Journal
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE
Volume 221, Issue 8, Pages 1329-1340Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1243/09544054JEM815
Keywords
rapid prototyping; optimization; neural networks; genetic algorithms
Ask authors/readers for more resources
In layer-based rapid prototyping, a volumetric object is approximated as a pile of slices with vertical walls. Process parameter selection in layer-based prototyping is a multicriteria multiparameter optimization problem. A number of criteria may be used for assessing the prototype's quality. Volumetric accuracy of shape approximation and building time are just two criteria taken in this work as an example. Criteria depend on process parameters, most commonly in a mutually contradictory manner. Model orientation and slice thickness constitute the minimum of process parameters to be considered, but others may also be added. For this reason, a neural network is used, trained by a number of input-output vectors, when analytical formulae representing the dependency of criteria on process parameters are not possible to develop and/or available numerical models take too long to execute. Neural network meta-models are used in the evaluation (cost) function of a genetic algorithm, each representing a particular criterion, and criteria are weighted according to the user's particular view. A case study is presented, referring to a wax model prototyping machine in which a particular tree for investment casting is built. A new criterion for assessing the quality of shape approximation is introduced, namely the local volumetric error per slice.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available