Neural Network Machining Parameter Generation
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Solution Overview
Problem
Existing machining programs require extensive trial and error and rely on engineer experience, leading to inefficiencies and increased costs when processing complex geometric shapes or new materials, as parameters like speed and feed are determined based on experience rather than data-driven methods.
Innovation Solution
An automatic machining parameter generation system utilizing machine learning, comprising a geometric data capturing module, feature recognition learning network, and machining parameter learning network, which extracts and optimizes machining parameters from existing data to improve planning and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machining parameters are determined based on engineer experience and reference data, then the machining process can be initiated, but multiple trials are needed to obtain appropriate parameters, leading to increased time and cost
Solution Approach 1:
The patent replaces the mechanical trial-and-error process with a machine learning-based automated system. The neural network model processes geometric features of workpieces and predicts optimal machining parameters directly, eliminating the need for repeated physical trials and engineer intuition-based adjustments.
Solution Approach 2:
The system enables self-service by automatically generating optimized machining parameters without requiring engineer intervention. The machine learning model autonomously processes input data (geometric features), learns from training data, and outputs optimized parameters, making the system self-sufficient for parameter determination.
2Manufacturing precision
If multiple trials are conducted to obtain appropriate machining parameters, then more accurate parameters can be found, but the process becomes time-consuming and costly
Solution Approach 1:
The patent substitutes repeated mechanical trials with a single machine learning inference. The neural network model, trained on comprehensive machining data, provides accurate parameter predictions in one calculation, achieving both high precision and time efficiency simultaneously.
Solution Approach 2:
The system performs preliminary action by pre-training the neural network model on extensive machining data before actual use. This pre-training phase captures optimal parameter relationships, allowing the model to provide accurate predictions during actual machining without requiring trial-and-error during production.
3Extent of automation
If existing machining information and artificially generated data are used for training, then a machine learning model can be developed, but the complexity of data processing and model training increases
Solution Approach 1:
The patent applies segmentation by dividing the system into distinct functional modules: geometric data capturing module, feature recognition learning network, and machining parameter learning network. Each module handles a specific task, making the overall complex system manageable and easier to implement through modular architecture.
Solution Approach 2:
The feature recognition learning network acts as an intermediary between the geometric data capturing module and the machining parameter learning network. It processes and extracts relevant features from geometric data, simplifying the input for the parameter prediction model and reducing the overall system complexity.
Data Source
AI summary
A machining parameter automatic generation system includes a geometric data capturing module, a feature recognition learning network and a machining parameter learning network. The geometric data capturing module captures a geometric shape of a workpiece to generate a candidate feature list. The feature recognition learning network trains the candidate feature list according to a first neural network model to obtain an applicable feature list. The machining parameter learning network trains the applicable feature list and the candidate machining parameter according to a second neural network model to obtain an applicable machining parameter. The applicable machining parameter is used to generate a machining program, and the machining program is read by a machine tool for processing.