Prediction of geometrical variation of forged and stamped parts for assembly variation simulation
Paper in proceedings, 2013

In assembly variation simulations a total sensitivity matrix, implicitly defined in a FEA-based simulation model describing all mating conditions, kinematic relations and non-rigid behavior, is used together with Monte Carlo simulation to predict variation in critical product dimensions. A major drawback with this kind of simulation is that form and shape variation of parts, stemming from their individual manu-facturing processes, are unknown. Therefore, better ways to predict form and size variation are needed. In this paper a five critical material and process parameters are varied in a full factorial computer experiment for two different test cases; one forged part and one stamped part. The geometrical deviation in each factori-al run is registered. These results are then used as input for construction of a meta-model, well suited for Monte Carlo based variation simulation. The contributions from the different material and process parame-ters to the total variation are discussed. The cases show that the proposed method is well suited both for forging and stamping and represents a general way to describe, model and simulate part variation in varia-tion simulation for assemblies.

Stamping

Part Tolerances

Forging

Design of Experiments

Author

Kristina Wärmefjord

Chalmers, Product and Production Development, Product Development

Rikard Söderberg

Chalmers, Product and Production Development, Product Development

Peter Ottosson

Mats Werke

Samuel C Lorin

Chalmers, Product and Production Development, Product Development

Lars Lindkvist

Chalmers, Product and Production Development, Product Development

Fredrik Wandebäck

Proceedings of International Deep Drawing Research Group Conference 2013, IDDRG2013

Subject Categories

Production Engineering, Human Work Science and Ergonomics

Computational Mathematics

Vehicle Engineering

Driving Forces

Sustainable development

Areas of Advance

Production

More information

Created

10/8/2017