Selective Laser Melting (SLM) is a powder bed fusion additive manufacturing (AM) process that is capable of producing metallic parts in a layer-by-layer fashion directly from CAD data. SLM can produce complex parts with near-full density and mechanical properties comparable to those provided by conventional casting and forming. Although there has been significant research into the entire SLM supply chain, one of the key challenges to the widespread adoption of SLM is the inability to achieve repeatable, high-quality parts from every SLM build. The reduction of distortion in SLM components is critical for ensuring a right-first-time SLM build. The DREAM project will address this challenge through a multidisciplinary digital approach, coupling real-time data acquisition, advanced modelling, cloud-based computing, and adaptive machine process parameter control to achieve zero-distortion builds.
The project approach will be independent of powder supplier and machine manufacturer. The software, hardware, and cloud-based solution will allow internet-enabled machines and systems to identify distortion during the build process and make real-time decisions and forecasts about process parameter controls to mitigate and control distortion during the build process. The outcomes will result in cost reduction, higher material utilisation, improved quality assurance, and reduced design cycle times in the SLM process chain.
A fundamental technical barrier to the widespread adoption and fulfilment of the economic potential of metal AM is the prediction and control of process-induced distortion. Whilst simplified, heuristic software is available that can be used to estimate distortion pre-build, there is no flexible, robust capability available that can detect and mitigate distortion in real time. LE’s such as Boeing, Rolls-Royce, and Airbus have identified distortion as a critical issue. In July 2016, Apriso identified distortion has the #1 problem for AM. As such, improved control of distortion would be a significant market advantage.
Despite all of the potential benefits that Selective Laser Melting (SLM) offers, it is only used to a limited extent in safety-critical applications. In part, this is because there are still a large number of unsuccessful SLM builds that increase production costs. Many of these build failures are due to distortion by cracking from the substrate after significant manufacturing effort has been applied, with one estimate of the impact being $100k/year/machine. As a consequence, large companies seeking to implement AM in their portfolios resort to SME acquisitions and in house R&D to provide confidence in their AM productions. As a result, competition can be muted in the wider AM community, and companies attempting to act as service bureaus in the AM supply chain have limited opportunities. A key challenge for SLM, therefore, is the establishment of a robust, repeatable, and reliable build process.
The DREAM project will exploit this business opportunity through a multi-disciplinary approach, combining aspects of real-time “big data” analysis, cloud-based computing, materials science, state-of-the-art numerical modelling, and data acquisition systems to enable adaptive process parameter control of SLM machines that results in zero-distortion parts. The DREAM solution will include optical images of the part during the build process so that the “in eventus” deformation of the part can be compared to the “ideal” CAD geometry. The entire “big data” acquisition system will

  1. be processed between layer slices by local software to provide heuristic corrections to the next build slice; and
  2. the data will be streamed to the cloud where advanced algorithms will provide forecasting of the optimal adaptive parameter modifications to mitigate distortion.

The key challenge will be the total integration of the new hardware, software and cloud-based solution in a manner that is scalable and implementable in a machine-manufacturer agnostic manner.