Monte Carlo Tree Search for high precision manufacturing

Abstract: Monte Carlo Tree Search (MCTS) has shown its strength for a lot of deterministic and stochastic examples, but literature lacks reports of applications to real world industrial processes. Common reasons for this are that there is no efficient simulator of the process available or there exist problems in applying MCTS to the complex rules of the process. In this paper, we apply MCTS for optimizing a high-precision manufacturing process that has stochastic and partially observable outcomes. We make use of an expert-knowledge-based simulator and adapt the MCTS default policy to deal with the manufacturing process.

• The paper presents an MCTS-based framework that integrates expert knowledge into a POMDP simulator to enhance manufacturing efficiency.
• It adapts the POMCP algorithm by discretizing continuous observations and embedding process-specific heuristics, effectively reducing production time.
• Results demonstrate that the optimized strategy minimizes intermediate measurements and resource use while meeting stringent quality metrics.

Optimization of High Precision Manufacturing by Monte Carlo Tree Search

Introduction

The utilization of Monte Carlo Tree Search (MCTS) in optimizing high precision manufacturing processes presents an opportunity to integrate advanced computational methods with expert domain knowledge for enhancing production efficiency. The paper explores the application of MCTS to a manufacturing process characterized by stochastic and partially observable outcomes, leveraging an expert-knowledge-based simulator and an adapted default policy within the MCTS framework.

Methodology

Simulation of Manufacturing Process

The manufacturing process is treated as a Partially Observable Markov Decision Process (POMDP), wherein the states are defined by the quality metrics of the manufactured component, and actions are represented by various manufacturing steps. A particle-based simulator is used to model the belief state, capturing the noise and partial observability inherent in high precision manufacturing.

The simulation is calibrated using empirical data from real-life manufacturing, aiming to achieve an accurate representation of the process dynamics (Figure 1).

Figure 1: Trajectories in the shape-roughness-space for a single mirror, showing empirical data (crosses) vs. simulation (cyan trajectories).

Optimization via POMCP

The Partially Observable Monte-Carlo Planning (POMCP) algorithm is applied to optimize the sequence of manufacturing actions to minimize production time while achieving target quality metrics. Adaptations to the POMCP algorithm include discretizing the continuous observation space and integrating process-specific heuristics into the default policy to handle terminal state requirements and reset bounds for specific processing steps.

(Figure 2)

Figure 2: A search tree transformation to an optimal tree of actions, showcasing action selection and reward propagation.

Results

The application of the optimized MCTS strategy demonstrated a marked improvement over traditional manufacturing sequences. The optimized process reduced reliance on intermediate measurements, offering significant savings in production time and resources while ensuring the required quality.

(Figure 3)

Figure 3: Comparison of optimized process (blues) vs. real-life benchmark (gray), indicating particle distribution across processing branches.

The optimized strategy offers a structured plan with flexibility to adapt actions based on real-time measurements, reinforcing the decision-making capability of process engineers.

Conclusion

The study successfully implements MCTS for optimizing a high precision manufacturing process by integrating expert knowledge into computational planning. The results indicate significant potential for reducing processing time and cost in complex manufacturing environments. Future work could explore extending this methodology to more intricate process scenarios, encompassing additional quality metrics and capacity optimization.

This approach paves the way for leveraging advanced AI algorithms in precision engineering, serving as a benchmark for further developments in AI-driven manufacturing optimizations.