GAI生成資料可行性驗證：幾何模擬、力學分析與產線應用

生成式 AI 在製造業具潛力，但因數據可靠性與應用效益難以預測，且缺乏驗證機制，導入時常需大量試切測試，造成高成本與低效率。加上現有模型欠缺產業專業知識，難以直接支援製程規劃，成為落地瓶頸。本文聚焦三項常用切削資料驗證技術：加工模擬、幾何拓樸與切削力學分析。透過生成資料驗證，可於實際加工前即完成模擬與評估，加速生成資料可行性確認，降低導入門檻。不僅能有效降低成本與縮短開發週期，更能提升產業回應需求與產品規劃效率。強化製造業者產品競爭力，並有助國際市場拓展與智慧製造轉型。

Generative AI has great potential in the manufacturing industry, but due to the unpredictability of data reliability and application benefits, and the lack of a verification mechanism. A large number of cutting tests are often required during implementation, resulting in high costs and low efficiency. In addition, the existing models lack industry expertise and cannot directly support process planning, which has become a bottleneck for implementation. This article focuses on three commonly used generated data verification technologies in the cutting field: virtual machining simulation, geometric topology, and cutting mechanics analysis. Through generated data verification, simulation and evaluation can be completed before actual machining, accelerating the feasibility confirmation of generated data and lowering the threshold for introduction. It can lower costs, shorten development time, and boost industry efficiency in meeting needs and planning products. It can enhance the competitiveness of manufacturing products and expand international market and transform to smart manufacturing.