Building a Database to Support Intelligent Computational Quality Assurance of Resistance Spot Welding Joints

A database system for storing information on resistance spot welding processes is outlined. Data stored in the database can be used for computationally estimating the quality of spot welding joints and for adaptively setting up new welding processes in order to ensure consistent high quality. This is achieved by storing current and voltage signals in the database, extracting features out of those signals and using the features as training input for classifier algorithms. Together the database and the associated data mining modules form an adaptive system that improves its performance over time. An entity-relationship model of the application domain is presented and then converted into a concrete database design. Software interfaces for accessing the database are described and the utility of the database and the access interfaces as components of a welding quality assurance system is evaluated. A relational database with tables for storing test sets, welds, signals, features and metadata is found suitable for the purpose. The constructed database has served well as a repository for research data and is ready to be transferred to production use at a manufacturing site.