Junsung Lim, Hoejoo Lee, Youngmin Won, and Hunje Yeon, Samsung Research
Recent advances in machine learning and the proliferation of edge computing have enabled manufacturing industry to integrate machine learning into its operation to boost productivity. In addition to building high performing machine learning models, stakeholders and infrastructures within the industry should be taken into an account in building an operational lifecycle. In this paper, a practical machine learning operation scheme to build the vision inspection process is proposed, which is mainly motivated from field experiences in applying the system in large scale corporate manufacturing plants. We evaluate our scheme in four defect inspection lines in production. The results show that deep neural network models outperform existing algorithms and the scheme is easily extensible to other manufacturing processes.
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