FGP model to optimize performance of tableting process with three quality responses

Author:

Al-Refaie Abbas1

Affiliation:

1. Department of Industrial Engineering, The University of Jordan, Amman, Jordan

Abstract

This research aims at optimizing the performance of tableting process with three quality responses: tablet’s weight, thickness and hardness, using the additive weighted model in fuzzy goal programming (FGP). At initial factor settings, the [Formula: see text] –s control charts are established, then found in-control for all the three responses. The estimated process capability index, [Formula: see text], values indicate that the process is highly capable for weight (=2.78), capable for thickness (=1.79), but it is incapable for hardness (=0.093). Three three-level process factors are investigated including: machine speed, compression force and filling depth, using an [Formula: see text] array. Confirmation experiments at the combination of optimal factor settings obtained using the FGP provide Cpk values of 4.73, 2.03 and 2.04 for weight, thickness and hardness, respectively. These values indicate that the tableting process becomes highly capable for the three responses. In addition, the estimated multivariate process capability index, MCpk (=2.70), has been greatly improved from the estimated value (=0.77) at the combination of initial factor settings. Such an improvement in process performance will lead to huge savings in quality and production costs. In conclusion, the FGP model will provide great support to process/product engineers when improving process performance with multiple quality responses.

Publisher

SAGE Publications

Subject

Instrumentation

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A fuzzy goal programming-regression approach to optimize process performance of multiple responses under uncertainty;International Journal of Management Science and Engineering Management;2018-07-16

2. Using mixed goal programming to optimize performance of extrusion process for multiple responses of irrigation pipes;Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering;2018-06-12

3. Optimal fuzzy scheduling and sequencing of multiple-period operating room;Artificial Intelligence for Engineering Design, Analysis and Manufacturing;2017-08-14

4. Optimizing performance of oil filling processes using fuzzy goal programming;Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering;2017-03-08

5. Optimizing Multiple Quality Responses in the Taguchi Method Using Fuzzy Goal Programming: Modeling and Applications;International Journal of Intelligent Systems;2015-03-28

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