Before production, experience and deterministic simulations are used to design the dies for the chosen part. Experience is mainly used for process design and input parameter selection. During production few parts are manually inspected to see if they are within specifications and have no defects. Control charts are also used by operators to make process changes based on experience if parts are out of tolerance.
Conventionally, deterministic finite element methods (FEM) are used for die design in production systems and experience is mainly used for process design. However in reality, process conditions vary with time and hence with the same nominal process settings it is likely that the final forged part geometry varies with time. The simulations will give us solutions which may not match with the actual part geometries.
In order to solve this problem, an approach to integrating numerical and response surface models for robust process design is described in this research. The integrated model is a virtual production model (VPM) which integrates numerical modeling techniques with response surface methodology (RSM). The VPM is driven by a combination of FEM, RSM and stochastic simulations. The production system is modeled as a series of processes with input parameters having distributions and output attributes which vary with time. The tasks to develop the model include deterministic FEM simulations, development of response surfaces of attributes and stochastic simulations. This model will be used to design the existing production process by designing the best input parameter settings. Its main aim is reducing process variability, reducing defect rates and improving process capability by robust process design.
In this dissertation two approaches for integrating numerical models and response surface models have been described. The first approach integrates well established empirical relations, Taguchi methods of experimental design and FEM to arrive at robust roll pass designs. The second approach integrates FEM, RSM and stochastic simulations to develop a model of the production system. This model is then used for robustness analysis and improvement of the existing production system.