| Autor: |
Ming-Der Jean, Shu-Yi Tu, Jen-Ting Wang |
| Předmět: |
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| Zdroj: |
Journal of Materials Engineering & Performance; Jun2005, Vol. 14 Issue 3, p307-314, 8p, 5 Diagrams, 6 Charts, 2 Graphs |
| Abstrakt: |
Artificial neural network (ANN) modeling and multiple linear regression (MLR) analysis have been used to develop a powder hard-facing process using high-energy plasma-transferred (HEPT) heating. HEPT heating can produce coatings with minimal surface roughness. An optimal procedure was developed involving the least number of process parameters but producing the most desirable performance characteristic. The quality characteristic of interest is the surface roughness after HEPT processing, utilizing the "the-smaller-the-better" criterion. Process performance was evaluated with respect to the signal-to-noise ratios, which were obtainable through experiments. The experimental results conclude that ANN models demonstrate a greater accuracy of predicting the surface appearance than the MLR models in terms of prediction error and the coefficient of determination. The results also reveal the most significant process control parameters. The predicted value of powder hard-facing roughness, through the implementation of optimal settings, produces a satisfactory result. The confirmation experiment showed that the ANN method achieved the expected optimal design goals for the HEPT powder hard-facing, thereby justifying the reliability and feasibility of the approach. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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