A. Grevstad, Third Wave Systems, Minneapolis, MN
Residual stresses and part distortion are major cost cutting obstacles with respect to time-to-market, reduced scrap, and high part quality in the metal machining community. Aerospace monolithic structures suffer from distortions that hamper assembly operations. Automotive powertrain components have high flatness-tolerance surfaces that maintain fuel efficiency and component life while lowering emissions. These problems persist because industry lacks a capability to predict machining induced residual stresses and part distortion. Part Distortion prediction and management needs to address the following essential components:
- Pre-machined stresses due to operations like forging, casting, rolling, etc.
- Machining induced residual stresses given significant material removal in thin components
- Tool path analysis
- Distortion prediction using the residual stress state of the workpiece and the claming loads
One such method of comprehensive distortion analysis is application of the finite element method (FEM). Here, a three-dimensional FEM model is presented that includes fully adaptive unstructured mesh generation, tight thermo-mechanical coupling, deformable tool-chip-workpiece contact, interfacial heat transfer across the tool-chip boundary, momentum effects at high speeds and constitutive models appropriate for high strain rate, and finite deformation analysis that predicts machining induced residual stresses. A complementary capability to translate the machining induced residual stress results and incorporate pre-existing stresses to a holistic part level analysis to achieve the required goal of predicting part distortion will be discussed.
The intent is to reduce testing as:
- Testing is expensive and slow.
- Testing does not allow reliable measurement of all parameters of interest; for example, temperatures and stresses within the part during and after cutting.
- Testing requires a prohibitively large parameter test space to encompass all the cutting conditions necessary to determine the best method for each application.
Residual stresses and part distortion are major cost cutting obstacles with respect to time-to-market, reduced scrap, and high part quality in the metal machining community. Aerospace monolithic structures suffer from distortions that hamper assembly operations. Automotive powertrain components have high flatness-tolerance surfaces that maintain fuel efficiency and component life while lowering emissions. These problems persist because industry lacks a capability to predict machining induced residual stresses and part distortion.
Part Distortion prediction and management needs to address the following essential components:
1. Pre-machined stresses due to operations like forging, casting, rolling, etc.
2. Machining induced residual stresses given significant material removal in thin components
3. Tool path analysis
4. Distortion prediction using the residual stress state of the workpiece and the claming loads
One such method of comprehensive distortion analysis is application of the finite element method (FEM). Here, a three-dimensional FEM model is presented that includes fully adaptive unstructured mesh generation, tight thermo-mechanical coupling, deformable tool-chip-workpiece contact, interfacial heat transfer across the tool-chip boundary, momentum effects at high speeds and constitutive models appropriate for high strain rate, and finite deformation analysis that predicts machining induced residual stresses. A complementary capability to translate the machining induced residual stress results and incorporate pre-existing stresses to a holistic part level analysis to achieve the required goal of predicting part distortion will be discussed.
The intent is to reduce testing as:
• Testing is expensive and slow.
• Testing does not allow reliable measurement of all parameters of interest; for example, temperatures and stresses within the part during and after cutting.
• Testing requires a prohibitively large parameter test space to encompass all the cutting conditions necessary to determine the best method for each application.
