Machining Data Feedback Loop for Lower Material Consumption
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Solution Overview
Problem
The challenge in manufacturing is high energy and material consumption, with existing systems struggling to efficiently reduce material consumption while maintaining product quality through digital twin integration.
Innovation Solution
A machining data processing system and method that includes a data design system to determine target machine tools and initial process parameters, a monitoring system for real-time data analysis, and a quality inspection system to update process parameters based on quality inspection results, facilitating smoother and more accurate machining processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If traditional machining processes are used, then material consumption is high, but implementing digital twin integration and real-time monitoring increases system complexity
Solution Approach 1:
The patent creates a digital twin (virtual copy) of the physical machining system that simulates and optimizes machining processes before actual execution. This digital model allows for predicting optimal process parameters and reducing material consumption without requiring complex physical modifications to the actual machining equipment.
Solution Approach 2:
The system performs preliminary simulations and optimizations in the digital twin environment before actual machining operations. By pre-determining optimal process parameters through digital simulation, the system reduces material consumption in actual machining without requiring complex real-time adjustments during the physical machining process.
2Productivity
If digital twin and real-time monitoring are implemented, then machining efficiency improves, but the extent of automation and system complexity increases
Solution Approach 1:
The patent implements a feedback mechanism where the digital twin continuously receives actual machining data from sensors and compares it with simulated data. This feedback loop enables real-time optimization of process parameters, improving machining efficiency while maintaining a balanced level of automation through iterative adjustments rather than fully autonomous operation.
3Manufacturing precision
If process parameters are optimized through iterative adjustments, then manufacturing precision improves, but the time required for multiple iterations increases
Solution Approach 1:
The digital twin performs preliminary simulations to predict optimal process parameters before actual machining begins. This pre-optimization reduces the number of iterative adjustments needed during actual machining, thereby improving manufacturing precision while minimizing the time lost to iterations.
Solution Approach 2:
The patent replaces physical trial-and-error iterations with virtual simulations in the digital twin environment. By substituting mechanical/physical iteration cycles with computational simulations, the system can evaluate multiple parameter combinations rapidly without the time cost of actual machining iterations, thus improving precision efficiently.
Data Source
AI summary
The application provides a machining data processing system and a machining data processing method. The machining data processing system includes: a data design system, a monitoring system and a quality inspection system, the data design system configured to determine a target machine tool and initial process parameters corresponding to a test project according to a quality requirements of the test project; the monitoring system configured to receive machining process data and machining target data, to generate process adjustment parameters based on the machining process data and the machining target data, a quality inspection system configured to receive quality inspection result of the machine product, and determine whether to transmit the process adjustment parameters to the data design system based on the product quality inspection result, to update the process parameters in the machining process parameter library in the data design system.