演算法在筆記型電腦機殼上的應用

NCUIR > School of Management at National Central University > Executive MBA > Electronic Thesis & Dissertation > Item 987654321/88273

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/88273

Title:	演算法在筆記型電腦機殼上的應用
Authors:	李峰旭;LEE, FONE-HSU
Contributors:	高階主管企管碩士班
Keywords:	塑膠成型最佳成型參數;灰系統關聯分析法;基因規劃演算法;基因演算法;Optimal parameters for plastic molding;Grey Relation Analysis model;Genetic programming;Genetic Algorithms
Date:	2022-04-15
Issue Date:	2022-07-13 21:15:07 (UTC+8)
Publisher:	國立中央大學
Abstract:	塑膠成型產業的研發和使用技術已經十分純熟且趨向穩定，不過隨著產品多樣化，塑膠成型其特性需要我們去發掘與開發和多用途的使用。傳統射出成型生產過程中，前期測試與參數調整皆須倚賴人工，成型產品多為使用人工方式進行品質檢驗與確認和不良修正，並在生產完成後以人工方式進行參數紀錄作業。然而，相關研究中探討的主題多為外觀有關，如尺寸、變形度或產品結合線等，但過去有關塑膠成型的主題，缺乏同時考量外觀以外的問題，如埋釘柱側推力與硬度等，同時也缺乏使用數位化方式進行製程參數改善。其次，過去塑膠成型的研究其模擬模型通常須符合一些假設及樣本數限制，例如：常態分配、需要大筆資料或未能將影響因素考慮至模型中。綜合上述，本文提出混合型製程參數優化方法包含灰系統關聯分析法、基因規劃演算法與基因演算法，此方法分為兩個階段，第一階段是灰系統關聯分析法，該方法的優點為能將探討主因子與影響因子的灰關聯係數導入模型之中，並且僅需四筆以上之樣本就能夠準確的推算關聯值。第二階段結合演算法建立誤差關聯數學式，此兩階段模型將被稱為混合型製程參數優化方法。實驗部分將以三個塑膠成型工廠為例，進行產品尺寸、變形度與埋釘柱側推力，驗證不同影響因素的組合，利用本研究提出的混合型製程參數優化方法和未使用混合型製程參數優化方法相互比較其標準差與製程能力指數，以確認此方法在實際問題運用上的價值。;The R&D and application technology of the plastic molding industry has stabilized. However, with the variety of products, the new technology of plastic molding need to be explored and developed. In the traditional injection molding process, preliminary testing and parameter adjustment are manually performed, and most of the molded products are manually inspected for quality confirmation and defect detection, and the parameters are manually recorded after production. However, most of research are related to appearance, such as dimensions, deformation, or product bonding lines. However, in the past, the topic of plastic molding has lacked the ability to consider issues other than appearance, such as the lateral force and stiffness of studs, and the use of digital methods to improve process parameters. The research of plastic molding using conventional statistical methods usually requires some assumptions and limitations such as normality, large dataset and not considered the factors into model. Consequently, this study investigated model which are Grey Relation Analysis model, Genetic Programming and Genetic Algorithms. First, the grey relation analysis model get the high relational group. Then use algorithm to create mathematic forms and get parameters. In the experimental section, three plastic molding plants will be used as examples to compare the process capability index and standard deviation with each other using the hybrid optimization method proposed in this study and the unused method, considering the combination of different influencing factors of the experiments, to confirm the value of this method in the application of practical problems.
Appears in Collections:	[Executive MBA] Electronic Thesis & Dissertation

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