Sensitivity analysis of truck structure based on CA algorithm

With the continuous improvement of modern CAE technology, structural reanalysis algorithm has gradually come into people’s vision and developed rapidly. The structure reanalysis algorithm introduced in this paper is an accelerated calculation method. The core idea of this algorithm is to avoid the complete analytical calculations after the structure modification, and reduce the calculation scale, save the calculation time, improve the efficiency of CAE simulation effectively on the premise of meeting the requirements of structure accuracy. The objective of this paper that is based on the initial third-order modal information of the truck structure is to control the overall quality of the structure. And it has important guiding significance for practical production. In this paper, different design variables are set in combination with the structural reanalysis algorithm. While the parameters of design variables are modified, sensitivity information analysis and Taylor expansion theorem are used to verify the feasibility and accuracy of the structural reanalysis method in optimal calculation

A New Smart Web Platform for Plastic Injection Molds in Industry 4.0 Environments

This paper presents a new smart web platform for plastic injection molds for use in industry 4.0 environments. The new platform requires as its only input the CAD model of the plastic part in a discrete format, the accuracy of the analysis, the thermoplastic material of which the part will be manufactured and the number of parts to manufacture per year. Using this information and through a fully automated process based on hybrid algorithms developed by the authors the smart platform generates an extended CAD model of the mold with additional expert information useful for industry 4.0 environments. In this way, it is possible to design a mold with uniform heat transfer, balanced ejection and a uniform filling phase of the mold cavity. The presented platform differ from other applications for mold designing in that the resulting mold meets all the geometric, functional and technological requirements of mold designing without needing CAE simulation software for its validation. The presented platform is considered as the first smart platform that does not require the interaction of the designer in the process of dimensioning and designing the different subsystems that compound the mold, being a tool to reduce time and costs in the initial phases of plastic part design and with the ability to integrate into a flexible manufacturing environment 4.0.

Joint Integrity and Strength CAE Simulation Methodology for PHEV Vehicle Skid Plate Assembly

Battery unit skid plate Joint integrity and Strength are key design attributes to ensure Functionality of vehicle. This paper provides an overview of simulation methodology to predict joint integrity and Strength of Battery unit skid plate. Bolt Joint integrity checked against the maximum vertical each wheel spindle loads, which captures maximum bending and twisting. This is the condition in which bolts can experience high load and bolt slippage should check against this load. Skid plate experiences different loading based on drivability and road profile. To simulate severe off road event condition, Maximum load which skid plate experience, applied, as Point load on skid plate at various critical locations to find the deflection and to make sure skid plate is strong enough to protect underneath battery unit.

Prediction of Polymer Flow Length by Coupling Finite Element Simulation with Artificial Neural Network

In this study, computer-aided engineering (CAE) simulation software and the design of experiments (DOE) method were used to simulate the injection molding process in terms of the melt flow length, using a spiral part. Process parameters such as melt temperature, mold temperature, injection pressure and mold cavity thickness were considered as injection molding variables. A predictive model for the flow length was created using a three-layer artificial neural network (ANN). The ANN model was trained with both simulation and experimental data, and the predictive performances were compared in terms of correlation coefficient, root mean square error and mean relative error. The cavity thickness and melt temperature were found to be the most significant factors for both the simulation and the experiment, while the injection pressure and the mold temperature had little effect on the flow length. The ANN model trained with Moldex3D data shows a significantly higher prediction capacity than the ANN model trained with experimental data. However, the melt flow lengths predicted by the ANN model for both Moldex3D and Moldflow simulation data are statistically significant, indicating that the proposed prediction methodology, which combines the ANN model, DOE method and the CAE simulation technology, can effectively predict the flow length of injection molded parts, with a small number of data.

Inverse Kinematic Simulation of Industrial Robotic Manipulators Using RecurDyn and Matlab with Experiment Verification

The simulation and application of industrial robots has developed very quickly in recent decades. Along with the development of computer science, a lot of softwares to perform dynamic simulation have been created. The results of simulation can be used for layout evaluation, kinematic, dynamic study, off-line programming to avoid obstacle and for design mechanical structure of robots. A co-simulation of 2R industrial robots have been performed by Recurdyn and Matlab. The input parameters are executed under Matlab, and then exported to Recurdyn environment. Kinematic parameters will be executed by RecurDyn then exported to Matlab. The main tasks of this paper are performing 2R robotic manipulator kinematic simulation in two postures with the same trajectory and the same time. Thus, the result of simulation can be compared with theories. Finally, a real 2R robot model was used to verify the trajectory with CAE simulation.