Transfer Learning for Detection of Casting Defects Model In Scope of Industrial 4.0

Abstract

Casting represents a production process where a liquid material is poured into a mold with a hollow cavity, usually of the intended shape, following which its solidification is allowed. Numerous defect types are available, including blow holes, pin holes, burrs, mold material defects, shrinkage defects, metallurgical defects, casting metal defects, etc. All industries have quality control departments to eliminate the occurrence of this defective product. But the main problem is that this inspection process is done manually. This is a very time consuming process and due to human sensitivity this is not 100% accurate. In this study, we will verify whether the "manual inspection" bottleneck can be eliminated by automating the inspection process with transfer learning in the manufacturing process of casting products. In this study, we will verify whether the "manual inspection" bottleneck can be eliminated by automating the inspection process with transfer learning in the manufacturing process of casting products. In this study, the casting images were divided into two separate classes, and the classification process was carried out by applying deep learning architectures. The benefits of this proposed approach are discussed and proposed as a more efficient way to control the quality of final products under Industry 4.0.