Graph Massivizer will be a big step towards harmonizing neural and symbolic AI methods, a revolutionary approach to quality estimation in welding processes, and a profound leap towards realizing a genuinely sustainable automotive industry.
In the fast-paced world of modern manufacturing, quality monitoring and analysis are paramount to ensuring the reliability and performance of products. As industries strive for excellence, maintaining stringent quality standards across all manufacturing processes becomes essential. This is particularly true for intricate and precision-dependent operations such as welding and soldering, which are foundational to the integrity of countless products.
In the fast-paced world of modern manufacturing, quality monitoring and analysis are paramount to ensuring the reliability and performance of products. As industries strive for excellence, maintaining stringent quality standards across all manufacturing processes becomes essential. This is particularly true for intricate and precision-dependent operations such as welding and soldering, which are foundational to the integrity of countless products.
For Bosch a global leader in engineering, the importance of quality monitoring cannot be overstated. The company’s diverse product line—from automotive components to home appliances—relies heavily on precise manufacturing processes. For instance, the production of an electric drive involves several intricate welding operations that are critical to the product’s functionality and durability. However, quality monitoring not only ensures product excellence but is also a fundamental lever towards a sustainable automotive industry. By optimizing manufacturing processes and reducing waste, robust quality control measures contribute to sustainable manufacturing practices.
However, conventional quality monitoring often presents significant challenges, mainly derived from the costs associated to the required human intervention, as traditional methods for estimating welding quality are often time consuming and expensive. For example, for evaluating the quality of spot-welding operations, one common approach involves measuring the diameters of welding spots using ultrasound technology, which, while effective, requires specialized equipment and skilled operators. Apart from this, destructive testing methods are also used, mainly by pulling the welded metal sheets apart and measuring the required force to separate them, leading to an increase in waste.
As such, to address the challenges in quality monitoring, researchers have developed data-driven methods that approach the problem from a multivariate time series perspective. These models estimate quality based on sensor measurements from the spot-welding machine. In Graph Massivizer we aim at the next generation of such methods that offer versatile explainability and transparency by leveraging expert knowledge.
Aim
The aim of our use case is to develop next-generation quality monitoring methods that go beyond the more traditional data-driven approach by combining knowledge and sensor measurements.
On the one hand, we refer to sensor-measurements as the time series that are produced during the welding operation, which can have the form of currents, voltages, temperatures… These are variables that evolve during each weld and are fundamental to the estimation of the quality (e.g., if an abnormality has been detected in the current that flows through the cathode of the welding machine, the presence of an anomaly will be likely).
On the other hand, we refer to knowledge that encompasses diverse and rich prior information about the process, derived from expert-knowledge, machine manuals, anomaly reports, etc. This knowledge is represented as a Knowledge-Graph, and it comprises what experts on spot-welding would know.
Benefits
The benefits of building models that combine knowledge and data-driven methods are immense, both from the accuracy and explainability sides. On the one hand, incorporating expert knowledge into quality prediction enhances transparency. This increased clarity makes it easier to apply corrective measures and potentially facilitates preventive maintenance. And on the other hand, by integrating expert knowledge with sensor measurements, these models adopt a more informed approach to quality estimation, resulting in more accurate predictions.
All in all, we believe that Graph Massivizer will be a big step towards harmonizing neural and symbolic AI methods. It introduces a revolutionary approach to quality estimation in welding processes, and signifies a profound leap towards realizing a genuinely sustainable automotive industry.
Authors: Mikel Mendibe, Antonis Klironomos, Mohamed Gad-Elrab, Evgeny Kharlamov (Ph. D. at Bosch)