製造現場之料件回報功能分析研究

A Study on the Report Data Functionality of the Shop-floor WIP

吳佳儒(JIA-RU WU)

指導教授 ： 鄭宗明

朝陽科技大學/理工學院/工業工程與管理系/碩士(2017年)

摘要

目前已有許多業者將部份現場工作轉以數位化軟硬體工具來代勞，除了減少人力不足的衝擊外，更希望可以藉此將生產運作更為系統化與自動化。資通訊工具的發展迅速，尤其在取代人力上，更可用其大量、快速與精確的本質，有效地重複甚至超越人類的智慧與能力。其中使用網路為基礎之雲端資訊處理機制，更可結合物聯網與RFID技術運用在製造現場上，將收集到的數據上傳到雲端平台，依據某些預先設定的運算法則，產生即時之決策參考，包含生產單元之狀態預知及數量分析。這些資訊尚可長時間累積，使進一步推演出更多生產現場之常態現象，提供更深入管理之決策所需，而最重要的是不會因此增加人力負擔。
即時匯集生產資訊，了解這些資訊數據的實質意義，並將資訊轉換為可以重複使用的知識與智慧，將是現代化生產現場需要導入的新科技。其前置作業，即為決定收集資訊之種類，以及建立處理此資訊之原則與方法，唯有具備正確無誤的資訊處理機制，未來才可於雲端系統上大量地使用，以機器智慧取代或輔助人力。本研究即以機械製造現場資訊收集為主題，探討其資訊分析方法與應用。

關鍵字

生產資訊收集 ； 雲端製造 ； 線上模擬

並列摘要

In modern manufacturing environment digital tools have been largely adopted to link or connect subtle tasks, and to replace human efforts. This is also the basic concept of systemization and automation. In fact, the information system has prevail human in speed, capability and accuracy, and thus intelligence. It is the cloud technology that makes IoTs and RFID more powerful in the shop-floor, in which production information are collected in realtime and managed in an overall perspective. As a result, the information pile up have become effective knowledge and even wisdom for an optimum management.
Realtime data acquisition and interpretation has become critical for manufacturing industry for they will help cutting down enormous works and generate more profit. The first step for implementing this technology is to determine the type of data to collect on the WIP, and outline the data process flow. By applying the methodology of this research on a cloud database, implicit production status, in the WIP point of view, in everywhere on a shop-floor can become explicit, and the decision made, thereby, can be more effective.

並列關鍵字

Production Data Collection ； Cloud Manufacturing ； On-line Simulation

參考文獻

西文部分：

Chen, C.L. Philip., Zhang, Chun-Yang., Data-intensive applications, challenges, techniques and technologies: A survey on Big Data, Vol. 275, pp 314-347, 2014.

Demirkan, Haluk., Delen, Dursun., Leveraging the capabilities of service-oriented decision support systems : Putting analytics and big data in cloud, Vol. 55, pp 412-421 ,2013.

Gao, R., Wang, L., Teti, R., Dornfeld, D., Kumara, S., Mori, M., & Helu, M, Cloud-enabled prognosis for manufacturing. Vol 64, No 2, pp 749-772. 2015.

Lee, Jay., Kao, Hung-An., Yang, Shanhu., Service Innovation and Smart Analytics for Industry 4.0 and Big Data Environment, Vol. 16, pp 3-8, 2014.