簡易檢索 / 詳目顯示
| 研究生： |
陳逸昕 Chen, Yat-Hsin |
|---|---|
| 論文名稱： |
印刷電路板之簡易生命週期評估研究 The Study of the Simple Life Cycle Assessment for Printed Circuit Boards |
| 指導教授： |
陳家豪
Chen, Jahau-Lewis |
| 學位類別： |
碩士 Master |
| 系所名稱： |
工學院 - 機械工程學系 Department of Mechanical Engineering |
| 論文出版年： | 2004 |
| 畢業學年度： | 92 |
| 語文別： | 中文 |
| 論文頁數： | 89 |
| 中文關鍵詞： | 生命週期評估 、印刷電路板 |
| 外文關鍵詞： | PCB, LCA |
| 相關次數： | 點閱：123 下載：7 |
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隨著科技快速的腳步，電子相關產品的日漸增多，印刷電路板可以堪稱電子產品根基，幾乎所有的電子產品上都不可或缺的一種材料，所以降低印刷電路板對環境的衝擊也成為現今最重要的環保工作，要知道印刷電路板對環境造成的衝擊，可利用傳統之生命週期評估方法（Life Cycle Assessment）做出詳細的分析，所得的結果固然相當的精確，但是其經濟效益並不高，因為評估時間長且必須了解其各項詳細的資訊。
本論文提出以較簡化的方式與資料擴充性大之方法來進行評估的工作，結合人工智慧的「類神經網路」來進行簡易的印刷電路板生命週期評估。本文將以所蒐集之印刷電路板資料，分成兩方法去作印刷電路板之簡易生命週期評估的類神經網路模式建立。第一種方法，只具有印刷電路板材料組成部分的資料，我們透過類神經網路，以印刷電路板之特徵為輸入，而輸出為印刷電路板之重量百分比後，在經過簡易的計算得到其生命週期評估數值；第二種方法，有完整的生命週期評估資料部分，在經過類神經網路訓練之後，以印刷電路板之特徵為輸入，輸出即為生命週期衝擊評估指標；而此簡化的評估方式可使設計工程師在產品設計時即得知印刷電路板對環境之衝擊。
More and more electronic-related products have been presented to the market because of the rapid development of technology. We can say that printed circuit board (PCB) is the pillar of the electronic products. It is the indispensable material to all electronic products. That’s the reason we need to decrease the pollution impact for protecting the global environmental. It can be completely analyzed by traditional life cycle assessment（LCA）to realizing the impacts on the environment caused by PCB. Although the analytic results of traditional LCA process can lead to accurate results, it doesn’t make good economic advantage because of long estimative time and detailed information requirement.
In this study, a simplified and data-expanding method is proposed for evaluating product. The proposed method uses the neural networks technique to obtain the simplified LCA result of PCB. According to the information of PCB, two neural networks modules are built for simplified LCA of PCB. First, for the data which consists of PCB, we consider the characteristics of PCB as the input and weighting percentage of PCB as output which are obtained by neural network. Next, we get the LCA result through a simple calculation. Second, after training by neural networks, we consider the characteristics of PCB as input and LCA impact indicators as output respectively. The proposed simplified evaluative method can make design engineers obtain the environmental impact of PCB during the design period.
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校內：立即公開校外：2004-08-17公開