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Advances in Manufacturing ›› 2017, Vol. 5 ›› Issue (3): 231-242.doi: 10.1007/s40436-017-0187-4

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Optimization of fused deposition modeling process parameters using a fuzzy inference system coupled with Taguchi philosophy

Saroj Kumar Padhi1, Ranjeet Kumar Sahu2, S. S. Mahapatra3, Harish Chandra Das1, Anoop Kumar Sood4, Brundaban Patro5, A. K. Mondal3   

  1. 1 Department of Mechanical Engineering, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan University, Bhubaneswar 751030, India;
    2 Department of Mechanical Engineering, SRM University, Kattankulathur 603203, India;
    3 Department of Mechanical Engineering, National Institute of Technology, Rourkela 769008, India;
    4 Department of Manufacturing Engineering, National Institute of Foundry and Forging Technology, Ranchi 834003, India;
    5 Department of Mechanical Engineering, National Institute of Technology, Warangal 506004, India
  • 收稿日期:2016-10-28 修回日期:2017-07-22 出版日期:2017-09-25 发布日期:2017-09-25
  • 通讯作者: Saroj Kumar Padhi,E-mail:padhisaroj1@gmail.com E-mail:padhisaroj1@gmail.com

Optimization of fused deposition modeling process parameters using a fuzzy inference system coupled with Taguchi philosophy

Saroj Kumar Padhi1, Ranjeet Kumar Sahu2, S. S. Mahapatra3, Harish Chandra Das1, Anoop Kumar Sood4, Brundaban Patro5, A. K. Mondal3   

  1. 1 Department of Mechanical Engineering, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan University, Bhubaneswar 751030, India;
    2 Department of Mechanical Engineering, SRM University, Kattankulathur 603203, India;
    3 Department of Mechanical Engineering, National Institute of Technology, Rourkela 769008, India;
    4 Department of Manufacturing Engineering, National Institute of Foundry and Forging Technology, Ranchi 834003, India;
    5 Department of Mechanical Engineering, National Institute of Technology, Warangal 506004, India
  • Received:2016-10-28 Revised:2017-07-22 Online:2017-09-25 Published:2017-09-25
  • Contact: Saroj Kumar Padhi,E-mail:padhisaroj1@gmail.com E-mail:padhisaroj1@gmail.com

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摘要：

Fused deposition modeling (FDM) is an additive manufacturing technique used to fabricate intricate parts in 3D, within the shortest possible time without using tools, dies, fixtures, or human intervention. This article empirically reports the effects of the process parameters, i.e., the layer thickness, raster angle, raster width, air gap, part orientation, and their interactions on the accuracy of the length, width, and thickness, of acrylonitrile-butadienestyrene (ABSP 400) parts fabricated using the FDM technique. It was found that contraction prevailed along the direction of the length and width, whereas the thickness increased from the desired value of the fabricated part. Optimum parameter settings to minimize the responses, such as the change in length, width, and thickness of the test specimen, have been determined using Taguchi's parameter design. Because Taguchi's philosophy fails to obtain uniform optimal factor settings for each response, in this study, a fuzzy inference system combined with the Taguchi philosophy has been adopted to generate a single response from three responses, to reach the specific target values with the overall optimum factor level settings. Further, Taguchi and artificial neural network predictive models are also presented in this study for an accuracy evaluation within the dimensions of the FDM fabricated parts, subjected to various operating conditions. The predicted values obtained from both models are in good agreement with the values from the experiment data, with mean absolute percentage errors of 3.16 and 0.15, respectively. Finally, the confirmatory test results showed an improvement in the multi-response performance index of 0.454 when using the optimal FDM parameters over the initial values.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-017-0187-4

关键词: Fused deposition modeling (FDM), Dimensional accuracy, Fuzzy logic, Performance characteristic, Multi-response performance index (MRPI), Artificial neural network (ANN)

Abstract:

Fused deposition modeling (FDM) is an additive manufacturing technique used to fabricate intricate parts in 3D, within the shortest possible time without using tools, dies, fixtures, or human intervention. This article empirically reports the effects of the process parameters, i.e., the layer thickness, raster angle, raster width, air gap, part orientation, and their interactions on the accuracy of the length, width, and thickness, of acrylonitrile-butadienestyrene (ABSP 400) parts fabricated using the FDM technique. It was found that contraction prevailed along the direction of the length and width, whereas the thickness increased from the desired value of the fabricated part. Optimum parameter settings to minimize the responses, such as the change in length, width, and thickness of the test specimen, have been determined using Taguchi's parameter design. Because Taguchi's philosophy fails to obtain uniform optimal factor settings for each response, in this study, a fuzzy inference system combined with the Taguchi philosophy has been adopted to generate a single response from three responses, to reach the specific target values with the overall optimum factor level settings. Further, Taguchi and artificial neural network predictive models are also presented in this study for an accuracy evaluation within the dimensions of the FDM fabricated parts, subjected to various operating conditions. The predicted values obtained from both models are in good agreement with the values from the experiment data, with mean absolute percentage errors of 3.16 and 0.15, respectively. Finally, the confirmatory test results showed an improvement in the multi-response performance index of 0.454 when using the optimal FDM parameters over the initial values.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-017-0187-4

Key words: Fused deposition modeling (FDM), Dimensional accuracy, Fuzzy logic, Performance characteristic, Multi-response performance index (MRPI), Artificial neural network (ANN)

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