Neural-network-based model for build-time estimation in selective laser sintering

  1. Lookup NU author(s)
  2. Dr Javier Munguia Valenzuela
Author(s)MunguĂ­a FJ, Ciurana J, Riba C
Publication type Article
JournalProceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture
ISSN (print)0954-4054
ISSN (electronic)2041-2975
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
Cost assessment for rapid manufacturing (RM) is highly dependent on time estimation. Total build time dictates most indirect costs for a given part, such as labour, machine costs, and overheads. A number of parametric and empirical time estimators exist; however, they normally account for error rates between 20 and 35 per cent which are then translated to inaccurate final cost estimations. The estimator presented herein is based on the ability of artificial neural networks (ANNs) to learn and adapt to different cases, so that the developed model is capable of providing accurate estimates regardless of machine type or model. A simulation is performed with MATLAB to compare existing approaches for cost/time estimation for selective laser sintering (SLS). Error rates observed from the model range from 2 to 15 per cent, which shows the validity and robustness of the proposed method.
PublisherSage Publications
Actions    Link to this publication

Altmetrics provided by Altmetric