Machine Learning in Visual Quality Inspection for field engineers

Our company in short

Siemens AG is a global technology company with a focus on industry, infrastructure, transport, and healthcare. With around 2100 employees worldwide, the division of Technology plays a key role in R&D within Siemens. It covers a wide range of research fields, including software development, electronic engineering, energy, sensor technology, automation, medical informatics, and imaging, as well as information and communications technology. The technology field Data Analytics and Artificial Intelligence has been driven for more than 30 years at Siemens, creating innovative solutions and new business opportunities in many divisions and products.

Our business problem and machine learning

While the different divisions Siemens operates in might choose different solutions to a problem, a common denominator is the operation of such systems. ML could be one of the many engineering aspects to solve a customer problem but often comes with operational complexity. This is one of the reasons we joined the IML4E research project: We want to master the challenges that arise with MLOps wrt. trustworthiness, scaling, and integration with systems engineering.

While there are many possible use cases in the different sectors we operate in, we chose a Visual Quality Inspection (VQI) use case as a reference within the project. While the specific use case is not representative of all our machine learning efforts, it is a complex and reoccurring use case on the shop floor, in buildings or in the field. Our industry partners expect solutions that don’t interfere with their operations but enhance them, which is a high expectation for a machine learning system.

With the technical and soft skills acquired with the IML4E we will enable our employees to bring innovations to our customers.

Our research and solutions

Through guiding and evaluating the technical work in the project, we took the opportunity to try the tools and frameworks in other projects throughout the company to connect the different efforts around MLOps in Siemens and create a shared knowledge pool.

Additionally, we integrated specific tools to the internal developer platforms, so that developers have an easy and proven way to integrate with these services, while our inner source approach facilitates reuse and comes with best practices without the operational overhead, since the tools fit well into the actual Siemens ecosystem.

Future direction and business benefits

We will proceed by leveraging the knowledge built within the project to extend our internal platforms and training offerings and engineers will be able to create ML systems faster and more reliable and thereby create more value for our customers and partners. With new opportunities like GenAI and other further advances, we are well equipped to build the future of automation.