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Valid Life

Today, many Swedish manufacturers have access to an enormous amount of data from production and products in use. Despite proven AI (artificial intelligence) techniques, digital technologies and lifecycle engineering (LCE) methods, applying these in combination to industrial cases with the holistic, lifecycle perspective is scarce. This project brings together expertise from AI and LCE to Product/Service Systems for Swedish manufacturing firms in a multidisciplinary research effort to utilise latest techniques efficiently for Swedish production industry.

The project will also develop a plan on how to utilize this hybrid approach in production and maintenance for complex, longlife products manufactured by global firms based in Sweden and in the context of suitable demonstrators. Three value chains will be investigated: Siemens Industrial Turbomachinery (SIT), Volvo Construction Equipment (VCE) and Semcon and with their selected customer. A plan to develop a demonstrator for each value chain will be documented.

The major expected impacts of this project (Step 1) and the forthcoming demonstration project (Step 2) on Swedish industry lie in their improved capacity and competitiveness in production and maintenance. The market potential for AI to be applied in practice is huge. Improved environmental performance is also expected.

Linköpings Universitet (LiU) is the project coordinator and provides expertise about LCE. Mälardalens Högskola contributes through its expertise on AI and provides a case from VCE. SIT provides its own case. Semcon provides a case from its own customers and potential solutions for the three cases. Imagimob provides parts of the technical solutions for the three cases.

Project manager

Participating researcher(s)

Topics

Artificial Intelligence, Life Cycle, Lifecycle engineering, Maintenance, Product Service System (PSS), Production

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Maintenance in existing plants is becoming increasingly important, where predictive maintenance has become an emerging technology. The use of decision support tools contributes to environmentally and economically sustainable production. Within this project, different types of digital twins have been designed and evaluated. Specifically, new predictive model types have been tested in two different industrial case studies; a heat exchanger at SSAB and a profiled header at Svenska Fönster AB.

Project time

2019-2019

Budget

827 545 kronor

Partners

Linköpings Universitet

Mälardalens högskola

Imagimob AB

Semcon Sweden AB

Siemens Industrial Turbomachinery AB

Funding

SIP Produktion2030