利用大數據分析提升智慧製造之應用

Abstract

隨著自動化生產的技術來臨使得各製造產業技術迅速發展提升，以往的傳統產業已朝向精密機械化的方向發展，近年來隨著工業4.0的蓬勃發展，隨之而來的智慧化工廠週邊設備也帶來相當龐大的商機，除了其週邊精密的設備、自動化產線等需求外，齒輪製造產業在面臨極度競爭狀況下，除了需要不斷創新研發、強化機構設計，以提高加工品質與效率，並藉由專利權的保護，以維繫及拓展客戶，提升競爭優勢之外，更要提升生產效能及隨時掌控進度以縮短交貨期。 故本研究將針對自動化生產之睦茗精密齒輪公司(ABOVEGEAR INC.)，藉由安裝資料擷取模組，來監測機器停機之確切時間，以此減少人為因素影響，並利用無線傳輸方式將資料同步至公司系統，增加生產資訊的即時性。接著以主要停機原因之次數為自變數，生產稼働率為應變數，建立迴歸模型，並利用複迴歸分析來探討停機原因之次數與稼動率之關係，建立一套藉由改善停機原因之次數來提升生產效能之預測模型，作為企業決策之依據。 本研究之公司已於齒輪業發展20年以上，雖已站穩腳步，但在技術的創新上，仍時刻致力於新製程的改善、突破。另也因主事者有工程背景，利用高精度的加工機所製造的品質效果，搭配其本身自有的開發團隊進行新製程的創新，優化原廠機械模具的設計，提昇加工速度而不影響物件的精度，便是睦茗精密齒輪公司(ABOVEGEAR INC.)的核心競爭力。但雖有其能力之外，如何讓設備稼働率提高並應用設備至極致化即是本研究的重要課題。

With the advance of automated production technology, the technology application of various manufacturing industries has developed rapidly. In the past, traditional industries have developed in the direction of precision mechanization. In recent years, with the vigorous development of Industry 4.0, the accompanying smart factory peripheral equipment has also been introduced. There are huge business opportunities. In addition to the demand for peripheral precision equipment and automated production lines, the gear manufacturing industry is facing extreme competition. In addition to continuous innovation, research and development, and strengthening of institutional design to improve processing quality and efficiency, Patent protection, in addition to maintaining and expanding customers, and enhancing competitive advantages, it is also necessary to improve production efficiency and control progress at any time to shorten delivery time. Therefore, this research will focus on the automatic production of ABOVEGEAR INC., by installing data acquisition modules to monitor the exact time of machine downtime, so as to reduce the influence of human factors. On the other hand, we synchronize data to the company system by using wireless transmission, to increase the immediacy of production information. Then take the number of main downtime reasons as the independent variable and the production yield rate as the contingency number, establish a regression model, and use multiple regression analysis to explore the relationship between the number of downtime reasons and the utilization rate, and establish a set of methods to improve the number of downtime reasons. The predictive model for improving production efficiency serves as the basis for corporate decision-making The company in this study has been in the gear industry for more than 20 years. Although it has established a firm foothold, it is still committed to the improvement and breakthrough of new manufacturing processes in terms of technological innovation. In addition, because the principal has an engineering background, the quality effects produced by the high-precision processing machine are used with its own development team to innovate the new process, optimize the design of the original machine mold, and increase the processing speed without affecting the object. The precision of this is ABOVEGEAR’s core competitiveness. But despite its capabilities, how to increase the equipment utilization rate and apply the equipment to the extreme is an important topic of this research.