Abstract
Heat Treatment represents one of the largest challenges for component risk management.
Traditional metallurgical test methods do not meet AIAG/VDA Defect detection criteria for safety-critical components and can represent significant overhead costs. Newer non-destructive methods are difficult to implement with substantial upfront costs and must be integrated as 100% inspection to impact PFMEA detection ratings, which can introduce a throughput constraint. Production controls and automated escalation are imperative to minimizing risk.
On the development side, it is impractical to physically evaluate all combinations of product/process variation, or even test specification limits. Consequently, designs which met requirements in validation may experience degraded functionality in production due to ‘normal’ process variation that cannot be eliminated, or inevitable differences between early development and production scale processes.
With the accelerated pace of innovation seen in the automotive industry, use of FEA simulation to evaluate part sensitivities is essential to identify and optimize design/process, reducing risk. Increased confidence must be achieved in test and data processing methodology through robust implementation which often requires substantial investment in time and data analysis, which can be streamlined through machine learning.
