Objectives

This project consists in a design methodology and cost-effective implementing solution for reverse engineering of complex-shaped objects using interlaced numeric and adaptive machining control.

Currently the interest is to find low-cost solutions replacing with sufficient accuracy the sophisticated mathematical CNC processors which describe the 3D complex surfaces or shapes, being then mapped to G-code by CNC post processors.

1. The first project objective consists in the study of the data acquisition system using the short range laser scanner. There are several possibilities of object inspection that are considered. It will be analyzed the performances of the laser scanner and its limitations. It will be developed a software simulation of the scanning process from multiple viewpoints, using a robotic arm for automatic orientation of the laser scanner.

2. The data acquisition system is composed by the laser scanner and the 6 d.o.f robotic arm. There will be designed and implemented command strategies based on raster patterns for the robotic manipulator for multiple viewpoints scanning and collision avoidance. The data noise removal, data thinning and registration is performed by an implemented software module. The next step consists in the 3D model reconstruction based on depth map images and processing them for surface machining.

3. Depth map images obtained by laser scanning acquisition techniques are used for generating the machining toolpaths. There will be designed algorithms for roughing and finishing machining toolpaths. The tool compensation is done by generating the depth map image and processing the depth map model of the object using the obtained structural model.

4. The obtained machining toolpaths must be optimized in order to reduce the tool wear and execution time, but also for assuring the quality of the machined surfaces. There will be implemented a strategy for roughing toolpaths of spiral type, with plunging number reduced and avoiding sharp angles for reducing the cutting force and off-machining movements time. For the finishing stage are used isoparametric adaptive curves generated in X, Y and Z plane. The finishing strategy will consider different sizes tools, for the details of the machined surface.

5. To optimize the production process it will be implemented an adaptive, autonomous and self-learning control system. There are two adaptive control strategies considered (Manual and Automatic). It will be elaborated the complete mathematical models of the milling process. There will be established the complete set of constraints associated to the milling regimes, tool types and machining technologies. It will be studied the adaptive techniques of the milling regimes for tool machine. There must be characterized the machine load and the strategies of adaptive constraints control.

6. There will be developed a knowledge-based, self-learning MAC (Manual Adaptive Control) fuzzy system based on sensors fusion and Observation-Prediction-Strategy techniques. It is necessary to implement a relational data-base with the components: materials, tools, technologies, regimes and constraints.

7. There will be determined the cost functions for machine productivity and tool life cycle characterizations. There will be developed and implemented methods used for real-time updating machining parameters is based on nonlinear, inequality constrained multivariable optimal control (ACO) and modelling of the cutting process.