A real-time, adaptive control and optimization system for laser flame cutting of thick plates of mild steel has been developed. The proposed system consists of two subsystems: a process monitoring system and a control and optimization system. The first subsystem aims at the on-line observation of the process status and corresponding cut quality. Since the different cut quality characteristics (e.g. cutting edge striations or dross) cannot be measured directly, the proposed system is based on so-called sensing parameters, which are easily-observable physical parameters that correlate well with the quality characteristics of the cut surface. The applicability of different optical sensors (photodiodes and a NIR-camera) has been investigated. The most optimal configuration of the process monitoring system is presented, including an overview of the selected set of sensing parameters.
The second subsystem, the real-time control and optimization system, supports the adaptation of the process parameters, based on the cut quality information obtained from the process monitoring system. A suitable hardware configuration for the real-time control and optimization is presented, starting from the original platform (i.e. an industrial 2D laser cutting machine). A generic expert strategy, has been designed for the control and optimization purpose. Using the developed experimental platform, the performance of the expert system was verified and optimized for different material-thickness combinations.
The obtained results demonstrate the effectiveness of the chosen approach in terms of increased autonomy, productivity, and efficiency of the process, as well as elimination of the need for manual quality control and the possibility to automatically generate quality reports.