期刊
APPLIED SCIENCES-BASEL
卷 12, 期 6, 页码 -出版社
MDPI
DOI: 10.3390/app12063002
关键词
eco-design; life-cycle assessment; response surface method; genetic algorithm; multi-disciplinary optimization
类别
资金
- Ministry of Science and Technology, R.O.C. [MOST 108-2622-E-110-016-CC3, MOST 110-2622-E-027-029]
From a life-cycle perspective, the design stage is crucial for controlling the environmental impacts of a product. This study proposes a method to optimize eco-design during the concept design stage by balancing environmental impacts and mechanical property requirements. Experimental data, response surface method, and genetic algorithm are used for multi-objective optimization. The results, based on a liquid crystal display monitor design, show that material thickness is a key parameter affecting both objectives, and ABS is the best choice considering environmental impact.
From a life-cycle perspective, the design stage is the key to controlling the environmental impacts of a product because at this stage, all the different parameters can be optimized to realize the required functions while ensuring that the product is environmentally friendly. Here, it is proposed that the optimization of an eco-design should be completed during the concept design stage to strike a balance between the environmental impacts and mechanical property requirements of the product. In this study, experimental data for these two parameters were first obtained via life-cycle assessments and von Mises stress analyses, respectively. Next, the response surface method was adopted to acquire the approximation functions. Finally, a genetic algorithm was employed for multi-objective optimization to realize the eco-design of the product. The proposed methodology was illustrated and evaluated by taking a liquid crystal display monitor design as an example. The results show that material thickness of the mirror is a key parameter that affects both objectives of the product. Although the mechanical properties of ABS are slightly worse than that of PS, it is the best choice for multi-objective optimization while considering the environmental impact at the same time.
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