In the industrial era, production equipment serves as an essential mother machine. In the global manufacturing industry, components such as laptop computers, mobile phones, and automotive parts all strive for aesthetic appearance. Taiwan’s machine tool industry plays a significant role globally. Faced with the constantly changing market environment, the development and competitive advantage of CNC machines are crucial topics for manufacturers. Domestic manufacturers of computer numerical control machines should move towards the integration of automated equipment to accommodate various advanced parts processing procedures. Smart manufacturing will become the trend of the industry in the future. This study invited experts from academia, industry, and research institutions to conduct expert interviews. Their opinions were compiled and analyzed, supplemented by fuzzy Delphi analysis to establish the development trends of various modules. The feasibility and demand of the product’s functional technology for industrial development were analyzed under three research dimensions and eight technical items. A total of 26 key sub-technical items were identified, achieving an expert consensus level of over 80. Furthermore, the importance ranking was analyzed using the fuzzy analytic hierarchy process, and the consistency tests were passed with C.I. < 0.1 and C.R. < 0.1. Finally, the obtained importance ranking of the hierarchical structure was used to predict the future development of computer numerical control machines through a technology roadmap, helping manufacturers use it as a reference model for future development trends to enhance market competitiveness.

This study took food-grade polypropylene packaging products as the research project and discussed how to control the polypropylene extrusion sheet thickness and vacuum thermoforming quality and weight. The research objective was to find the key factors for reducing costs and energy consumption. The key aspects that may influence the polypropylene extrusion molding quality control were analyzed using literature and in-depth interviews with scholars and experts. These four main aspects are (1) key factors of polypropylene extrusion sheet production, (2) key factors of the extrusion line design, (3) key factors of polypropylene forming and mold manufacturing, and (4) key factors of mold and thermoforming line equipment design. These were revised and complemented by the scholar and expert group. There are 49 subitems for discussion. Thirteen scholars and experts were invited to use qualitative and quantitative research methods. A Delphi questionnaire survey team was organized to perform three Delphi questionnaire interviews. The statistical analyses of encoded data such as the mean (M), mode (Mo), and standard deviation (SD) of various survey options were calculated. Seeking a more cautious research theory and result, the K-S simple sample test was used to review the fitness and consistency of the scholars’ and experts’ opinions on key subitem factors. There are ten key factors in the production quality, including “A. Main screw pressure”, “B. Polymer temperature”, “C. T-die lips adjustment thickness”, “D. Cooling rolls pressing stability”, “E. Cooling rolls temperature stability”, “F. Extruder main screw geometric design”, “G. Heating controller is stable”, “H. Thermostatic control”, “I. Vacuum pressure”, and “J. Mold forming area design”. The key factors are not just applicable to classical polypropylene extrusion sheet and thermoforming production but also to related process of extrusion and thermoforming techniques in expanded polypropylene (EPP) sheets and polylactic acid (PLA). This study aims to provide a key technical reference for enterprises to improve quality to enhance the competitiveness of products, reduce production costs, and achieve sustainable development, energy savings, and carbon reductions.

This study identified the competency requirement for artificial intelligence in finite element analysis. The 10 Delphi group members included 5 field engineers in mechanical fields and 5 scholars from a technology institute. Next, 10 field experts were invited to participate. Using the Delphi technique and analytic hierarchy process, questionnaires were designed to assess competency indicators of artificial intelligence in finite element analysis. The data collected from questionnaires were analyzed using a nonparametric Wilcoxon signed-rank test, the Z value of the Kolmogorov–Smirnovone (KS) test, and relative weight. To fulfill the research objectives, a questionnaire was designed to collect data for 40 general competencies in 10 domains: (1) introduction to the finite element method, (2) pretreatment, (3) coordinate system, (4) model construction skills, (5) boundary conditions and solutions, (6) post processor application, (7) machine learning, (8) neural network, (9) deep learning, and (10) artificial intelligence in finite element analysis. The results of the three rounds of the Delphi technique expert questionnaire revealed essential professional competencies for AI in FEA. These findings can be used to devise a training and development plan and provide valuable references for educators in the field of engineering and technical education.

