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Voice of customer is typically captured through multiple connect points like surveys, warranty claims, social media, and so on. Customer verbatim is collected through these connect points to encourage free expression of opinion by customers. Such verbatim data is generally of high value and is typically analyzed using Natural Language Processing (NLP) techniques for translating into influencing actions in manufacturing, customer service, marketing, and product development departments. One of the challenges in analyzing unstructured verbatim data is to map that data onto appropriate concern codes (CCCs), which are typically used in automotive firms for tracking quality and satisfaction metrics. These concern codes map to a hierarchy of function areas in the organization aimed at improving product, service and hence the customer’s overall experience.

Typical automotive body structures use resistance spot welding for most joining purposes. New materials, such as Advanced High Strength Steels (AHSS) are increasingly used in the construction of automotive body structures to meet increasingly higher structural performance requirements while maintaining or reducing weight of the vehicle. One of the challenges for implementation of new AHSS materials is weldability assessment. Weld engineers and vehicle program teams spend significant efforts and resources in testing weldability of new sheet metal stack-ups. In this paper, we present a methodology to determine the weldability of sheet metal stack-ups using an Artificial Neural Network-based tool that learns from historical data. The paper concludes by reviewing weldability results predicted by using this tool and comparing with actual test results.