Abstract: The Park System Assembly provides a means to immobilize a vehicle equipped with an automatic/Hybrid transmission or Electric Drive unit when the “Park” position is selected manually/hydraulically or by an electric motor. The immobilization of the transmission is achieved by engaging the “Park Pawl” into the “Park Gear,” which in turn grounds the output shaft and the vehicle. Being a safety device, the Park System should allow the driver to engage the shift lever into the “Park” position when the vehicle is rolling within a range of low speed. The max speed of the gear/vehicle at which the pawl can lock the gear is called the Speed of Engagement (SOE) of the gear/Vehicle. The Park System should allow the components to ratchet when the vehicle is rolling at speed above the engagement speed. When the vehicle reaches the engagement speed, the “Park Pawl” should engage the “Park Gear” and immobilize the vehicle. All this should take place without any component failure. The Abaqus/Explicit predicts the park system speed of engagement, ratcheting conditions, and actuator spring force values.
The analyst may perform DOE (Design Of Experiments) studies to optimize the park system design for SOE targets. The analyst needs to post-process many output database files (odbs) during this process. This paper describes the development of an automation script using the Abaqus scripting interface (for Python). Manually, the Speed of engagement and ratcheting conditions are determined by overlapping the gear and vehicle angular speed Vs. Time graphs. By visualizing the chart’s data points, SOE is identified where vehicle/gear speed becomes zero. In the DOE optimization process, the analyst needs to open every odb individually to extract SOE, a repetitive and cumbersome task. The conventional method is time-consuming and not robust enough to eliminate human errors or inconsistency while recording the SOE and Actuator Spring Forces from many odbs.
The above problem statement encourages the automation of post-processing activity. The post-processing activity is automated using the Abaqus scripting interface. The Abaqus plugin is developed using the AFX toolkit. As part of the development, the user needs to open the plugin and follow the steps. It automatically begins odbs one at a time, identifies the SOE and ratcheting conditions based on a predefined logic, and writes the information to a consolidated Excel worksheet. Also, win 32 modules are used in the script to generate the PowerPoint report. The automation significantly reduces the annual effort by ~94%, enabling the reduction of design cycle time. The same script can also be used in workflows of optimization tools (such as Isight). The consolidated worksheet can be used as a dataset to create a machine learning model. This will be added in the future scope.
KEYWORDS: Park system, Automotive, Abaqus, Simulation, Post-processing Automation, Scripting interface, Plugin, Python.
Biography: Ujjwal Kumar DAS pursued B.Tech in Mechanical Engineering from NIT- Durgapur and graduated in 2007. He has 15 years of experience in FEA Structural Analyses and automation. He worked in various industries, including automotive OEMs. He gained exposure to diverse product designs and FEA analysis types such as modal, simple harmonic, non-linear static, dynamic, thermal, fatigue, optimization tools, scripting, and automation. His prime interest is to provide better, cheaper CAE solutions to customers in the early design phase.
