ML-Driven CNC Control for Sensor-Adaptive Robotic Machining
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Solution Overview
Problem
Traditional manufacturing methods are impractical for complex processes due to the need for numerous decisions and are often hindered by a lack of skilled labor, leading to idle resources and inefficiencies.
Innovation Solution
A modular robotic apparatus with sensors and a machine learning module that generates CNC configurations based on manufacturing parameters, enabling autonomous operation and continuous improvement of manufacturing processes, reducing dependence on human labor and enhancing resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional manufacturing methods are used for complex processes, then human decision-making is required to select between different manufacturing objectives, but the process becomes impractical due to the large number of decisions required and lack of skilled labor
Solution Approach 1:
The machine learning model enables the manufacturing system to automatically make decisions about manufacturing objectives and parameters without human intervention. The system trains the model on historical manufacturing data, and the model independently selects optimal manufacturing objectives and adjusts parameters based on sensor feedback from the robotic apparatus.
Solution Approach 2:
The patent replaces human decision-making (mechanical/cognitive system) with an artificial intelligence system. The machine learning model processes sensor data and generates CNC configurations, substituting the need for skilled human operators to make manufacturing decisions.
2Productivity
If traditional manufacturing methods are used, then skilled labor is required to make manufacturing decisions, but manufacturing jobs remain idle due to lack of skilled labor
Solution Approach 1:
The robotic apparatus with machine learning capability operates autonomously, making its own decisions about manufacturing parameters and objectives. This eliminates the need for skilled human operators while maintaining high productivity and continuous operation of manufacturing equipment.
Solution Approach 2:
The machine learning model dynamically adjusts manufacturing parameters based on sensor feedback and learned patterns from training data. This enables the system to optimize productivity while operating with simplified controls that do not require skilled human intervention.
3Manufacturing precision
If machine learning model adjusts CNC configuration based on sensor signals, then manufacturing accuracy is improved, but system complexity increases
Solution Approach 1:
The machine learning model continuously receives sensor signals from the robotic apparatus during manufacturing operations and adjusts CNC configurations in real-time based on this feedback. This closed-loop control improves manufacturing accuracy by correcting deviations from desired outcomes while maintaining manageable system complexity through incremental learning.
Solution Approach 2:
The machine learning model is trained in advance on historical manufacturing data and parameters before deployment. This preliminary training equips the model with knowledge to make accurate decisions during actual manufacturing operations, improving precision without requiring complex real-time computation during production.
Data Source
AI summary
A modular robotic apparatus includes one or more sensors configured to generate sensor signals representing a manufacturing environment in which the modular robotic apparatus is located. A machine learning module is communicably coupled to the one or more sensors and includes a computer processor. The computer processor generates, by a machine learning model trained based on one or more manufacturing parameters, a computer numerical control (CNC) configuration. The one or more manufacturing parameters define a manufacturing task to be performed by the modular robotic apparatus. The machine learning model adjusts the CNC configuration based on the sensor signals. A robotic machine tool is communicably coupled to the machine learning module and includes an end effector. The robotic machine tool is configured to operate the end effector in accordance with the adjusted CNC configuration.