Project: ZDMP
Type: | Updated at: 20-05-2021
Engine block manufacturing: Defects detection and prediction in aluminium injection and machining operations
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Associated projects
Collaborative Product-Service Factories Pathway
Specifics for Result UC1.1 / UC1.2 | Engine block manufacturing: Defects detection and prediction in aluminium injection and machining operations
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Digitalisation pathways
Demonstrator specific Kanban
Digitalisation pathways
Collaborative Product-Service Factories Pathway
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Demonstrator specific Kanban
Comment:
Sse-cases offer an additional services for quality assessment and prediction based on the aggregated data from the participating industrial partners (Martinrea Honsel (MRHS) and FORD) that will be provided by the ZDMP platform. In this case, the service covers only specific product lifecycle stage, when the product – aluminium cylinder blocks, is produced and arrives at FORD to be used in engine production, as the defected blocks are filtered both at MRHS and FORD. Thus, the goal is to reduce the amount of defected parts through collaboration between two industrial partners.
Service-enabled Product Design
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Demonstrator specific Kanban
Voice of suppliers Customers / Users
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Comment:
In UC 1.1 / UC 1.2 use-cases, both industrial partners exchange the data from the various end devices (sensors) in order to achieve the cumulative effect in terms of defects reduction. Thus, the MRHS – supplier of aluminium cylinder blocks, can benefit from the data sharing between the MRHS and FORD to improve the quality of products needed for the engines assembly.
Service orient. Product Design (integration of PLM and CRM)
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Comment:
The service proposed doesn’t require big hardware installation, but rather makes use of the data gathered from already installed field level devices (sensors, smart meters, cameras) having the supportive or supplementary role in the production process. However, the added value is in reduction of the number of defected parts or products through extensive utilization of the data-rich environment. |