| Summary: | 碩士 === 國立中央大學 === 光電科學研究所碩士在職專班 === 93 === Surfacing technology for optical components has been well established for almost 250 years. The industry has continued to grow vigorously mostly because of new applications. Throughout the long development history, many different designs and materials were applied; however, the process and challenges of today remain similar to those experienced from the very beginning. Processed surface quality is one of the key factors in achieving good optical performance. Although the modern machines are now equipped with better capabilities, optimization of operation conditions by skilled technicians remains a requirement. Especially, the best control parameters require practices that include trade-offs during prototyping and production startup. Moreover, time-consuming trial and error methods based on experience remain a general practice. All optical components follow the same three-phase surfacing process in general including: 1st - Generating, 2nd - Fine grinding, (or smoothing), 3rd - Polishing. The Fine grinding phase was identified as the most critical process for production efficiency, quality yield, and component performance. Thus, this experimental design focuses on the fine grinding process. The experiment design in this paper applied the general use ophthalmic spherical power range as study case. The lenses design including meniscus concave lens for myopia correction and periscopic convex lens for hyperopia or presbyopia correction. There are total 57 sets curvature designs with 0.25D step international ophthalmic standard spherical power range form S-7.00D to S+7.00D.
This paper shows how process optimization can be achieved in two steps, the first step using Six Sigma methodology gauges the surfacing process control in order to confirm the five general specified factors that are critical to the surfacing operation. A second effective method coupling the Taguchi experimental design and the most important improvement tools of Six Sigma methodology was then applied. The design plan is based on the use of orthogonal arrays introduced by Taguchi. Through the application of Taguchi’s signal-to-noise (S/N) ratio, we demonstrate that the best parameters design plan from an experimental design can be determined. This has several implications: (1) It reduces the implementation time, (2) it can identify a fractional design that contains the best design plan and that design plan could be studied for full experimentation, (3) within a subset of a fractional design plan, the best design point can be found, and (4) the cost of experimentation is significantly reduced since a minimal number of runs is required to identify the best design point. Finally, this important result helps experimenters to select a fractional design plan that contains the “best design point.” The experiment condition for example, it takes minimum﹙53 x 53 =15,625﹚15,625 experiment trials if using the traditional trial and error methods in order to find the optimal parameters. The fact, the results prove the optimal parameters can be found and confirmed with only (18 x 3 =54) 54 trials according to the design in this paper. The result shows it takes only 0.34% time if the same effect use the traditional trial and error non-specific methods.
The traditional control parameters require practices which include trade-offs by skilled senior engineers who are required at this moment to make experiential judgments. In this article, optimized parameters are obtained by applying the mathematical exercise of Non-linear “Artificial Neural Network” to eliminate the subjective judgments. It replaces the errors caused from the experiential judgments made by the expert senior engineers. In terms of the production equipment control and adjustment ability of the newly recruited technician, their capability for exact and reasonable recognition of the production parameters set up is substantially improved. Moreover, the optimal parameters can be applied as the default factory setting in order to be utilized as the reference parameters for general production purposes.
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