Logistic Regression Analysis of Factors Affecting Robot Arm Movement Testing
DOI:
https://doi.org/10.55003/ETH.410209Keywords:
Improvement, Logistic regression, Operator performance, Robot arm movement test, Root cause analysisAbstract
Currently, with the rapid growth of robots in the industrial and service sectors, the robotic arm product is in high demand, and manufacturers need to deliver it on time. The manufacturer has a new product called “robot arm”. The issue is a high failure rate at the test station called the robot arm movement test. The manufacturer focused on the test process in order to reduce any variance that may result in a failure rate, with the operator's performance being their primary interest. Since the first group was built and tested, totaling 233 units of the robot arm, the logistic regression was applied with three independent variables. There are operators, working shifts, and product models. The results indicate day or night shifts are not related to the test failing. The operator and product model are important factors in the test failing. The 1.5–meter–long model has a higher chance of passing the test than the 1–meter–long model by about 13.66 times and the 2–meter–long model by 25.25 times. Operator D is the best at performing the robotic arm test and has a better chance of passing than the other operators (2.07 times for Operator A, 6.53 times for Operator B, and 7.01 times for Operator C). The action is that the software test needs to be updated for the 1– and 2–meter–long models. Moreover, Operators B and C need to be retrained as a priority. Then the manufacturer needs to focus more on the assembly process for yield improvement.
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