A small-lot manufacturer of mission-critical components is interested in improved process understanding and the ability to monitor part-to-part process repeatability during resistance spot welding. This particular resistance welding process is somewhat unique in that coalescence is achieved primarily through diffusion. Radiography, ultrasonic, eddy current, and electrode displacement all rely on phase transformations of the material as a means of inspecting bond quality. Consequently, unlike other traditional resistance welding processes, inspecting weld quality through various post-process non-destructive techniques is difficult due to the lack of melting.
Faster processing speeds, smaller heat affected zones, shortened cooling cycles, and higher quality make laser welding an attractive alternative to traditional welding processes such as arc and resistance spot welding. However, the highly focused and narrow beam requires precise workpiece fit-up, improved edge preparation, and accurate alignment with the joint line to produce high quality welds. Minor variability in the laser welding process can have deleterious effects on weld quality. The increased throughput that laser welding offers along with its need for tighter tolerances requires process monitoring that catches errors as they occur in real-time, rather than reacting to flaws downstream. This article demonstrates InnerVoice's ability to detect root openings during autogenous laser welding.
In-process monitoring is the on-machine, real-time assessment of part and process quality based on the direct observation of the process physics and dynamics. It is based on the premise that most in-process physical behaviors of a manufacturing process can and should be regarded as process quality feedback. In-process monitoring provides real-time, non-destructive manufacturing process quality feedback that enhances, complements, and extends the engineering efforts that have already gone into the process. This article provides a brief introduction to in-process monitoring, including why it is important, how it works, and the benefits associated with its implementation.
InnerVoice is a real-time, in-process monitoring hardware and software platform for manufacturing quality assurance. InnerVoice combines AI and machine learning into a powerful, general-purpose, data-driven process monitoring system that enhances the productivity and reliability of your manufacturing process. InnerVoice condenses the raw manufacturing process data into a unique digital fingerprint on a part-by-part basis and immediately alerts the manufacturer to process outliers when novel or anomalous behavior is encountered. InnerVoice catches flaws when and where they occur and before they become defects, thereby preventing bad parts from being delivered to the customer.