NGenMech

Project "NGenMech" data sheet

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 Partnership

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 Project objective

80% of failures in machines using lubricated moving parts (bearings, gears, camshafts etc.) is due to the damaging operational condition called starved oil lubrication. Recurrent exposure to this state is the bottleneck of performance and durability for such machinery. End-users are happy to pay more for reliable and durable engineering products, but state of the art offers only costly and deficient workarounds to the key peformance-limiting problem. A disruptive approach to enhance oil lubrication specifically under the critical damaging conditions is recently viable due to high-frequency ultra-short pulse lasers (HFUSPL) having become affordable outside the realm of high-end applications. By using HFUSPLs the micro- and nanoscale patterning of surfaces is possible with unprecedented precision. Such patterning activates a novel and highly effective oil lubricant delivery mechanism expressly under critical contact conditions, resulting in 3x reduction of friction coefficient, improved performance and reliability, reduced maintenance – doubling the lifetime of the entire machinery. This technology has been advanced to TRL6: real textured engine parts demonstrated in model engines. To make NGenMech available for industry it needs to be automatised using an optoelectric control unit, duly parametrised for the market entry use case, and piloted by a selected industrial partner. To engineer this workplan, a feasibility study will assess potential markets, analyse business model and partnership options, and evaluate commercialisation and IPR strategies to select Primary Use Case and define Strategic Business Plan. As R&D and tech transfer company, IfU Diagnostic Systems GmbH is engaged with several large industrial companies interested in commercialising NGenMech technology. NGenMech will double the lifetime of machinery while reducing maintenance need. The powerful cascade impact over the entire value chain of mechanical engineering cannot be overestimated.