A combined energy and error optimization method for metal powder based additive manufacturing processes. Issue 3 (20th April 2015)

• Record Type: Journal Article
• Authors: Paul, Ratnadeep; Anand, Sam
• Is Part Of: Rapid prototyping journal. Volume 21:Issue 3(2015)
• Page Start: 301
• Page End: 312
• Publication Date: 2015-04-20
• Subjects: Engineering design -- Periodicals; 620.004205
• DOI: 10.1108/RPJ-07-2013-0065
• Languages: English
• ISSNs: 1355-2546

Abstract

<abstract> <title> <x content-type="archive" xml:space="preserve">Abstract</x> </title> <sec> <title content-type="abstract-heading">Purpose</title> <p> – The purpose of this paper is to develop a methodology to analyze the total sintering energy (TSE) required for manufacturing a part in metal powder-based additive manufacturing (AM) processes and optimize AM processes for minimizing total energy and form errors of AM parts while maximizing part strength. </p> </sec> <sec> <title content-type="abstract-heading">Design/methodology/approach</title> <p> – The paper uses a computational geometry approach to determine the TSE expended for manufacturing a metal AM part. The stereolithography (STL) file of a part is converted into a voxel data structure and the total sintering volume (TSV) is computed from the voxel representation. The TSE is then calculated from the TSV using the material property information of the metal powder. </p> </sec> <sec> <title content-type="abstract-heading">Findings</title> <p> – The TSE of an AM part is calculated for different slice thickness and part orientations, and the correlation of the total energy to these parameters is calculated. Using these correlations, the AM process is optimized to calculate the optimal values of slice thickness and part orientation which would result in lower process energy, lower part form errors and higher part strength. </p> </sec> <sec> <title content-type="abstract-heading">Originality/value</title> <p> – The methodology presented in this paper provides AM users a roadmap to predict the energy required for manufacturing a part. In addition, the optimization model will allow engineers to manufacture precision parts which satisfy their design specifications with minimal energy expenditure.</p> </sec> </abstract>