Reinforcement Learning for Statistical Process Control in Manufacturing |
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| Zsolt János Viharos, Richárd Jakab |
- Abstract:
- The main concept of the paper is to place Reinforcement Learning (RL) into various fields of manufacturing. As a first attempt, RL for Statistical Process Control (SPC) in production is introduced in the paper; it is a promising approach owing to the adaptability and continuous application capability of reinforcement learning. The well-known Q-Table method was applied for get more stable, predictable and easy to overview results, therefore, quantization of the values of the time series to stripes was required. The formulated goal was to predict the time series value in a certain number of production steps ahead as manufacturing trend forecast. The recent values of the analysed time series were selected as states for the RL and the future probabilities of its values being in the formulated stripes were defined as RL actions. For action update, the Bellman equation was applied and the RL reward depends on how accurate the predicting is. Furthermore, two concepts were introduced, the Reusing Window (RW) and the Measurement Window (MW). The RW is a sliding window that determines how many times one measured value of the time series will be reused during the RL repeatedly, while the MW is defined for enabling the comparison of learnings with different RWs by sampling them with the same evaluation frequency.
- Keywords:
- Reinforcement Learning; Manufacturing; Statistical Process Control (SPC); Quality Trend Forecast
- Download:
- IMEKO-TC10-2020-032.pdf
- DOI:
- -
- Event details
- IMEKO TC:
- TC10
- Event name:
- TC10 Conference 2020 (ONLINE)
- Title:
17th IMEKO TC10 Conference "Global trends in Testing, Diagnostics & Inspection for 2030” (2nd Conference jointly organized by IMEKO and EUROLAB aisbl)
- Place:
- Dubrovnik, CROATIA
- Time:
- 20 October 2020 - 22 October 2020