{"id":9685,"date":"2026-08-05T21:54:26","date_gmt":"2026-08-05T13:54:26","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=9685"},"modified":"2026-08-06T23:07:34","modified_gmt":"2026-08-06T15:07:34","slug":"jase-202611-34-015","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202611-34-015","title":{"rendered":"Deep Reinforcement Learning For Adaptive Multi Axis CNC Tool Path Optimization Under Dynamic Constraints"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 34<\/a><\/p>\n<\/div>\n\n\n\n

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Xiaorong Zhou<\/i><\/a>, Lidong Huang, and Xiaoping Liu<\/p>\n\n\n\n

School of Mechanical Engineering, Hunan Mechanical & Electrical Polytechnic, Changsha, Hunan, 410151, China<\/p>\n<\/div>\n\n\n\n

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Received: June 05, 2026
Accepted: July 18, 2026
Publication Date: August 05, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Overall Flow of Multi Axis CNC Tool Path Optimization <\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202611_34.015<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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In modern production, multi-axis Computer Numerical Control (CNC) machines are utilized to cut complex pieces with extreme precision. However, during the cutting process, real-world problems such as tool wear, vibration, heat, and material changes make it difficult to adhere to predetermined tool paths. Traditional methods cannot react in real time, resulting in low efficiency and increased expenses. The goal of this research is to develop an intelligent and adaptive system for CNC tool path planning that uses deep reinforcement learning (DRL) to respond to dynamic machining environments and increase cutting performance. The dataset includes 3D CAD models, sensor data, tool positions, feed rates, and material parameters, which are utilized to train and validate the DRL agent. The proposed method utilizes fixed grid voxelization (FGV) to convert 3D CAD part representations into voxel grids, enabling the algorithm to analyze the machining region as discrete geometry blocks. Next, environment modeling is structured using a Markov Decision Process (MDP), considering tool conditions like location, spindle load, and vibration as states and tool movements as actions. Dijkstra\u2019s algorithm (DA) is used to find the shortest baseline path across the voxel grid, which improves the DRL agent\u2019s decision-making during toolpath generation. The learning agent uses Proximal Policy Optimizer Driven Double Deep Q-Network (PPO-DDQNet) for optimal tool pathways, ensuring precise value prediction and stabilizing the learning process. Geometry Planning under Varying Time Constraints based on angular loss, vibration loss 1450-2000 seconds.<\/p>\n\n\n\n

Keywords: CNC environments, tool path planning, Proximal Policy Optimizer Driven Double Deep Q-Network (PPO-DDQNet), cutting performance, Computer Numerical Control (CNC)<\/em><\/p>\n\n\n\n

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