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This paper presents an innovative convolutional neural network (CNN) architecture for the parametric identification of 18 critical dynamic model parameters in a three-linear-axis Cartesian robot. The dynamic model is obtained by the Euler\u2013Lagrange motion equations from an analysis of lumped parameters such as friction, inertia, mass and stiffness of the design of a three-axis linear Cartesian robot. The proposed CNN architecture is trained and validated using experimental data, achieving a parametric identification accuracy of approximately 98%. The identified parameters are further applied to optimize the robot\u2019s dynamic response, demonstrating improved trajectory tracking and reduced energy consumption in manufacturing tasks. 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