Virtual CNC Reinforcement Learning for Global Tool Path Planning
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Solution Overview
Problem
Computer numerical control (CNC) machine tools execute machining programs sequentially with a short sight on the future and without a global overview, leading to inefficiencies in machining processes.
Innovation Solution
A virtual environment for reinforcement learning is implemented, where a system comprising an interpreter and agent component determines rewards and actions to optimize tool paths by changing tools, moving tools, turning spindles on/off, and learning from experiences to improve machining efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If CNC machine tools execute machining programs sequentially with short sight, then the control system remains simple and easy to program, but machining efficiency and productivity deteriorate due to lack of global overview and future planning
Solution Approach 1:
The reinforcement learning agent performs preliminary learning and planning in a virtual environment before actual machining. The agent simulates multiple machining scenarios, learns optimal tool path strategies, and prepares decision-making models in advance. This allows the CNC system to execute pre-learned optimal sequences without real-time complex calculations, thereby improving productivity while keeping the actual control system relatively simple.
Solution Approach 2:
A reinforcement learning agent acts as an intermediary between the simple sequential controller and the desired intelligent optimization. The agent learns optimal policies through simulation and translates them into actionable tool path instructions. This intermediary layer provides global overview and future planning capabilities without requiring fundamental changes to the underlying CNC control architecture, thus improving productivity while managing complexity.
2Manufacturing precision
If traditional CAM software programs tool paths without reinforcement learning, then the programming process is straightforward, but the quality of tool paths and machining precision deteriorate due to lack of adaptive optimization
Solution Approach 1:
The system creates a virtual copy of the machining environment where reinforcement learning can be performed without affecting actual production. The agent learns optimal tool paths in this virtual replica, which is then applied to the real machining process. This copying approach enables sophisticated learning and optimization while keeping the actual CAM software relatively simple, as the complex AI model is trained separately in simulation.
Solution Approach 2:
The reinforcement learning agent performs preliminary optimization of tool paths before actual machining operations. By pre-learning optimal strategies in virtual environments and pre-planning tool paths with future considerations, the system achieves higher manufacturing precision. The actual machining process then executes these pre-optimized paths, maintaining simplicity in the real-time control while achieving superior precision results.
3Loss of time
If CNC machine tools lack global overview of processes, then the control system remains simple and responsive, but the ability to optimize overall machining performance and reduce total machining time deteriorates
Solution Approach 1:
The reinforcement learning agent performs preliminary analysis of the entire machining process in virtual simulations, identifying optimal sequences and strategies before actual execution. By pre-planning with global overview in the virtual environment, the system determines the most efficient tool paths and operation sequences. This preliminary action reduces total machining time when the pre-learned optimal paths are executed, while keeping the real-time control system relatively simple.
Solution Approach 2:
The reinforcement learning agent serves as an intermediary that provides global overview capabilities without requiring the entire CNC system to become complex. The agent analyzes the complete machining process, considers future states, and generates optimized tool paths. This intermediary layer enables reduced machining time through intelligent planning while maintaining simplicity in the core control system that executes the generated paths.
Data Source
AI summary
Systems, devices, and methods including: receiving, by an interpreter component having a processor with addressable memory, a first state of a tool of a computer numerical control (CNC) machine; determining, by the interpreter component, a reward and a value of the reward based on the received first state, where the reward is at least one of: positive and negative; transmitting, by the interpreter component, a set of information comprising the determined reward and the value of the reward to an agent component; performing, by the agent component, at least one action to generate a tool path and to proceed to a second state, where the second state is combined with the first state; and determining, by the agent component, the generated tool path based on the determined reward and value associated with the at least one action.