Implementation of Real-Time Machining Process Control Based on Fuzzy Logic in a New STEP-NC Compatible System

Po Hu,Hongya Fu,Yunzhong Fu,Zhenyu Han

doi:10.1155/2016/9814973

Po Hu, Hongya Fu + Show 2 more

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https://doi.org/10.1155/2016/9814973

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Abstract

Implementing real-time machining process control at shop floor has great significance on raising the efficiency and quality of product manufacturing. A framework and implementation methods of real-time machining process control based on STEP-NC are presented in this paper. Data model compatible with ISO 14649 standard is built to transfer high-level real-time machining process control information between CAPP systems and CNC systems, in which EXPRESS language is used to define new STEP-NC entities. Methods for implementing real-time machining process control at shop floor are studied and realized on an open STEP-NC controller, which is developed using object-oriented, multithread, and shared memory technologies conjunctively. Cutting force at specific direction of machining feature in side mill is chosen to be controlled object, and a fuzzy control algorithm with self-adjusting factor is designed and embedded in the software CNC kernel of STEP-NC controller. Experiments are carried out to verify the proposed framework, STEP-NC data model, and implementation methods for real-time machining process control. The results of experiments prove that real-time machining process control tasks can be interpreted and executed correctly by the STEP-NC controller at shop floor, in which actual cutting force is kept around ideal value, whether axial cutting depth changes suddenly or continuously.

Highlights

Machining efficiency and quality of finished parts can be improved by monitoring, analyzing, and diagnosing the process of product manufacturing
This study has focused on the information exchanging mechanism, implementation method, and system scenario of real-time machining process control
The issue of exchanging high-level product manufacturing data between CAPP systems and CNC systems is solved by extending STEP-NC standard and building an open STEP-NC controller that interpret STEP-NC data directly at shop floor

Summary

Introduction

Machining efficiency and quality of finished parts can be improved by monitoring, analyzing, and diagnosing the process of product manufacturing. Artificial intelligent algorithms are usually used to build the relational model of machining parameters, cutting tool wear, and quality of finished part, with the purpose of developing machining process controllers that shorten machining time, prevent damage of tools, and improve quality of finished part. Li et al used back propagation neural network for multiobjective cutting parameters optimization in sculpture parts machining to increase surface quality [1]. Huang et al proposed a fuzzy control strategy based on constraint of spindle power in end milling process for reducing machining time of complex shape machining [2]. Zuperl et al proposed an adaptive control strategy based on neural network to maximize the feed rate subject to allowable cutting force on the milling tool [3]

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