工具機數位孿生模型於故障自主偵測之實務應用

工具機設備如發生切削瑕疵時會影響客戶端的產品交期，造成生產效率大幅降低，因此工具機售服維修端往往需要快速針對機台問題進行故障辨別與修復，現階段的售服人員大多依賴自身經驗與試誤法來猜測問題，加上無設計端的研發人員參與問題解析，使得故障原因判別問題無法即時得到解決。本技術將建立工具機數位模型與關鍵零組件參數敏感度資料庫，連結實際加工製程品質，當整機動態性能指標因關鍵零組件磨耗產生變化時，即可連結到敏感度分析資料庫進行肇因追溯，並發出警訊給終端使用者及工具機設備售服維修單位進行故障排除，達到提升設備稼動率及工具機產品妥善率的目標，縮短台灣工具機產業在維修售服端的成本並降低客戶停機時間損失。

When machine tool cutting defect occurs, it can delay delivery date of client’s products and cause significant reduction in production efficiency. As a result, the service personnel needs to be able to identify and fix problems quickly with the machine. Currently, most sales and service personnel rely on their own experience to carry out a trial and error method in repairing, It will not make significant difference even with the help of R&D personnel if they don’t have fault diagnosis experience. The aforementioned points all lead to slow identification and resolution of the problem. This article will discuss establishment of a digital model of the machine tool and key parameters that are linked to the actual quality of the manufacturing process. When indicators show that key parts have been worn, it will be linked to sensitive database to identify the cause, and issue a warning to user and the machine tool equipment. This could help to achieve the goal of increasing the utilization rate of equipment and the proper rate of machine tool products, as well as to reduce the service and maintenance cost of machinery and machine downtime in Taiwan's machine tool industry.