Many technology suppliers now offer artificial intelligence (AI)-enabled products and many machine builders have started to evaluate the technology. However, there are several roadblocks impeding adoption. Most prominent among these are the lack of data scientists and available data, legal aspects, human factors, and unclear use cases.
The following case studies from the ARC European Forum highlight these issues.
Most applications presented at the ARC European Forum were in the area of quality control.
According to BrainCreators, over half of the quality checks in manufacturing involve visual confirmation, which are an easy target for AI. The challenge here is performing the needed quality checks with increasingly small batch sizes and higher variances in production, where a combination of expert know-how and AI support is the right offering. One of the key challenges in the industrial world is the codification of knowledge. This means documenting the tribal knowledge of engineers and others in written, or similar format, so it can be shared across the entire organization. Maarten Stol, principal scientific advisor at BrainCreators referred to this as, “turning domain expert knowledge into digital intelligence.”
Another example shared by BrainCreators was visual road inspection. While not exactly an industrial use case, it demonstrates some benefits and pain points of AI-based quality control. The algorithms can combine the knowledge of many inspectors, increasing quality and freeing the outcomes of the inspections from subjectivity. The digital inspections can be linked to geographic information system (or any other) data, allowing a seamless workflow and speed up of not only the quality control itself, but the time needed to complete the road repair.
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Larsen & Toubro (L&T) Construction uses AI and image processing techniques to ensure quality in welding processes. While this can be done in a pre-defined setting for in-plant welding machines, L&T is challenged to analyze welds in pipelines and other typically “one-off” welds. While the images are taken locally at the edge, analysis is done in the cloud to train and use more computing power.
Another alternative to testing every single product is one used by Siemens in its Amberg factory, an electronics production site. In this high-speed production environment, testing each piece is time and cost intensive, so AI is used to predict quality using production data and existing quality test results.
ARC estimates that maintenance is the largest implementation area for AI applications in machinery applications, where it can bring substantial savings. This estimate is based on the fact that 80% of lifecycle costs for automation equipment typically relate to operations and maintenance. BrainCreators showed its solution for cabinet inspection, which allows real-time, proactive asset management based on maps of assets.
Another pain point is generating the time-intensive, post-inspection paperwork needed to comply with regulations. Here, AI can help and support the maintenance staff so they can focus on more value-adding work.
Warehouse logistics, MES, and ERP applications
One highly discussed application at the forum was Toyota Material Handling’s forklift application. The application describes the development from manual forklift trucks to guided vehicles to autonomous vehicles. This spans several applications, including safety, collaboration, and operational optimization. Training, verification, and optimization are performed in the cloud using a digital twin. There is onboard intelligence and cloud-based communication, enabling swarm intelligence and distributed learning.
This cloud-based learning with a digital twin not only shortens project times, but also provides Toyota with a lifecycle service opportunity using failure analysis, preventive maintenance, and optimization. Further integration with enterprise resource planning (ERP) allows for fully automated logistics that also work with smaller batch sizes.
AI is increasingly popular when it comes to improve usability of systems, from manufacturing execution system (MES) up to ERP. In addition to pre-generated interfaces and reports, AI can help answer questions such as “How much material is used?” Contextualization is key here and needs close collaboration between users such as L&T and AI experts.
In addition to quality control, AI can be used to fill in and generate reports to free workers from paperwork.
Operational Simulation and Optimization
While consumers expect consistent product quality and taste, natural variances of ingredients impact both food and beverage products. This is a particularly acute problem for global brands. Working with a brewery customer, Siemens developed an AI-based offering that aims to guarantee consistent quality and taste. An additional benefit from this project was to reduce loss of domain knowledge, since the process is codified.
The Tip of the Iceberg
Obviously, these just represent the tip of the iceberg when it comes to potential use cases for AI in machinery applications and across the industrial world. But, as always, before embarking on any new automation project, it’s important to first identify a real need and build a business case for implementing the technology.