Multi Response Optimisation of Injection Moulding Process Parameter Using Taguchi and Desirability Function

Multi Response Optimisation of Injection Moulding Process Parameter Using Taguchi and Desirability Function

In this study, the optimum injection molding process parameter of warehouse plastic pallets is identified. Compressive strength and part weight are the selected quality characteristic. Barrel temperature, injection speed and holding pressure are the selected process parameter. Taguchi optimization m...

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Main Authors: Vivekanandan, Panneerselvam, Faiz, Mohd Turan
Format: Conference or Workshop Item
Language:English
Published: Springer 2020
Subjects:
TJ Mechanical engineering and machinery
Online Access:http://umpir.ump.edu.my/id/eprint/26733/
http://umpir.ump.edu.my/id/eprint/26733/
http://umpir.ump.edu.my/id/eprint/26733/1/Multi%20Response%20Optimisation%20of%20Injection%20Moulding%20Process%20Parameter1.pdf
id ump-26733
recordtype eprints
spelling ump-267332020-01-20T07:13:42Z http://umpir.ump.edu.my/id/eprint/26733/ Multi Response Optimisation of Injection Moulding Process Parameter Using Taguchi and Desirability Function Vivekanandan, Panneerselvam Faiz, Mohd Turan TJ Mechanical engineering and machinery In this study, the optimum injection molding process parameter of warehouse plastic pallets is identified. Compressive strength and part weight are the selected quality characteristic. Barrel temperature, injection speed and holding pressure are the selected process parameter. Taguchi optimization method and desirability function is used to identify the most effective process parameter on the compressive strength and part weight. Based on the conducted experiment, 241 °C of barrel temperature, 72 mm/s of injection speed and 11 MPa of holding pressure, optimise the compressive strength to 5242 kg and part weight to 11.6 kg. The optimised process parameters are studied with an actual experiment and the percentage error of optimised process parameter are identified which is 4.6% for compressive strength and 0.2% for part weight. Moreover, a quantitative relationship between the process parameter and the selected quality response is established using regression analysis. The percentage error of the prediction model for compressive strength is 10% and for part weight is 0.3%. Thus, the prediction model used in this study is effective and practical. This research is beneficial for all the plastic moulding industry which produce plastic pallets. The results can save cost on material consumption and also ensure high product quality. Springer 2020 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/26733/1/Multi%20Response%20Optimisation%20of%20Injection%20Moulding%20Process%20Parameter1.pdf Vivekanandan, Panneerselvam and Faiz, Mohd Turan (2020) Multi Response Optimisation of Injection Moulding Process Parameter Using Taguchi and Desirability Function. In: Intelligent Manufacturing and Mechatronics: Proceedings of the 2nd Symposium on Intelligent Manufacturing and Mechatronics – SympoSIMM 2019, 8 July 2019 , Melaka, Malaysia. pp. 252-264.. ISBN 978-981-13-9539-0 https://doi.org/10.1007/978-981-13-9539-0_26
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
topic TJ Mechanical engineering and machinery
spellingShingle TJ Mechanical engineering and machinery
Vivekanandan, Panneerselvam
Faiz, Mohd Turan
Multi Response Optimisation of Injection Moulding Process Parameter Using Taguchi and Desirability Function
description In this study, the optimum injection molding process parameter of warehouse plastic pallets is identified. Compressive strength and part weight are the selected quality characteristic. Barrel temperature, injection speed and holding pressure are the selected process parameter. Taguchi optimization method and desirability function is used to identify the most effective process parameter on the compressive strength and part weight. Based on the conducted experiment, 241 °C of barrel temperature, 72 mm/s of injection speed and 11 MPa of holding pressure, optimise the compressive strength to 5242 kg and part weight to 11.6 kg. The optimised process parameters are studied with an actual experiment and the percentage error of optimised process parameter are identified which is 4.6% for compressive strength and 0.2% for part weight. Moreover, a quantitative relationship between the process parameter and the selected quality response is established using regression analysis. The percentage error of the prediction model for compressive strength is 10% and for part weight is 0.3%. Thus, the prediction model used in this study is effective and practical. This research is beneficial for all the plastic moulding industry which produce plastic pallets. The results can save cost on material consumption and also ensure high product quality.
format Conference or Workshop Item
author Vivekanandan, Panneerselvam
Faiz, Mohd Turan
author_facet Vivekanandan, Panneerselvam
Faiz, Mohd Turan
author_sort Vivekanandan, Panneerselvam
title Multi Response Optimisation of Injection Moulding Process Parameter Using Taguchi and Desirability Function
title_short Multi Response Optimisation of Injection Moulding Process Parameter Using Taguchi and Desirability Function
title_full Multi Response Optimisation of Injection Moulding Process Parameter Using Taguchi and Desirability Function
title_fullStr Multi Response Optimisation of Injection Moulding Process Parameter Using Taguchi and Desirability Function
title_full_unstemmed Multi Response Optimisation of Injection Moulding Process Parameter Using Taguchi and Desirability Function
title_sort multi response optimisation of injection moulding process parameter using taguchi and desirability function
publisher Springer
publishDate 2020
url http://umpir.ump.edu.my/id/eprint/26733/
http://umpir.ump.edu.my/id/eprint/26733/
http://umpir.ump.edu.my/id/eprint/26733/1/Multi%20Response%20Optimisation%20of%20Injection%20Moulding%20Process%20Parameter1.pdf
first_indexed 2023-09-18T22:41:48Z
last_indexed 2023-09-18T22:41:48Z
_version_ 1777416964479647744

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