This paper presents a machine vision-based surface inspection system for journal bearings. Traditionally, human operator inspection classifies the defective and defect-free bearings. Although the operator has capability to find a defect on the bearing surface, his/her inspection ability is influenced by fatigue and physical conditions related to repetitive work. Therefore, machine vision systems are widely used for quality control in order to reduce costs and to improve product quality. In this paper, we develop a machine vision system for journal bearing surface inspection that can inspect various types of defects on the bearing surface, such as laser marking quality, gas pockets, 2 Φ hole measurements, rust and so on. The proposed system was evaluated and installed on a journal bearing manufacturing line. The journal bearing manufacturer reported that the proposed system has high accuracy and efficiency.
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