Automotive - Case study 1

3D FE machining simulation

Need for this analysis

The objective of this case study is to assess the rigid body motion of the work pieces and their elastic-plastic deformations induced during high speed milling of thin-walled parts. These are the main root causes of part geometrical and dimensional variability. These are governed mainly from the choice of process plan parameters such as:

• fixture layout design

• operation sequence

• selected tool path strategies

• the values of cutting variables

Therefore, it becomes necessary to judge the validity of a given process plan before going into actual machining.

Our analysis method

iDG10 has developed a computationally efficient milling process plan verification and optimization system called MillCut. It has been developed using advance numerical (Finite Element Method FEM) and artificial intelligence (GA, ANN and Rule-based Expert system) techniques. The developed system is capable of predicting the workpiece transient non-linear behaviour during machining. It takes into consideration the impact of machining loads on the following:

• workpiece changing rigidity

• inelastic material properties

• fixture workpiece flexible contacts

The developed software also allows to accurately capture the effects of initial residual stresses (residing inside the raw stock) on part deformations.

MillCut comprises of five main modules namely

1. feature-based process-planner

2. machining load computation module

3. the FEM based transient milling simulation module

4. the report generator

5. the rule-based part error diagnostic system

The developed process planning module provides a feature-based modelling environment to create a process plan for new part geometries or extract machining information from an imported APT (automatic programming tool) file generated by other conventional CAM software. To achieve optimal machining conditions (respecting machine tool technological constraints) a GA (Genetic Algorithm) based cutting parameter selection model is also incorporated within the process planning module. To simulate the behaviour of different cutters with varying geometries, an ANN (Artificial Neural Network) based generalized machining load model is developed to predict the cutting force components and average shear plane temperature. For the verification of a given process plan, a FEM based 3D virtual machining environment is developed. By first simulating the transient material removal process, one can have prior knowledge of form errors (if any) and then using the expert system (rule-based) diagnostic functionalities, error sources can be identified and corrected before transferring the process plan to the CNC machine.

Results

iDG10 developed state of the art MillCut software allows users to easily configure the workpiece fixture set up and perform transient machining simulation taking into account the cutting loads.

The sample results extracted from MillCut viz Temperature/Stress FEA predicted vs experimental profiles, workpiece initial residual stresses and final machine shapes etc. are shown in figures below.