• Journal of the Korean Society for Precision Engineering
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• v.30 no.9
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• pp.915-921
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• 2013

Most of large scale solar panel handling robots adopt the timing-belt drive system for its driveline because of the simplicity and the easiness of implementation. The vibration caused by the flexure of the timing belt would increase as the size and the weight of the panel that the robot handles increase and the vibration would deteriorate the precision and/or productivity of the whole robot system. For the development of a proper control system and for the improvement of the design of the robot it is important to estimate the oscillatory response of the robot system including the flexible drive system properly. In this paper a flexible multi-body dynamics model of a large-scale solar-panel-handling robot with the flexible timing-belt drive system is developed using a generic multi-body dynamics analysis program, RecurDyn.

• Composites Research
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• v.35 no.5
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• pp.303-308
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• 2022

PIC (Piecewise Integrated Composite) is a new concept for designing a composite structure with mosaically assigning various types of stacking sequences in order to improve mechanical properties of laminated composites. Also, machine learning is a sub-category of artificial intelligence, that refers to the process by which computers develop the ability to continuously learn from and make predictions based on data, then make adjustments without further programming. In the present study, the tapered box beam type PIC robot arm for carrying and transferring wide and thin LCD display was designed based on the machine learning in order to increase structural stiffness. Essential training data were collected from the reference elements, which were intentionally designated elements among finite element models, during preliminary FE analysis. Additionally, triaxiality values for each finite element were obtained for judging the dominant external loading type, such as tensile, compressive or shear. Training and evaluating machine learning model were conducted using the training data and loading types of elements were predicted in case the level accuracy was fulfilled. Three types of stacking sequences, which were to be known as robust toward specific loading types, were mosaically assigned to the PIC robot arm. Henceforth, the bending type FE analysis was carried out and its result claimed that the PIC robot arm showed increased stiffness compared to conventional uni-stacking sequence type composite robot arm.