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Improving principal component analysis (PCA) in automative body assembly using artificial neural networks : Khi-young Jang, Kai Yang, v20, n3, 2001, pp188–197


Publisher
Society of Manufacturing Engineers
Year
2002
Tongue
English
Weight
154 KB
Volume
21
Category
Article
ISSN
0278-6125

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✦ Synopsis


According to production records and field tests of the cellular phone industry, the existing hot bar blade design has two defects: (1) temperature distributions along the edge of the hot bar blade are nonuniform during the heating and soldering processes; and (2) the blade cannot reach the desired temperature and temperature increase rate. The direct result of these problems is poor quality of the soldered printed circuit board. In this research, the heat transfer process along the blade edge during the heating and soldering processes was modeled using conduction and convection equations. The equations were solved based on the initial and boundary conditions from real production. The manufacturing process was simulated by using the finite element method. Simulation results have shown that in the heating process the temperature difference along the blade edge is up to 100°C for the existing blade design. The new design of the hot bar blade was based on the simulation and analysis results and then improved using computer-aided design. The simulation results have shown that the uniformity of the temperature distribution of the new blade has been improved greatly. The temperature distribution difference is less than 15°C in the heating process, and the temperature increase rate meets the technical requirement. Several prototypes of the newly designed hot bar blade were manufactured and tested on the cellular phone production line. The results have shown that measured data were in good accordance with simulation results, and the new design can improve product qualities and raise productivity. The proposed methods used in the modeling, simulation, design, and testing of the hot bar blade can also be applied to other similar processes.


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Improving principal component analysis (
✍ Khi-young Jang; Kai Yang 📂 Article 📅 2001 🏛 Society of Manufacturing Engineers 🌐 English ⚖ 1001 KB

Principal Component Analysis (PCA) has been widely applied to identify the sources of dimenSional variation in automotive body assembly; however, current PCA methods based on a covariance matrix are not appropriate for dealing with high-dimensional data, noisy data, and missing data. Due to its adap