Fuzzy Evaluation and Improvement Decision-Making Model for Machining Operation Performance

Abstract

Taiwan is a major exporter and producer of machinery and machine tools in the world. There are at least hundreds of components for various machining machines. According to the concept of Taguchi loss function, when the process quality of the spare parts of machining machines is not good, the failure rate will increase after the product is sold, resulting in an increase in maintenance costs and carbon emissions. As the environment of the Internet of Things (IoT) becomes more common and mature, it is beneficial for manufacturers of machining machines to collect relevant information about process data from outsourcers, suppliers, and machining machine factories. Effective data analysis and application can help the machining machine industry move towards smart manufacturing and management, which can greatly reduce the average number of failures per unit time for all sold machines. Therefore, this paper developed a practical evaluation and improvement decision-making model for the machining operation performance to help machining machine manufacturers find out the components that often fail and improve them, so as to reduce the total loss caused by machine failures. This paper first defined the machining operation performance index for the machining machines and discussed the characteristics of this operation performance index. Subsequently, the confidence interval of the index was deduced, a fuzzy evaluation model based on this confidence interval was proposed, and decision-making rules regarding whether to make any improvement was established. The fuzzy evaluation and improvement decision-making model for the operation performance of machining machines proposed in this paper will contribute to various tool industries to boost their process quality, reduce costs, and lower carbon emissions, in order to achieve sustainable management of enterprises and the environment.

Building Up of Fuzzy Evaluation Model of Life Performance Based on Type-II Censored Data

“…As noted by Buckley [32], the α-cuts of triangular fuzzy numbers β L0 are [19,22] β L0 [α] = βL01 (α), βL02 (α) , for 0.01 ≤ α ≤ 1 βL01 (0.01), βL02 (0.01) , for 0 ≤ α ≤ 0.01 (20) where βL01 (α) = 2r…”

“…Similarly, d T (the length of the base of the set B T ) is replaced with the area of B T . As the membership functions are asymmetric, d T = 2(C RR − C R ) on the basis of Chen and Chang [13] and Chen et al [20]. In Figure 2, by using the principle of similar shapes, the square of the side length ratio is equal to the area ratio.…”

“…In Figure 2, by using the principle of similar shapes, the square of the side length ratio is equal to the area ratio. Next, B R /B T was replaced with d R /d T as the fuzzy evaluation tool, where d R and d T are calculated as follows [13,20,33]:…”

“…This does not fit the conventions of the industry. Chen et al [20] therefore proposed a relative lifetime performance index. This index is defined as follows:…”

Industrial Internet of Things (IIoT) and Other Industry 4.0 Technologies in Spare Parts Warehousing in the Oil and Gas Industry: A Systematic Literature Review

“…The results of successful IoT implementation in these activities are given to be improved financial performance, labor productivity, and customer satisfaction. Improvement in decision making through Industry 4.0 technologies has benefits along an industry's value chain, including external suppliers and outsourcers [34]. In healthcare, IoT in combination with blockchain and cloud computing secures the storing of health records, whose benefits are transparency and decentralization [35].…”