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This paper presents a deployment method of various test maneuver scenarios for 2 degree of freedom (2 DoF) vehicle simulator by using feature extraction and neural networks (NN). A prototype version has been set up for the 2 DoF vehicle simulator. Then, a hardware in the loop (HIL) model with 2 inputs (torque, $\tau _{1}$ - $\tau _{2}$ ) and 3 outputs (acceleration, $\text{a}_{\mathrm {x}}$ - $\text{a}_{\mathrm {y}}$ - $\text{a}_{\mathrm {z}}$ ) is created. System identification is performed to obtain the training data of NNs to be used for the deployment of test maneuvers. In the system identification process, 2 arbitrary sinusoidal torque signals ( $\tau _{1}$ - $\tau _{2}$ ) are generated by using the actuator specs of the 2 DoF vehicle simulator. By applying the generated torque signals to the actuators, acceleration ( $\text{a}_{\mathrm {x}}$ - $\text{a}_{\mathrm {y}}$ - $\text{a}_{\mathrm {z}}$ ) data are collected from the inertial measurement sensor (IMU) on the 2 DoF vehicle simulator. It is determined to create 3 different NN models for the obtained data. The $1^{\mathrm{st}}$ NN model is trained with 3 inputs ( $\text{a}_{\mathrm {x}}$ - $\text{a}_{\mathrm {y}}$ - $\text{a}_{\mathrm {z}}$ ) and 2 targets ( $\tau _{1}$ - $\tau _{2}$ ) training data. The $2^{\mathrm{nd}}$ NN model is trained with 6 inputs (amplitudes and phases of $\text{a}_{\mathrm {x}}$ - $\text{a}_{\mathrm {y}}$ - $\text{a}_{\mathrm {z}}$ ) and 2 targets ( $\tau _{1}$ - $\tau _{2}$ ) training data. The input data features for the 2nd NN model is extracted by using the Fast Fourier Transform (FFT). The $3^{\mathrm{rd}}$ NN model is trained with 6 inputs (amplitudes and phases of $\text{a}_{\mathrm {x}}$ - $\text{a}_{\mathrm {y}}$ - $\text{a}_{\mathrm {z}}$ ) and 4 targets (amplitudes and phases of $\tau _{1}$ - $\tau _{2}$ ) training data. For the 3rd NN model, the features of input and target data are extracted by using the FFT. The NN training process continues until acceptable performance criteria are reached. Then, 3 NN models are run and analysed under various test scenarios such as Double Lane Change, Constant Radius, Increase Steer, Fish Hook, Sine with Dwell and Swept Sine. Only for the $3^{\mathrm{rd}}$ NN, the actuator signals ( $\tau _{1}$ - $\tau _{2}$ ) are recomposed by applying an inverse FFT process to the 4 targets (amplitudes and phases of $\tau _{1}$ - $\tau _{2}$ ). Finally, the reference trajectory tracking performances are evaluated by comparing the NN models that are run under the test scenarios.

In this study, the focus is on an implant used in the treatment of early-onset scoliosis called magnetically controlled growing rods (MCGR). The primary goal of the study is to address and propose solutions for the mechanical problems reported in the literature concerning MCGR. The problems of the MCGR are mainly due to excessive stress and mechanical bearing problems. Therefore, an MCGR removed from a patient is teardown and geometrically modeled. Then, eleven design parameters are determined on the MCGR for the mechanical problems experienced and these are evaluated by mechanical analysis over 14 control points. In this study, analysis processes are carried out with L12 orthogonal array for eleven design parameters and 2 levels using Taguchi’s experimental design method (DoE). With the obtained data by analyzing the experiments in L12, the fitness functions depending on the design parameters are created for 14 control points. Since the problem is multi-objective, a non-dominated sorting genetic algorithm (NSGA II) and multi-objective particle swarm optimization (MOPSO) are used to minimize stress and displacement in existing mechanical problems using fitness functions. The obtained design models from NSGA II and MOPSO are analyzed and evaluated in comparison with the existing mechanical model obtained through pre-optimization teardown study of MCGR.