Abstract
A robust measurement system in the Taguchi Method as a testing method should be explained from the beginning of an experimental design until the application of the optimum condition. Measurement has always been described either by discussing the measurement concepts theoretically or demonstrating a case study on how the data measurement is being done practically. The distance between theory and practical case study that connects the test method used for measurement is always missing. In this paper, a case of T-peel test on strength measurement is used to reflect the robust measurement system, which includes the theory of experimental design together with methods to achieve the optimum condition. Seven control factors, two noise factors with one signal factor are used with orthogonal array L18. Not only the experiment results, but methodology on choosing the control, noise, and signal factors are described intensively. Therefore, the aims are to provide the procedure on evaluating optimum conditions, to analyze variability and optimization of T-peel test when measuring the strength, and to establish a mainstream flow to achieve high-quality experimental design for a robust measurement system. As a result, a robust measurement system that includes variation elimination is developed, which consists of four elements—F (function), N (noise), C (Control), and O (Optimization). The elements of the F-N-C-O system are connected to one another by the Plan-Do-Study-Act; P-D-S-A cycle. The results affect the existing measurement system by enlightening the black box of parameter design behind optimization results in Taguchi Method. Thus, the measurement is more convergent and obtain higher degree of confidence in parameter design.
Highlights
The aim of a robust measurement system is to design a measurement system to get an optimized output by taking variation into consideration
This is done in parameter design that is used to improve the quality without removing the cause of variation and to make the product robust against the noise factors [1]
This paper aims to provide the procedure on determining optimum conditions to analyze variability and optimization when designing a measurement system, and to establish a process flow to achieve high-quality experimental design for a robust measurement system [26]
Summary
The aim of a robust measurement system is to design a measurement system to get an optimized output by taking variation into consideration. The noise factor cannot be eliminated, but the effect can be reduced by choosing the proper level for control factors This is done in parameter design that is used to improve the quality without removing the cause of variation and to make the product robust against the noise factors [1]. The second category is a group of control factors that are active by virtue of having an interaction with noise factors [8] The third category is the factors that have no effect on the response, called non-active This kind of factor can be set at their cheapest or most convenient levels. The finding of this paper reveals the quality result or optimum condition in a measurement system which consists of the function, noise, control parameter, and optimization elements stated as F-N-C-O
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