A neural network-based build time estimator for layer manufactured objects

A correct prediction of build time is essential to calculate the accurate cost of a layer manufactured object. The methods presented in literature are of two types: detailed–analysis- and parametric-based approaches. The former require that a lot of data, related to the kinematic and dynamic performance of the machine, should be known. Parametric models, on the other hand, are of general use and relatively simple to implement; however, the parametric methods presented in literature only provide a few of the components of the total build time. Therefore, their performances are not properly suited in any case. In order to overcome these limitations, this paper proposes a parametric approach which uses a more complete set of build-time driving factors. Furthermore, considering the complexity of the parametric build time function, an artificial neural network is used so as to improve the method flexibility. The analysis of the test cases shows that the proposed approach provides a quite accurate estimation of build time even in critical cases and when supports are required.

[1]  Hecht-Nielsen.  Theory of the backpropagation neural network , 1989 .

[2]  Georges Fadel,et al.  Expert system-based selection of the preferred direction of build for rapid prototyping processes , 1995, J. Intell. Manuf..

[3]  Andrew Y. C. Nee,et al.  Multi‐objective optimization of part‐ building orientation in stereolithography , 1995 .

[4]  Xue Yan,et al.  PII: 0010-4485(95)00035-6 , 2003 .

[5]  Seth Allen,et al.  Part orientation and build cost determination in layered manufacturing , 1998, Comput. Aided Des..

[6]  Duc Truong Pham,et al.  A comparison of rapid prototyping technologies , 1998 .

[7]  Han Tong Loh,et al.  Considerations and selection of optimal orientation for different rapid prototyping systems , 1999 .

[8]  J. Giannatsis,et al.  A study of the build-time estimation problem for Stereolithography systems , 2001 .

[9]  Kenneth Cooper,et al.  Rapid Prototyping Technology: Selection and Application , 2001 .

[10]  Chih-Chen Chang,et al.  Selection of training samples for model updating using neural networks , 2002 .

[11]  Ren C. Luo,et al.  The development of Web-based e-business system for rapid prototyping manufacturing , 2003, IECON'03. 29th Annual Conference of the IEEE Industrial Electronics Society (IEEE Cat. No.03CH37468).

[12]  Stergios Maropoulos,et al.  Process build‐time estimator algorithm for laminated object manufacturing , 2004 .

[13]  Kwan H. Lee,et al.  Determination of optimal build direction in rapid prototyping with variable slicing , 2006 .

[14]  James Tannock,et al.  The training of neural networks to model manufacturing processes , 2005, J. Intell. Manuf..

[15]  Zhao Jibin.  Determination of optimal build orientation based on satisfactory degree theory for RPT , 2005, Ninth International Conference on Computer Aided Design and Computer Graphics (CAD-CG'05).

[16]  Kwan H. Lee,et al.  Determination of the optimal build direction for different rapid prototyping processes using multi-criterion decision making , 2006 .

[17]  Christopher Tuck,et al.  Empirical laser sintering time estimator for Duraform PA , 2006 .

[18]  Yucheng Ding,et al.  Price quotation methodology for stereolithography parts based on STL model , 2007, Comput. Ind. Eng..

[19]  Ludrick Barnard,et al.  Stereolithography build time estimation based on volumetric calculations , 2008 .

[20]  Joaquim Ciurana,et al.  Neural-network-based model for build-time estimation in selective laser sintering , 2009 .

[21]  D. Rajenthirakumar,et al.  Analysis of interaction between geometry and efficiency of impeller pump using rapid prototyping , 2009 .

[22]  Luca Di Angelo,et al.  Parametric cost analysis for web-based e-commerce of layer manufactured objects , 2010 .

[23]  Farshad Barazandeh,et al.  Build time estimator for determining optimal part orientation , 2010 .

[24]  S. R. Devadasan,et al.  Agile product development through CAD and rapid prototyping technologies: an examination in a traditional pump-manufacturing company , 2010 .