Atlas Copco launches machine learning app for manufacturing26 August 2021

Atlas Copco Tools and Industrial Assembly Solutions has launched a software application that harnesses artificial intelligence (AI) and machine learning to identify risks in production before they occur.

The ALTURE app (ALTernate FutURE) is the latest evolution of Atlas Copco’s Smart Connected Assembly philosophy with the objective of error-proofing the production line, thereby increasing throughput by avoiding product rework or downtime.

Using raw data captured from smart tooling and integrated controllers (only tool data, result data and tightening traces are collected), ALTURE analyses this using AI and an algorithm in the app. Managers are provided with real-time line performance notifications, as well as any ‘Not OK’ (NOK) tightening trends and recommendations for process improvement. Feedback from more than 50 of Atlas Copco’s global customers during a 12-month trial confirmed that the smartphone app was the simplest and fastest means of delivering real-time information, enabling personnel on the shop floor to counterstrike any emerging issues.

Commenting on the launch of ALTURE, James McAllister, general manager, Atlas Copco Tools and Industrial Assembly Solutions, said: “For manufacturers, the challenge of increasing throughput that’s right first time, every time, towards zero defect production, is what Atlas Copco’s primary mission is focused on achieving. With the introduction of ALTURE, which interrogates data in real time using AI, we can provide customers with a continuous value cycle, so that any erroneous production issues are flagged before they become problematic. In this way, costly interruptions to the manufacturing process can be avoided.”

“As a strategic tooling and data services partner for some of the world’s leading manufacturers across numerous sectors, Atlas Copco is at the forefront of innovating assembly solutions which deliver safer and more cost-efficient productivity. ALTURE represents the next level in harnessing machine learning to make manufacturing more sustainable by reducing material wastage and unnecessary energy consumption, thanks to the power of data.”

Operations Engineer

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