 and corresponding optimal parameter conditions) as the application of GA.…”

Section: Discussionmentioning

confidence: 99%

“…At first, within this method, GA was employed to find the global optimum. A GA-based intelligent method outperformed the statistical method due to a search for the global optimal solution through a continual multidimensional space [36][37][38][39]. For the purpose of further improvement of the developed method, the SA algorithm was employed in this paper instead of GA. Based on the analysis of four case studies (Tables 2, 4, 6 and 8), the results could be summarised as follows:…”

Section: Discussionmentioning

confidence: 99%

“…However, the other tested crossover and selection function types resulted in a significantly lower SPM and different optimal parameter conditions [37]. It could be said that the selection of GA operator types significantly affects the quality of the final outcome in this case study.…”

Section: Case Studymentioning

confidence: 98%

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An integrated simulated annealing-based method for robust multiresponse process optimisation

Šibalija

Majstorovic

2011

Int J Adv Manuf Technol

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This paper presents a generic optimisation methodology for the selection of optimal process parameters in multiresponse processes that consists of two stages: The first stage is based on a quality loss function and multivariate statistical methods in order to adequately present responses, uncorrelate and synthesise them into a single process performance measure; the second stage uses artificial neural networks to build a process model and a simulated annealing (SA) algorithm to find the optimal process parameter conditions. The initial point of the SA algorithm is generated in such a way as to improve the convergence to the actual global optimum. The most important SA algorithm parameters are varied to assess the algorithm's robustness in terms of the effect of the algorithm setting on the quality of the final solution (optimal process parameters and the corresponding process performance measure). The results are benchmarked to genetic algorithm (GA) performance within the proposed methodology, and the quality of a final solution, the robustness of the optimisation algorithm, the speed of a convergence to the optimum and the computational time are evaluated. Four case studies are presented to illustrate the effectiveness of the proposed methodology in comparison to several commonly used approaches from the literature and also to the GA-based performance.

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Section: Introductionmentioning

confidence: 71%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Case Studymentioning

confidence: 98%

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An integrated simulated annealing-based method for robust multiresponse process optimisation

Šibalija

Majstorovic

2011

Int J Adv Manuf Technol

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Quality loss prediction of mechanical system life considering linear degeneration and random error

Qian

Liu

Mao

et al. 2020

Quality & Reliability Eng

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To accurately predict quality loss of high reliability and long‐life product in service, a dynamic quality characteristics model is established based on linear degeneration and random error. According to this model, the expectation and variance of dynamic quality characteristics can be figured out. Moreover, assuming that the quality characteristics follow the normal distribution at the initial stage, the expectation of quality loss and the life distribution of dynamic quality characteristics follow three types are derived, such as L‐type (Larger is better), S‐type (Smaller is better), and N‐type (Nominal is better). According to the discount theory of quality loss, the present value model of dynamic quality loss based on the life is derived by combining life and the present value of dynamic quality loss. Then, this model is used to evaluate the quality loss of a GaAs laser and the rationality of the model is analyzed.

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Intelligent Modelling of Hard Materials Machining

Patel

Chate

Parappagoudar³

et al. 2020

Machining of Hard Materials

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Multi-response design of Nd:YAG laser drilling of Ni-based superalloy sheets using Taguchi’s quality loss function, multivariate statistical methods and artificial intelligence

Cited by 55 publications

References 35 publications

An integrated simulated annealing-based method for robust multiresponse process optimisation

An integrated simulated annealing-based method for robust multiresponse process optimisation

Quality loss prediction of mechanical system life considering linear degeneration and random error

Intelligent Modelling of Hard Materials Machining

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