Search

The wrapping layer's foaming process of the car steering wheel industry usually relies on the manual parameter setting by experienced engineers, but the reliability and validity are usually hard to control due to many foaming factors that affect the hardness of steering wheel wrapping layer (e.g., vulcanization time, mold temperature, material liquid temperature, material discharge pressure, humidity). This paper first proposes an intelligent process parameter recommendation system architecture, then develops a neural network-based hardness prediction (NNHP) model to predict the car steering wheel's wrapping hardness. Consequently, we combine NNHP and genetic algorithm (GA) to develop a foaming process parameter recommendation model (NNHP-GA) to effectively recommend the most suitable combinations of process parameters for achieving the desired wrapping hardness in an IoT-based intelligent manufacturing environment. An empirical study is applied in Taiwan's world-leading car steering wheel company. By analyzing the 16 important process sensor data installed in each casting machine, the five experimental results show that the NNHP-GA recommendation system can successfully predict the hardness of the steering wheel's wrapping layer and recommend appropriate process parameters (e.g., sensors S1-vulcanization time, S2-mold temperature, S5-A.P. pressure, S6-B.I. pressure, and "pressurization time") under a specific hardness target. The manufacturing company may employ the NNHP-GA model to build a process parameter recommendation system and increase the quality of process parameter setting. [ABSTRACT FROM AUTHOR]

With the vast amount of capital invested in the wafer fab facility, how to effectively utilize the capacity is always a crucial challenge for semiconductor capacity planners. Currently, static capacity planning approach is widely applied to estimate the planned capacity; however, fab management believes that the ideal planned capacity is too optimistic and unachievable since many production constraints and the impact of abnormal events (e.g., machine breakdown) are not considered. Therefore, a managerial question must be solved: "What is the reasonable capacity plan?" Hence, this paper aims to develop an object-oriented capacity analysis and planning simulation (CAPS) model, which takes into account the production constraints, the operation characteristics of machine tools (e.g., multi-chamber), and the dispatching rules applied in a full-scale wafer fab. Then, capacity planners may employ CAPS model to study the impact of the available time (AT) and its fluctuation of critical machine tools to fab overall capacity and output performance (e.g., wafer out, utilization, work in process (WIP)); a reasonable planned capacity may be generated. Consequently, fab managers only need to watch attentively on the key machine tools, which will cause high impact on the throughput, and keep their promised AT level or increase AT level. [ABSTRACT FROM AUTHOR]

Carbon nanotube field emission backlight (CNT-BLU) is promising to replace traditional backlighting devices in liquid crystal display (LCD) industry. This study reports a laser irradiation process to enhance field emission properties of buckypaper, a thin sheet of high-loading carbon nanotube network. The scanning laser treated the selected region of buckypaper to activate CNT emitters. The improvement of phosphorescence luminance intensity, uniformity, and the reduction of turn-on field were achieved by adjusting machining parameters of laser power, laser lens motion speed, laser resolution, laser beam size, and pattern orientation. Design of experiment and response surface methodology provided ways to rapidly search the feasible laser parameter setting for processing buckypaper field emitters and improving field emission properties within fewer experimental runs. 25−1 Fractional fracotrial design presented the initial models of five repsponses. In addition, the face-centered central composite design is applied since the 25−1 factional factorial design showed curvature significance. It assisted to give the scientifical insight of the following conclusions. High-energy laser treatment damages and burns the CNTs into carbon oxide materials; furthermore, it loses the effective CNTs. Low-energy laser treatment performs CNT activation and produced low field emission performance. In this study, we succeeded to apply statistical analysis methods to understand the physics and mechanics of laser-activated buckypaper field emission and, furthermore, improve, optimize, and demonstrate performance by material selection, process development, and characterization. [ABSTRACT FROM AUTHOR]