Abstract
The future of machining lies in the fully autonomous machine tool. New technologies must be developed that predict, sense and action intelligent decisions autonomously. Digital twins are one component on this journey and are already having significant impact in the manufacturing industries. Despite this, the implementation of machining Digital Twins has been slow due to the computational burden of simulating cutting forces online resulting in no commercially available Digital Twin that can automatically control the machining process in real time. Addressing this problem, this research presents a machining Digital Twin capable of real-time adaptive control of intelligent machining operations. The computational bottleneck of calculating cutter workpiece engagements online has been overcome using a novel method which combines a priori calculation with real-time tool centre point position data. For the first time, a novel online machine-induced residual stress control system is presented which integrates real-time model-based simulations with online feedback for closed loop residual stress control. Autonomous Digital Twin technologies presented also include chatter prediction and control and adaptive feed rate control. The proposed machining Digital Twin system has been implemented on a large-scale CNC machine tool designed for high-speed machining of aerostructure parts. Validation case studies have been conducted and are presented for each of the machining Digital Twin applications.
Highlights
The digital transformation from legacy systems of computer numerically controlled (CNC) machine tools to Industry 4.0 within the machining sector is crucial to achieving higher productivity, reducing costs and working towards circular economies
This paper presents a real-time machining Digital Twin for autonomous closed loop control applications
Based on the research and results presented in this paper, the following conclusions can be drawn
Summary
The digital transformation from legacy systems of CNC machine tools to Industry 4.0 within the machining sector is crucial to achieving higher productivity, reducing costs and working towards circular economies. A Digital Twin for machining is a real-time digital replica of the process; within machining this comprises real-time data, datadriven digital models or model-based simulations updated in real-time with information from the computer numerically controlled (CNC) machining centre. By virtue of the real-time simulations, enable users to access process information without the requirement for a full suite of sensors. The use of “virtual sensors” results in an ability to add intelligent machining capabilities to an existing machining centre without the need for installation of. This research presents Digital Twin capabilities for machining, which include a novel machining-induced residual stress (MIRS) control, chatter detection and control and adaptive feed rate control. A brief survey on the state of the art of these functions is hereby presented
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