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Article
Publication date: 5 August 2019

Angus Jeang, Chang Pu Ko, Chien-Ping Chung, Francois Liang and Guan-Ying Chen

This study considers the five factors of a car rotation system: angle (F1), arm length (F2), toe in and out (F3), width (F4) and length (F5). The purpose of this paper is to fine…

136

Abstract

Purpose

This study considers the five factors of a car rotation system: angle (F1), arm length (F2), toe in and out (F3), width (F4) and length (F5). The purpose of this paper is to fine tune the design so it produces the smoothest response to various rotation angles.

Design/methodology/approach

In the case of Ackerman’s principle, the response surface methodology (RSM) was used to analyze data when encountering different quality characteristics at various rotation angles.

Findings

In this study, RSM was used to obtain the best factor and the best reaction value for the five factors of a car rotation system.

Practical implications

In this study, the four-wheel steering of a car is taken as an example. When the current wheel is turned, the intersection of the left and right wheels of the front axle falls on the extension line of the rear wheel. In this case, the steering will be the smoothest. In this example, we selected angle (F1), arm length (F2), toe in and out (F3), width (F4) and length (F5) as experimental factors, hoping to satisfy the Ackerman principle.

Social implications

Traditionally, when dealing with four-wheel steering problems, solutions may be based on past experience or on new information used to formulate R&D plans. In this study, the combination of statistical factors and optimization is used to find the optimal combination of factors and the relationship between factors.

Originality/value

In the past, most literature relied on kinematics to study the car rotation system due to a lack of experimental design and analysis concepts. However, this study aims to achieve the above goals in finding the solution, which can be used to predict reaction values.

Details

International Journal of Quality & Reliability Management, vol. 36 no. 7
Type: Research Article
ISSN: 0265-671X

Keywords

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Article
Publication date: 11 February 2019

Angus Jeang, Chang Pu Ko, Chien-Ping Chung, You-Jie Chen and I. Lin

The purpose of this paper is to establish the regression model by a simulation method that was obtained by using the system response at unit cost as the response value. The unit…

160

Abstract

Purpose

The purpose of this paper is to establish the regression model by a simulation method that was obtained by using the system response at unit cost as the response value. The unit availability was maximized, while the unit cost was minimized.

Design/methodology/approach

In this study, the Monte Carlo simulation method was used to simulate an operational system, and the regression model was obtained by using response surface methodology with the experimental matrix and different levels of experimental combinations.

Findings

The optimal value of mean time between failure (MTBF) and mean time to repair (MTTR) of each component was then obtained by using the system response at unit cost as the response value.

Practical implications

Due to the upgrading of industrial technology and the maturity of electronic technology, product development technology has become highly sophisticated with complex designs. Reliability engineering has become a key procedure of high-tech industry.

Social implications

Based on the system availability of unit cost as the response value, it can maximize the availability to help decision makers to formulate the best selection strategy components and repair strategy.

Originality/value

Previous works regarding the parameter settings of reliability values never mention the simulation methodology. However, this study aims to achieve the above goals in finding the relationship of MTBF and MTTR simultaneously.

Details

International Journal of Quality & Reliability Management, vol. 36 no. 3
Type: Research Article
ISSN: 0265-671X

Keywords

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Article
Publication date: 7 September 2015

Angus Jeang

The purpose of this paper is to build a curve that can portray quality level, with standard deviation, as a function of the production process related to elements such as…

1046

Abstract

Purpose

The purpose of this paper is to build a curve that can portray quality level, with standard deviation, as a function of the production process related to elements such as operating time and cumulative units produced.

Design/methodology/approach

The Cobb-Douglas multiplicative power model will be introduced to represent the proposed function in simultaneously describing the learning process for productivity and quality. The experimental devices consisted of reflective mirror, path paper, iPod Touch and pen. They were arranged as shown in Plate 1. The students were instructed to draw a line with a pen along the middle of the rail line on the path paper through the mirror indirectly. The iPod Touch acted as a stopwatch to monitor the time taken to complete each experiment. The path paper is shown in Figure 1. This statistical analysis is completed by computer programs, SAS.

Findings

This study presented an experiment in which subjects drew a line on a path while looking through a mirror. This study uses the Cobb-Douglas model to regress the S as a function of 0.3366×x 1−0.347×x 2−0.011.

Research limitations/implications

All units produced are acceptable in quality, disregarding the magnitude of standard deviation in the produced quality level. Like Porteus (1986) with the fixed probabilistic distribution is assumed. The fatigues are ignored in presented curve. In fact, operators are easy to get tired for attending quality and productivity simultaneously. The initial value of operating time or standard deviation for the first unit is estimated from a subject having been trained for a sufficient period of time; however, this consideration does exist in the present experiment.

Practical implications

The economic order (production) quantity model with learning effects in a production system could be considered. The other implication could be in a wider framework, such as multistage and multivariate of production development production systems and supply chains.

Social implications

For a life cycle application, the criteria considered in resolving the production problem should not only be limited to the costs involved in the production process, but also the quality-related costs incurred after the goods are delivered to customers.

Originality/value

Previous works regarding the learning process never mention the quality-related learning process. However, this study aims to achieve the above goals in finding the relationship of quality vs production volume and production time simultaneously.

Details

International Journal of Quality & Reliability Management, vol. 32 no. 8
Type: Research Article
ISSN: 0265-671X

Keywords

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