Dimensional Control Systems (DCS) is hosting a webinar to demonstrate how to simulate gear assemblies in 3D tolerance analysis software. This event will be presented by guest Tom Oetjens, CAE Integrations Inc., who brings his more than 40 years of experience developing proof of concept models for a variety of clients in CAE applications. The webinar, aptly named ‘New Gear Simulation Modeling’ will be hosted on Thursday, September 24th, 2020 at 11:00 am EST via Gotowebinar.
Gears need to work together, and identifying and fixing the root cause of misalignment before production begins is critical. Even the slightest misalignment can reduce performance, leading to lengthy delays spent identifying and fixing the issue and often involving high-cost corrective actions.
The focus of this event is to show both fine and coarse studies on a gear assembly, demonstrating the inputs and outputs and how they can be leveraged for reducing the risk of misalignments while minimizing variation through design iteration. This includes the analysis of tooth contact and complex gear mechanism dynamics with high accuracy, as well as how to fix issues with adjustments in bearings, shims, and pinion nut adjustment. This process provides the flexibility to make small adjustments in design that create a gear structure with minimal problems.
The use of 3DCS software can be used to assess gear backlash and mounting misalignment, perform angular backlash analysis, and create both flank test displays, and contact patterns. Using this solution integrated into the CAD platform allows for analysis of:
•Backlash and system backlash.
•Line of action contact.
•Center mounting distance.
•Contact pressure angles.
•Flank contact area.
•Radial tooth interface.
•Radial tooth gap.
The 3D model utilizes multiple measures and analytics that compare the variation tolerances of each component and the way the finished gear mechanism works with each of these variations — individually, as they are produced, compiling the results into statistical outputs, correlated sensitivity data for identifying contributors, and risks assessments.