Centre for Additive Manufacturing
  • Print
   
   

Additive Manufacturing (AM) Management is concerned with creating, operating and controlling an AM transformation system that takes inputs of a variety of resources and produces outputs of products needed by end-users, which may be customers. Our competing objectives are: 

  • the technologist’s commercial objective of maximising profit
  • the ancient design objective of generating the most useful product in a world of scarce resources
AM Management

AM Management

 
 

Ultimately serving the purpose of perfectly reconciling needs with available inputs.

A few practical questions addressed by AM Management include:

  • Organisation of AM as a digitally integrated, "parallel" manufacturing technology, enabling the contemporaneous creation of different parts or products
  • Exploitation of AM’s ability to create product (shape) complexity at zero or very low marginal cost

Projects

The enabling role of 3DP in redistributed manufacturing: A total cost model

Title: The enabling role of 3DP in redistributed manufacturing: A total cost model

This project is funded through the Bit-by-Bit project at the University of Cambridge

Total value: £41,182

Start to end date: May - Dec 2015

Summary:

3DP_RDM_description-new

The overarching aim of the project was to address the existing lack of a fundamental understanding of the process economics that underpin the commercial application of 3D Printing. Forming a main element in the identification of the business case for the adoption of 3D Printing technology, existing costing approaches have concentrated on capital investments, i.e. the 3D Printing systems and ancillary equipment, and consumables, most importantly the build material. The focus on such “well-structured” costs has shown that utilising the available machine capacity constitutes a pre-requisite for the efficient operation of 3D Printing. However, existing investigations of the cost of 3D Printing have mostly ignored other costs resulting from, for example, build failure, and the rejection of parts. The consequence of the omission of such “ill-structured” costs is that existing cost models for 3D Printing lack realism.

The undertaken work developed new methodologies and executed a campaign of build experiments to collect a body of data allowing the formulation of a novel, more comprehensive, costing. The model resulting from this research provides a basis for further investigations into the processes economics of 3D Printing and the development of more robust process selection tools.

To create dataset required to achieve the project’s objectives, a series of build experiments was performed on a polymeric EOSINT P100 Laser Sintering (LS) system, with additional builds on the metallic Renishaw SLM 250 Selective Laser Melting System. The main series of experiments on the LS system consisted of ten identical builds which reflected machine operation at full capacity and four builds at sub-maximal levels of capacity utilisation. A sufficient number of repetitions of the build experiments was obtained by artificially limiting machine capacity to a 30 mm thick horizontal band of build space and extrapolating the obtained results to the full height of the available build cuboid (330 mm). The used build space was populated with test geometries and tensile specimens using a computational build volume packing tool.

 Project team:

  • Dr Martin Baumers (CfAM)
  • Prof Matthias Holweg (University of Oxford)
  • Jonathan Rowley (Digits2Widgets)

Key publications:

  • Baumers, M. and Holweg, M., 2016. The Cost Impact Of the Risk of Build Failure in Laser Sintering. Solid Freeform Fabrication Symposium, 2016, University of Texas at Austin.
  • Baumers, M., Tuck, C. and Hague, R., 2015. Selective Heat Sintering versus Laser Sintering: Comparison of Deposition Rate, Process energy Consumption and Cost Performance. Solid Freeform Fabrication Symposium, 2015, University of Texas at Austin.
  • Despeisse, M., Baumers, M., Brown, P., Charnley, F., Ford, S.J., Garmulewicz, A., Knowles, S., Minshall, T.H.W., Mortara, L., Reed-Tsochas, F.P. and Rowley, J., 2016. Unlocking value for a circular economy through 3D printing: A research agenda. Technological Forecasting and Social Change.
  • Baumers, M., Holweg, M., and Rowley, J., 2015. The economics of 3D Printing: A total cost perspective. Project Report. 3DP-RDM project.
  • Baumers, M., 2017, “Digitalisation of manufacturing and restructuring of value chains”, Presentation held on 23 February 2017 by ETUI in Naples/Italy.
 
3D Printing Production Planning (3DPPP): Reactive manufacturing execution driving redistributed manufacturing

Title: 3D Printing Production Planning (3DPPP): Reactive manufacturing execution driving redistributed manufacturing

This project was funded by the 3DP-RDM network (EP/M017656/1)

Total value: £42,010

Start to end date: April –Dec 2016

Summary:

3D PackRat Build Volume in Additive Manufacturing

3D Printing (3DP) technology promises supply chain innovation by enabling manufacturing configurations yielding value through product differentiation, including spatial location. Pursuing distributed 3DP supply chains may be the result of strategic deliberation, yet it is also frequently noted that 3DP is prone to higher unit costs than conventional manufacturing where quantities become large. Additionally, 3DP faces the challenge of being integrated into existing manufacturing and information systems, which may be operated in a centralised location. The implementation of appropriate supply chains is now seen as a core capability for manufacturing businesses.

Building on previous work on computational build volume packing for 3DP this project has implemented a feasibility demonstrator for an integrated build volume packing and scheduling approach. Labelled the “3D Packing Research Application Tool” (3DPackRAT), the resulting operational software tool enables a flexible and reactive manufacturing execution methodology that is designed to complement the strengths of 3DP.

To achieve the project goals, the integrated computational framework enables the inclusion of a wide range of general and location related aspects in a single optimisation-based production planning procedure. Being fed an order stream, the demonstrator thus aims to determine the best 3DP system for each build request. Crucially, this approach is also capable of considering the benefits resulting from re-distributed 3DP, driving supply chain structures towards such configurations where beneficial. The flow chart shown in Figure 1 illustrates the general working principle and informational inputs and outputs of the developed demonstrator system.

Project team:

  • Dr Martin Baumers (CfAM)
  • Dr Ender Ozcan (University of Nottingham)
  • Dr Jason Atkin (University of Nottingham)
  • Warren Jackson (University of Nottingham)
  • Wenwen Li (University of Nottingham)

Key publications:

  • Baumers, M. and Özcan, E., 2016. Scope for Machine Learning in Digital Manufacturing, Position Paper, arXiv preprint, arXiv:1609.05835.
  • Baumers, M., Özcan, M., and Atkin, J., 2016. 2., 3D Printing Production Planning (3DPPP): reactive manufacturing execution driving re-distributed manufacturing. Project Report. 3DP-RDM project.
 
 

Centre for Additive Manufacturing

Faculty of Engineering
The University of Nottingham
Nottingham, NG7 2RD


telephone: +44(0)115 84 66374
email: CfAM@nottingham.ac.uk