Precision additive metal manufacturing - PAM2
Funding: H2020 Marie Skłodowska-Curie Actions - Innovative Training Networks (ITN)
Duration: December 2016 – October 2018
Team: Richard Leach, Simon Lawes
Website: https://www.pam2.eu/
This Marie-Curie Innovation Training Network brings together research institutes, national measurement institutes and commercial entities from across Europe to advance the precision of Additive Manufacturing (AM). Currently AM metal technologies offer many advantages compared to subtractive manufacturing - design freedom, functional integration, and the capability to produce personalized parts locally with efficient material use. However AM is a young technology, most contemporary computer aided engineering (CAE) tools do not support the freedom of design that AM allows, and traditional design practices, having evolved alongside conventional subtractive manufacturing, are often predicated on the high level of geometric and material certainty that has come from >100 years of research into subtractive methods.
The production of precision parts relies on three principles: 1) Production is robust, i.e. that all sensitive parameters can be controlled. 2) Production is predictable. 3) That produced parts are measurable. AM technologies presents challenges on each of these fronts.
Metal AM produces highly textured surfaces and complex surface features that stretch the limits of contemporary metrology. AM of metals is an inherently high energy process, with dozens of sensitive and inter-related process parameters, making it susceptible to thermal distortions, defects and process drift. The modelling of these processes is beyond current computational power and novel methods are needed to practicably predict performance and inform design. With so many factors to consider, there is a significant shortage of qualified, trained and expert personnel.
This Innovative Training Network (ITN) project aims to drastically improve the precision of metal AM processes by tackling the three principles of robustness, predictability and metrology, and by developing CAE methods that empower rather than limit AM design.
An illustration of an integrated-vision system for additive manufacturing