Joseph M. Mellichamp, David M. Miller and Jiin Wang
This article concerns the process of conducting a machine qualification (or process capability) study. It presents the results of a research project undertaken to investigate the…
Abstract
This article concerns the process of conducting a machine qualification (or process capability) study. It presents the results of a research project undertaken to investigate the shortcomings inherent in the typical team approach to machine qualification, and to design a comprehensive computer system to overcome these shortcomings. The article begins with a discussion of the steps involved in a qualification study, pointing out that inefficiencies and inaccuracies often arise due to the heavy time and statistical burden placed on the study team. The functionality and the features of the PC‐based computer system developed in the research are described. Numerous screens are provided to illustrate these features. Finally, a synopsis is given of the primary benefits of the system, as well as its limitations.
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Vinayambika S. Bhat, Thirunavukkarasu Indiran, Shanmuga Priya Selvanathan and Shreeranga Bhat
The purpose of this paper is to propose and validate a robust industrial control system. The aim is to design a Multivariable Proportional Integral controller that accommodates…
Abstract
Purpose
The purpose of this paper is to propose and validate a robust industrial control system. The aim is to design a Multivariable Proportional Integral controller that accommodates multiple responses while considering the process's control and noise parameters. In addition, this paper intended to develop a multidisciplinary approach by combining computational science, control engineering and statistical methodologies to ensure a resilient process with the best use of available resources.
Design/methodology/approach
Taguchi's robust design methodology and multi-response optimisation approaches are adopted to meet the research aims. Two-Input-Two-Output transfer function model of the distillation column system is investigated. In designing the control system, the Steady State Gain Matrix and process factors such as time constant (t) and time delay (?) are also used. The unique methodology is implemented and validated using the pilot plant's distillation column. To determine the robustness of the proposed control system, a simulation study, statistical analysis and real-time experimentation are conducted. In addition, the outcomes are compared to different control algorithms.
Findings
Research indicates that integral control parameters (Ki) affect outputs substantially more than proportional control parameters (Kp). The results of this paper show that control and noise parameters must be considered to make the control system robust. In addition, Taguchi's approach, in conjunction with multi-response optimisation, ensures robust controller design with optimal use of resources. Eventually, this research shows that the best outcomes for all the performance indices are achieved when Kp11 = 1.6859, Kp12 = −2.061, Kp21 = 3.1846, Kp22 = −1.2176, Ki11 = 1.0628, Ki12 = −1.2989, Ki21 = 2.454 and Ki22 = −0.7676.
Originality/value
This paper provides a step-by-step strategy for designing and validating a multi-response control system that accommodates controllable and uncontrollable parameters (noise parameters). The methodology can be used in any industrial Multi-Input-Multi-Output system to ensure process robustness. In addition, this paper proposes a multidisciplinary approach to industrial controller design that academics and industry can refine and improve.