In this study, a Back Propagation (BP) neural network algorithm based on Genetic Algorithm (GA) optimization is proposed to plan and optimize the trajectory of a redundant robotic arm for the upper limb rehabilitation of patients. The feasibility of the trajectory was verified by numerical simulations. First, the collected dataset was used to train the BP neural network optimized by the GA. Subsequently, the critical points designated by the rehabilitation physician for the upper limb rehabilitation were used as interpolation points for cubic B−spline interpolation to plan the motion trajectory. The GA optimized the planned trajectory with the goal of time minimization, and the feasibility of the optimized trajectory was analyzed with MATLAB simulations. The planned trajectory was smooth and continuous. There was no abrupt change in location or speed. Finally, simulations revealed that the optimized trajectory reduced the motion time and increased the motion speed between two adjacent critical points which improved the rehabilitation effect and can be applied to patients with different needs, which has high application value.

/pdf/trajectory-planning-and-simulation-study-of-redundant-1v604szt.pdf

Trajectory Planning and Simulation Study of Redundant Robotic Arm for Upper Limb Rehabilitation Based on Back Propagation Neural Network and Genetic Algorithm

This paper presents a fully coupled three-dimensional finite element model for the simulation of a tube manufacturing process consisting of roll forming and high-frequency induction welding. The multiphysics model is based on the dual mesh method. Thus, the electromagnetic field, the temperature field, the elasto-plastic deformation of the weld bead, and the phase transformations within the material can be simulated for a moving tube without remeshing. A comparison with measurements shows that the geometry of the welded tube and the weld bead, the force on the squeeze rolls, the temperature along the band edges, and the hardness distribution within the heat-affected zone can be simulated realistically.

https://www.mdpi.com/1996-1944/15/3/1270/pdf?version=1644397931

Numerical Simulation of Tube Manufacturing Consisting of Roll Forming and High-Frequency Induction Welding

Critical conditions for melting of metallic wire in induction heating system through numerical simulation and experiments

In this paper, a process parameter optimization method of induction heating temperature field was proposed, in which finite element simulation as data supply, back-propagation (BP) neural network as data prediction, and non-dominated sorting genetic algorithm II (NSGA-II) as data optimization. Firstly, the electromagnetic-thermal coupling analysis in COMSOL was used to calculate the temperature field of the welding interface of electromagnetic induction welding of CF/PEEK composites, and the temperature data of each point of the welding interface was obtained. Then, the BP neural network model based on the L-M algorithm was established to predict the temperature field of the welding interface, and train and test the established neural network model, got a prediction model that reasonably reveals the welding process parameters and the temperature field relationship of the welding interface. Then, the multi-objective optimization algorithm NSGA-II was used to optimize the design of welding process parameters, and according to the thermal properties of polyetheretherketone (PEEK) resin, based on the crowding calculation and combined with the priority solution analysis method, the priority solution non-dominated sorting genetic algorithm (pNSGA-II)was proposed. Finally, a set of process parameters was obtained that satisfies the optimal heating effect of the weld interface. 

Optimization of process parameters for induction welding of composite materials based on NSGA-II and BP neural network

The use of thermoplastic composites in aerospace structures is interesting given they can be assembled using fastener-free assembly methodologies, such as induction welding. In induction welding, the thermoplastic resin is locally melted at the interface between two parts and resolidified to form a cohesive bond between the components. Depending on the stacking sequence of the composites, and importantly the layer orientation at the interface, the volumetric heat generation can vary significantly for a given coil geometry. Therefore, to maximize the volumetric heat generated one can optimize the stacking sequence instead of altering process parameters like current and frequency settings. Another option to maximize the volumetric heat generation is to optimize the coil geometry. This research solely focuses on stacking sequence optimization to improve volumetric heat generation for induction welding of thermoplastic composites using genetic algorithms. The optimized laminates showed significant improvements in volumetric heat generation compared to a baseline quasi-isotropic laminate. The volumetric heat increased by a factor of 9 and 13 when the layer orientation was allowed to vary with a step size of 45° and 15°, respectively. Verification of the software with experiments and comparison with other computational tools will be done next. 

Chunyang Shi

Papers

Research on the optimization of welding parameters in high-frequency induction welding pipeline