Chien-Hsin Cheng

A segment of the machine tool industry in Taiwan specializes in manufacturing equipment tailored to the semiconductor sector. Due to the stringent quality requirements inherent in semiconductor manufacturing processes, the machine tools employed in these processes are also subject to correspondingly rigorous quality standards. In fact, a machine tool is assembled from hundreds of individual components, each of which must meet the required standards to ensure that the final product adheres to overall quality requirements. Similarly, each component possesses multiple quality characteristics, all of which must individually satisfy the specified criteria to guarantee that the component achieves the required quality level. Clearly, without a comprehensive evaluation model, ensuring final product quality is difficult. To address this practical issue, this study employed the Process Capability Index (PCI), the most widely used process capability index in the industry, and based on statistical verification principles, constructed a quality assessment and analysis model applicable to products with multiple quality characteristics. This approach enables process engineers to simultaneously evaluate all product quality characteristics and determine whether they meet the desired quality standards. For products that do not meet the expected quality standards, improvement directions are proposed, and improvement decisions are made based on cost and economic benefits, thereby ensuring final product quality. This study concludes with a real-world case study to illustrate the application of the proposed model, making it easier for relevant industries to apply the model.

The rapid progression of emerging technologies like the Internet of Things (IoT) and Big Data analytics for manufacturing has driven innovation across various industries worldwide. Production data are utilized to construct a model for quality evaluation and analysis applicable to components processed by machine tools, ensuring process quality for critical components and final product quality for the machine tools. Machine tool parts often encompass several quality characteristics concurrently, categorized into three types: smaller-the-better, larger-the-better, and nominal-the-better. In this paper, an evaluation index for the nominal-the-better quality characteristic was segmented into two single-sided Six Sigma quality indexes. Furthermore, the process quality of the entire component product was assessed by n single-sided Six Sigma quality indexes. According to numerous studies, machine tool manufacturers conventionally base their decisions on small sample sizes (n), considering timeliness and costs. However, this often leads to inconsistent evaluation results due to significant sampling errors. Therefore, this paper established fuzzy testing rules using the confidence intervals of the q single-sided Six Sigma quality indices, serving as the fuzzy quality evaluation model for components of machine tools.