台灣自行車產業蓬勃發展，目前為第三大中高階自行車出口國，技術的創新是台灣在自行車產業領先的關鍵。自行車產業除了設計與開發流程之外，生產技術也是讓台灣自行車產業在市場上保有競爭優勢的關鍵因素。其中，車架製程中的焊接技術是一個極需要高技術門檻的關鍵製程，隨著工業生產對於自動化的要求不斷的提高，因此在現場使用焊接機械人的比例也逐漸的提升，導入智能自動化機械手臂的用途以及優勢在於不僅可以提高產品的品質亦可以節省人力、提高生產效率以及可以在不能使用人力的作業環境之下利用機器人協助作業來解決焊接區環境下的種種限制。
在機械手臂導入之初研究者以價值流圖的繪製找出現況發生的問題所在，並設計具體的改善方案逐一消弭所發現的問題。接下來對於所蒐集之工時資料進行情境分析，從中找出規則，將內部經驗外顯化來建構一套具有經驗法則之專家系統，結合專家系統與生產管理方法進行績效指標的蒐集後，經由結果分析與比較發現案例公司若採用專家系統的排序方式可以使得以下幾項績效指標的提升，機台利用率改善9.72%、產出數量在一人兩機以及兩人兩機狀態下導入專家系統後則有顯著的差異：在兩人兩機的作業方式下有9.09%的改善幅度;一人兩機的作業方式下則有6.09%的改善幅度，由此可見利用此方法可以提升系統內的績效。

The bicycle industry is booming in Taiwan, which is currently the third-largest exporter of high-end bicycles. Technical innovation has been the key to Taiwan's leading position in the bicycle industry. In addition to the design and development of high-end bicycles, production techniques are important for a sustainable competitive advantage in the marketplace. In particular, bike frame welding techniques have a high technical threshold. Continuous advances in industrial robot technology have led to the widespread use of robots, particularly in non-traditional applications. Due to the safety and efficiency of automated robots, welding automation is quickly becoming the norm. Due to the made-to-order policy, the procedure is complex and subject to variability. With automatic robots employed in various environments, manufacturers recognize the benefits of robotic welding to keep them competitive in an ever-expanding and highly competitive global marketplace. Some of the main benefits of robotic welding include higher quality bike frames, increased productivity due to simultaneous welds, and greater safety resulting from reduced exposure to toxic fumes. In the present study, we provide an overview of the general procedure and describes the development of an expert system with empirical rules. To achieve this, we use value stream mapping to identify opportunities for improvements and develop multiple solutions to solve the identified problems. We then use the expert system to collect data on-site in order to analyze the workpiece matching problem and assess the performance of our system. Comparing the results before and after the use of the proposed expert system, we found a 9.72% improvement in utilization and a 9.09% improvement in throughput.

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