NC Program Optimization via Dynamic Feed Rate Adjustment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional CNC machining processes face challenges in accurately expressing the geometric structure of workpieces through NC programs, leading to inefficient tool paths and potential tool damage due to excessive machining forces, as existing methods lack scientific methods for adjusting machining conditions.
Innovation Solution
An automatic machining force optimizing system that acquires and modifies coordinate information from machine tool controllers to generate optimized NC programs by analyzing machining forces and adjusting processing feed rates based on tool, workpiece, and machine tool characteristics, thereby improving tool path accuracy and reducing tool damage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If engineers use conventional NC programs and process-aided software to plan tool paths, then the machining process can be executed, but the tool path cannot accurately express the geometric structure or contour of the workpiece, leading to poor machining precision
Solution Approach 1:
The patent replaces conventional process-aided software methods with a machine learning-based system that automatically generates optimized NC programs. The neural network model learns from historical machining data and workpiece geometric information to directly output optimized tool paths, substituting the traditional manual or software-based planning process with an intelligent automated system that preserves geometric accuracy while optimizing machining parameters
Solution Approach 2:
The system creates a digital copy or representation of the workpiece geometric structure through 3D modeling data, which is then fed into the machine learning model. This digital copy allows the system to understand and preserve the exact geometric contours and features of the workpiece, ensuring the generated tool path accurately follows the intended geometry without information loss
2Productivity
If engineers adjust machining conditions based on experience, then the process can be optimized, but excessive machining force is applied causing tool damage and increased costs
Solution Approach 1:
The system implements feedback by using historical machining data including tool life, machining forces, and process outcomes to train the machine learning model. The model continuously learns from past experiences and adjusts machining parameters accordingly, creating a closed-loop system where previous results inform future decisions, thereby optimizing the balance between productivity and tool durability
Solution Approach 2:
The machine learning model dynamically changes machining parameters such as feed rate, spindle speed, and depth of cut based on the specific workpiece geometry, material properties, and tool characteristics. Instead of using fixed or experience-based parameters, the system automatically adjusts these parameters to optimize the cutting process, reducing excessive forces that cause tool damage while maintaining high productivity
3Reliability
If conservative machining conditions are used to prevent tool damage, then tool durability is maintained, but processing efficiency deteriorates
Solution Approach 1:
The system transitions from static, conservative machining parameters to dynamic, adaptive parameters that change throughout the machining process. The machine learning model continuously adjusts feed rates and cutting depths based on real-time considerations of tool condition, workpiece geometry, and machining stage, allowing aggressive cutting where safe and conservative cutting when needed, thus maximizing both tool durability and processing efficiency
Data Source
AI summary
A system and a method for optimizing machining force of NC program is disclosed. The system includes a tool path acquisition unit and a NC program optimizing unit; the tool path acquisition unit is for acquiring a coordinate set of points composed by a coordinate information outputted by a controller, and modifying with respect to the coordinate set of points so as to form a tool path; the NC program optimizing unit is for analyzing machining force in accordance with the tool path, a tool information, a workpiece information and a machine tool characteristic information, and modifying with modified processing feed rates to generate an optimized NC program.