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Updated: Jun 13, 2026

Operation of the Collaborative Composite Manufacturing (CCM) System

Operation of the Collaborative Composite Manufacturing (CCM) System

Published on: October 1, 2019

Adaptive error compensation in CNC turning based on deep reinforcement learning and genetic algorithm fusion.

Huaize Pan^1,2, Yuexia Lv^3,4, Wenfeng Bai⁵

¹School of Mechanical Engineering, Shandong Key Laboratory of CNC Machine Tool Functional Components, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250353, Shandong, China.

Scientific Reports

|June 11, 2026

Summary

This study introduces an adaptive error compensation framework using deep reinforcement learning (DRL) and genetic algorithms (GA) for CNC turning. The coupled approach significantly improves precision manufacturing accuracy and convergence speed.

Keywords:

Adaptive control CNC turning Deep reinforcement learning Error compensation Genetic algorithm Precision manufacturing

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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

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Published on: October 14, 2017

Area of Science:

Manufacturing Engineering
Control Systems
Machine Learning

Background:

CNC turning operations suffer from complex errors like thermal deformation, tool wear, and force deflections.
Existing error compensation methods often treat predictive and adaptive techniques separately, limiting real-time performance.

Purpose of the Study:

To develop and validate an adaptive error compensation framework for CNC turning.
To integrate deep reinforcement learning (DRL) and genetic algorithms (GA) in a bidirectional loop for enhanced precision manufacturing.

Main Methods:

A coupled DRL and GA framework was implemented, where GA optimizes hyperparameters and reward weights, while DRL performs real-time adaptive control.
The approach was validated on aerospace-grade Ti-6Al-4V turning operations.

Main Results:

Achieved a mean absolute error of 2.6 μm, demonstrating 86.3% compensation effectiveness.
Showcased 38% faster convergence compared to standalone DRL and a process capability index of 1.67.
Outperformed BPNN, LSTM, and PSO-based predictive compensation baselines across multiple metrics.

Conclusions:

The proposed DRL-GA coupled framework offers superior performance in CNC turning error compensation.
The method shows limitations with significant parameter drifts or material variations, suggesting future work in transfer learning.