_applied_  
_sciences_  
_Article_  
The 3D Deburring Processing Trajectory Recognition Method  
and Its Application Base on Random Sample Consensus  
[PERSON] 1, [PERSON] 2,*\(\copyright\), [PERSON] 1 and [PERSON] 3\(\copyright\)  
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deburring is mostly performed manually. Nevertheless, deburring has a high output value. For example, Taiwan's metal factories manufacture approximately four million bicycle forks per year. Deburring after die casting is mainly performed manually, and bicycle forks produced after casting are subcontracted to and deburred by four organizational equipment manufacturers (OEMs), who are paid tens of millions of dollars (NT$). However, despite this high revenue, the profit margins enjoyed by these OEMs are small (of all the costs involved, labor accounts for approximately 80%). This, coupled with the fact that young people and foreign workers have been reluctant to venture into the manufacturing industry in recent years, has resulted in the OEMs encountering a labor shortage and evaluating the benefits of automatic deburring. In metal and plastic molding and cutting operations, deburring is the least automated procedure and thus has the highest labor costs. Before deburring operations can be performed with current automatic deburring systems, the main body of the automated carrier, the tool center point, the tool axes, and the relationship between the carrier and the object to be processed must be calibrated. In addition, a series of offline programming software settings must be entered to generate projection trajectories. During processing, a control device is used to compensate for the dynamic errors in robotic arm movement (the robotic arm is in an open kinematic chain, and the end precision is thus easily affected during manufacturing and assembly; furthermore, the accuracy of joint drones and trajectories considerably affects the processing quality [3; 4; 5; 6]) and deviations in workpiece dimensions. As the manufacturing industry shifts from mass production with an economies-of-scale model to a customized business-to-consumer (B2C) model (i.e., producing small quantities of goods for large numbers of clients), how businesses can quickly generate processing trajectories for new products remains a question that must be answered before smart deburring can be realized. The main problems with implementing automated deburring in the context of the customized B2C model are as follows:  
1. Planning the trajectories of various workpieces offline is a costly, time-consuming, and labor-intensive process that cannot meet the manufacturing industry's demand for rapid production of customized goods. 2. Molding processes (such as plastic injection molding and metal casting molding) are affected by their environments. If large dimensional deviations occur during processing, workpieces may fail to maintain their fixed dimensions, and predetermined processing trajectories will not be applicable to all workpieces.