Past opportunity

Edge AI Solutions for Industrial Assembly with Atlas Copco

Edge AI
Self-supervised training
Real-time inferencing

Industry

Industrial equipment and automation

Revenue

2020

€9.6B

Employees

2020

40,000

Countries

2020

70+

About

Atlas Copco’s Industrial Assembly Solutions division is a leading provider of joining and dispensing solutions for various vehicle painting and e-mobility applications. Some of our customers prefer not to share any data created by our equipment in the production line. Without this customer-specific data, we cannot adjust our products in the best way. Hence, we are looking for edge AI solutions that would allow self-supervised or incremental training based on the data generated directly on the equipment.

Opportunity overview

Today, a high level of expertise is required to set up the system correctly, troubleshoot and optimize the production process. This complexity makes it very hard for a non-expert to adjust the system in the best way to the process, to identify and solve errors very quickly or to schedule maintenance accordingly. The behavior of the system is also highly dependent on local environmental conditions such as adhesive properties, type of application and robot programming, which prevents us from using pre-trained AI models without adapting them to local conditions.

The system should hide this complexity from the customer by making intelligent decisions based on customer-specific data. This includes but is not limited to automatic root cause analysis in case of failures, constant wear monitoring for predictive maintenance, improvement of production quality and reduction of waste. The ultimate vision is that only a start button needs to be pressed, everything else is done automatically and requires as little user attention as possible. Additionally, an ideal solution should work with limited computational resources and without constant internet connection.

Your opportunity with Atlas Copco

Atlas Copco is looking for edge AI partners who can enable self-supervised learning in cases where customer-specific data is considered sensitive and cannot be processed in a server. We are open for various solutions including software and hardware innovations. Though an ideal solution solves the end-to-end problem, we are also open for solutions solving more specific sub-problems.

In pilot phase, the solution could be applied to a few test facilities but later scaled to a wider use in Industrial Assembly Solutions’ products. Moreover, Atlas Copco can provide access to various other industrial assembly opportunities as well as to major industrial OEMs.

Focus Areas

Examples of solutions we’re interested in

Root cause analysis

A system capable of determining the actual cause (e.g. “nozzle is clogged”) instead of showing an error message of the symptom (e.g. “motor overloaded”).

Predictive maintenance and wear monitoring

A system constantly monitoring the condition of its electromechanical components and reporting wear-related failures within sufficient lead time.

Process monitoring and optimization

A system reacting appropriately to changes in adhesive viscosity indicated by changes in the pressure level by automatically adjusting heating parameters and pressure, and avoiding unnecessary purging processes.