OCCAM

ITALIAN FUND FOR APPLIED SCIENCES (FISA) - DIRECTOR'S DECREE NO.1233 OF AUGUST 1, 2023

Within the framework of the OCCAM – Optimal Control for Computer-Aided Manufacturing project, Prof. Manolo Garabini serves as the Principal Investigator (PI). As this is a personal research project, in accordance with the provisions of the funding call, the budget is assigned directly to the Principal Investigator, who is responsible for identifying and selecting the institutions involved in the project, defining both the Host Institution and the Affiliation Institution. In this context, Eurosoft S.p.A. acts as the Host Institution, as defined by the funding program. As the hosting industrial partner, Eurosoft provides its application environment, technological expertise, infrastructure, and resources necessary for carrying out the research, development, and validation activities foreseen by the OCCAM project. The collaboration with Eurosoft also enables the development and validation of the project's innovative solutions in a real industrial environment, facilitating technology transfer and maximizing their impact on the manufacturing sector. The University of Pisa, through the Enrico Piaggio Research Center and the Department of Information Engineering, to which Prof. Garabini is affiliated, participates in the project as the Affiliation Institution. These structures contribute to the scientific activities of the project by providing advanced research expertise, methodological support, and scientific validation of the results, ensuring the necessary connection with the academic community and guaranteeing the scientific rigor of the activities carried out within the OCCAM project.

Goal

The OCCAM (Optimal Control for Computer-Aided Manufacturing) project was conceived to significantly increase productivity, energy efficiency, and process quality in Computer Numerical Control (CNC) machines by overcoming the limitations of current motion planning and control systems, which are predominantly based on heuristic approaches. Although modern machine tools, laser and plasma cutting systems, and woodworking machines offer outstanding dynamic performance in terms of speed, acceleration, and precision, their full potential is still only partially exploited. This is mainly due to the use of trajectory generation algorithms that do not allow machine capabilities to be utilized optimally while simultaneously ensuring compliance with kinematic constraints and the quality requirements of the manufactured product. OCCAM aims to develop a new generation of motion planning and control algorithms based on advanced mathematical optimization techniques. These algorithms will be capable of generating optimal trajectories that minimize machining time, reduce vibrations and energy consumption, and simultaneously guarantee compliance with the speed, acceleration, jerk, and geometric accuracy constraints required by the manufacturing process. The project will apply these methodologies to several strategic industrial sectors within both the Italian and international manufacturing industries, including laser and plasma cutting machines for tubes and metal profiles, as well as CNC woodworking machines. The objective is to achieve measurable productivity gains, improve machining quality, and reduce programming and machine setup times.

Innovation

The highly innovative aspect of the OCCAM project lies in the introduction, within the machine tool and Computer-Aided Manufacturing (CAM) sectors, of motion planning and control methodologies based on mathematical models and advanced optimization techniques derived from the latest developments in robotics and intelligent automation. Unlike the solutions commonly adopted by industry today, which are based on heuristic rules and parameterization procedures requiring extensive application experience and frequent tuning activities by specialized technicians, OCCAM will develop algorithms capable of automatically determining optimal machine trajectories while rigorously ensuring compliance with the physical limits of the machine axes and the geometric tolerances required by the manufacturing process. The proposed approach will make it possible to transform the machine's kinematic characteristics—such as speed, acceleration, jerk, and energy constraints—into parameters that can be directly utilized by optimization algorithms. This will significantly reduce empirical tuning activities, making system behavior more predictable, repeatable, and easily transferable across different machines. A further innovative element of OCCAM will be the integration of advanced Model Predictive Control (MPC) techniques with trajectory optimization algorithms. This approach will enable the development of a unified framework capable not only of generating optimal trajectories while respecting the machine's kinematic and dynamic constraints, but also of adapting machine behavior in real time according to variations in the production process and operating conditions. By anticipating system evolution and explicitly managing constraints related to speed, acceleration, and jerk, predictive control will contribute to simultaneous improvements in productivity, precision, and process robustness. The adoption of the solutions developed within the OCCAM project will significantly reduce cycle times, decrease mechanical stress and vibrations, increase machining accuracy, and improve the overall energy efficiency of manufacturing systems. In this way, the project will make a tangible contribution to enhancing the competitiveness and sustainability of the Italian manufacturing industry.