Hydrostatic guides or bearings are very important crucial components in precision mechanical system. First, the article selects the structural parameters of the standard design film restrictor with regard to hydrostatic bearing. Moreover, using COMSOL, the article performs the fluid–structure interaction analysis of the diaphragm to obtain pressure and stress field distribution, and the deformation of diaphragm in membrane restrictors. Then, fluid resistance and other related properties of the membrane restrictors are obtained, and the related parameters including oil pressure, restrictor gap, membrane thickness, and membrane restrictors to perform feedback action of different oils are explored. Finally, the simulation results and experimental measurements are compared to verify their feasibility. In this study, an oil pressure of 10 bar induced deformation of the membrane with consistent trend; however, oil pressures of 30 and 50 bar induced large error with the same trend.

The machine tool industry is an economically important industry in Taiwan. However, due to the limited natural resources in Taiwan, many of the raw materials required for production must be imported. In 2020, COVID-19, the most serious infectious disease in modern times, broke out across the globe. This has had a great impact on the economic and industrial development of various countries and indirectly affected the development of the machine tool industry. The machine tool industry generally is facing shocks and crises. Therefore, this research article mainly discusses a sustainable operation strategy for the machine tool industry during the COVID-19 epidemic period in Taiwan. Firstly, through the literature on dynamic capability theory (DCT) and expert interviews, the relevant dimensions and criteria are summarized. Then, the fuzzy Delphi method (FDM) and the analytic network process (ANP) are integrated to confirm the relevant dimensions and criteria and to sort the criteria. The five dimensions, in order, are integration ability, learning ability, quality improvement, environmental adaptation, and marketing ability. The ten criteria are diversified learning and innovation ability, integration of multiple sources of knowledge, the ability to learn across departments, the ability to adapt to the external environment, marketing strategy ability, organizational learning ability, integration of resources, improved management efficiency, market research ability, and backward integration. Finally, we put forward business strategies for the ranking results and provide relevant research and industry references.

This research investigates the control of thickness and weight in plastic extrusion vacuum-thermoforming products to identify optimal key parameters for cost reduction and energy savings. The initial step involves identifying crucial influencing factors. In this step, the Delphi technique was employed through a questionnaire administered to a panel of expert scholars to ensure minimal error and maximal reliability in determining key influencing factors. Consensus was sought to establish appropriateness and consistency. Subsequently, the Taguchi method was applied for quality design and planning of the extrusion vacuum-forming process. The experimental design parameters were selected using an L₁₈ (2¹ × 3⁷) orthogonal array, and the desired quality characteristics were determined. Comparative analysis of quantitative production data from two consecutive experiments was conducted, and based on F-values and contribution analysis, the combination of control factors maximizing the Signal-to-Noise (S/N) ratio was identified. The objective is to seek optimal parameters for improving the quality of the plastic polypropylene (PP cup lid) manufacturing process, reducing process variability, and identifying the most robust production conditions. Through multiple actual production prediction experiments, it was determined that five control factors, “polypropylene new material ratio,” “T-die lips adjustment thickness”, “mirror wheel temperature stability”, “molding vacuum pressure time”, and “forming mold area design”, contribute to the maximization of the S/N ratio, i.e., minimizing variability. Statistical validation confirms a significant improvement in product quality and weight control. Noteworthily, the quality control model and experimental design parameters established in this study are also applicable to other plastic products and bio-based materials, such as PET, HIPS, and biodegradable PLA lids with added calcium carbonate. The results of the experimental production demonstrate its ability to consistently control product weight within the range of 3.4 ± 0.1 g, approaching the specified tolerance limits. This capability results in approximately 2.6% cost savings in product weight, contributing significantly to achieving a company’s KPI goals for environmental conservation, energy efficiency, and operational cost reduction. Therefore, the findings of this study represent a substantial and tangible contribution.