Open AccessCategorization of material quality using a model-free reinforcement learning algorithm
Author(s) -
Annapoorni Mani,
Shahriman Abu Bakar,
Pranesh Krishnan,
Sazali Yaacob
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2107/1/012027
Subject(s) - path (computing) , reinforcement learning , computer science , automation , process (computing) , categorization , quality (philosophy) , ideal (ethics) , raw material , artificial intelligence , algorithm , industrial engineering , machine learning , engineering , mechanical engineering , philosophy , chemistry , organic chemistry , epistemology , programming language , operating system
Reinforcement learning is the most preferred algorithms for optimization problems in industrial automation. Model-free reinforcement learning algorithms optimize for rewards without the knowledge of the environmental dynamics and require less computation. Regulating the quality of the raw materials in the inbound inventory can improve the manufacturing process. In this paper, the raw materials arriving at the incoming inspection process are categorized and labeled based on their quality through the path traveled. A model-free temporal difference learning approach is used to predict the acceptance and rejection path of raw materials in the incoming inspection process. The algorithm presented eight routes paths that the raw materials could travel. Four pathways correspond to material acceptance, while the rest lead to material refusal. The materials are annotated using the total scores acquired in the incoming inspection process. The materials traveling on the ideal path (path A) get the highest total score. The rest of the accepted materials in the acceptance path have a 7.37% lower score in path B, whereas path C and path D get 37.28% and 42.44% lower than the ideal approach.