Tool-path continuity determination based on machine learning method

Computer-aided manufacturing (CAM) software outputs machining data by encoding a tool-path into a series of G-codes which are composed of various lengths of line segments. The discontinuities of these line segments may cause inefficiency for computer numerical control (CNC) system. To achieve high-s...

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Bibliographic Details
Published in:International journal of advanced manufacturing technology 2022-03, Vol.119 (1-2), p.403-420
Main Authors: Zhou, Bo, Tian, Tongtong, Zhao, Jibin, Liu, Dianhai
Format: Article
Language:English
Subjects:
Acceleration
Accuracy
Advanced manufacturing technologies
Algorithms
CAE) and Design
CAI
CAM
Classification
Codes
Computer aided manufacturing
Computer assisted instruction
Computer-Aided Engineering (CAD
Continuity (mathematics)
Corners
Deceleration
Engineering
Feature extraction
G codes
Industrial and Production Engineering
Interpolation
Machine learning
Machining
Manufacturing
Mechanical Engineering
Media Management
Neural networks
Numerical controls
Original Article
Process parameters
Real time
Sampling
Segments
Smoothing
Support vector machines
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Summary:Computer-aided manufacturing (CAM) software outputs machining data by encoding a tool-path into a series of G-codes which are composed of various lengths of line segments. The discontinuities of these line segments may cause inefficiency for computer numerical control (CNC) system. To achieve high-speed continuous motions, corner smoothing algorithms based on look-ahead methods are widely used. However, it is difficult to meet smoothing trajectories in real-time requirements. Based on machine learning method, in this paper, a support vector machine (SVM) system is presented for directly outputting classification results of the various geometric continuities at the transition corners. The feature values used for generating continuity classification model are extracted from sampling paths of the previous publication work: the machining parameters, length, fairness criteria, the root mean square (RMS) contour errors, and dominant stage type of movement of each sampling path are calculated. The acceleration/deceleration (ACC/DEC) feedrate planning scheme is used to determine the feedrate at the transition corners. Simulations and experiments show that the proposed algorithm can realize accurately and efficiently continuity classification in real-time requirements under the conditions of machining accuracy.
ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-021-08156-2