Using Artificial Intelligence to Facilitate Assembly Automation in High-Mix Low-Volume Production Scenario

摘要

Artificial Intelligence (AI) based algorithms are being used increasingly to support industrial robots in the automation of assembly processes. The objective of this work is to detect bores of different geometry, appearance, and inner structure for automating a high-mix low-volume assembly line. Two most widely used AI algorithms namely, Single Shot Detection (SSD) and You Only Look Once (YOLO) have been used to perform the bore detection process. The results obtained by these algorithms have been compared with the conventional detection algorithm (Gradient filter) using the standard metrics used for evaluating the performance of the detection algorithms. The obtained results demonstrate the efficiency and robustness of AI-based algorithms for the detection as they exhibit better performance than the conventional detection method.