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Go to Editorial ManagerFused deposition modeling (FDM) is a commonly used 3D printing technique that involves heating, extruding, and depositing thermoplastic polymer filaments. The quality of FDM components is greatly influenced by the chosen processing settings. In this study, the Taguchi technique and artificial neural network were employed to predict the ultimate tensile strength of FDM components and establish a mathematical model. The mechanical properties of ABS were analyzed by varying parameters such as layer thickness, printing speed, direction angle, number of parameters, and nozzle temperature at five different levels. FDM 3D printers were used to fabricate samples for testing, following the ASTM-D638 standards, using the Taguchi orthogonal array experimental design method to set the process parameters. The results indicated that the printing process factors had a significant impact on tensile strength, with test values ranging from 31 to 38 MPa. The neural network achieved a maximum error of 5.518% when predicting tensile strength values, while the analytical model exhibited an error of 19.376%.
This paper presents the design of robust four parameters (two degree of freedom) PI-PD controller based on Kharitonov theorem for antilock braking system. The Particle Swarm Optimization (PSO) method is used to tune the parameters of the proposed controller based on Kharitonov theorem to achieve the robustness over a wide range of system parameters change. The proposed cost function combines the time response specifications represented by the model reference and the frequency response specifications represented by gain margin and phase margin and the control signal specifications. The model reference control is used because of the antilock braking system is originally nonlinear and has different operating points. The robust stability is guaranteed by applying the Kharitonov theorem. Three types of road conditions (dry asphalt, gravel and icy) are used to test the proposed controller.