The development of modern manufacturing requires key solutions to enhance the intelligence of manufacturing such as digitalization, real-time monitoring, or simulation techniques. For smart robotic manufacturing, the modern approach regarding robot programming and process planning aims for both high efficiency and energy-awareness. During the design and manufacturing stages, optimization becomes crucial and can be fulfilled by means of appropriate digital manufacturing tools. This paper presents the development of a Digital Twin for a robotic deburring workcell along with the process planning and robot programming. Considering a large size workpiece, a new robot programming solution was implemented, based on image processing to safely re-machine only areas where burrs could not be completely removed in the main deburring routine. The work also covers the development of a new web platform to remotely monitor the robotic workcell, to trigger alerts for unexpected events and to allow the control to authorized personnel enabled by the employment of robot web services following an architectural RESTful style which establishes a communication link to the robot virtual controller. The aim of this research is to integrate the Digital Twin with the innovative proposals of Industry 4.0, offering a project-based model of smart robotic manufacturing and experience concepts such as Cyber-Physical System, digitalization, data acquisition, continuous monitoring, and intelligent solutions in a novel approach. Furthermore, the work covers energy consumption strategies for energy-aware robotic manufacturing. Finally, the results of an energy-efficient motion planning along with signal-based scheduling optimization of the robotic deburring cell are discussed.

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The paper presents the works performed by the authors in the field of structural and functional optimization numerically controlled (NC) axes. The study includes two computing applications developed by second author of the paper in a PhD thesis related on NC axes’ structural and functional optimization. The first computing application is used for calculating the total reflected inertia of a linear motion NC axis (total inertial loads' reducing on the driving motor's shaft level). The second computing application is used for both preliminary selection of the driving servomotor (by checking first the accomplishment of the kinematic criterion) and a secondary selection of the electric motor (by checking in a second stage the accomplishment of the static and dynamic criterion). By mean of both software applications optimal matching of servomotor driving system with available NC axis mechanical structure may be determined. The analyzed linear motion NC axis is part of an experimental stand (existing in the MMS department from EMTS faculty), that supplementary to the driving servomotor, includes a belt drive transmission, a ball screw - bearings assembly and a driven element guided by ball rail system.