Parametric error modeling and software error compensation for rapid prototyping

Tong, Kun; Lehtihet, E. Amine; Joshi, Sanjay

doi:10.1108/13552540310502202

Cited by 70 publications

(49 citation statements)

References 31 publications

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“…Error avoidance can be performed on the data preparation step, but because of the nature of the errors, avoidance may not be an option for process material error. Error correction is sometimes a better option for the other two categories, albeit a more complicated one [21].…”

Section: Challenge 5: Error Controlmentioning

confidence: 99%

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Ten challenges in 3D printing

Oropallo

Piegl

2015

Engineering with Computers

320

170

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predominantly in two ways: (1) material is removed from a stock, or (2) material is added to a part in progress that started out as a non-existent entity, e.g., an empty moving table or a box of powder with no part being solidified, depending on the type of manufacturing process being deployed. 3D printing is a technology that adds material to produce the part, and hence, it is also called additive manufacturing. Our notion of printing involves transferring ink to paper, line-by-line until the document is completed. Generalizing this process to 3D would involve transferring material to 3D space layer-by-layer till the object is completed. Since most of the 3D printers manufacture objects layer-by-layer, the term 3D printing struck.Since our 2D printers have become such common, and by-and-large, fairly reliable machines, this may create the impression that going from 2D to 3D is a straightforward task. The third dimension has always posed a challenge to mankind (according to surveys we performed, a significant percentage of people are 3D blind), so much so that we believe that 3D printing will be no exception. The ten challenges below illustrate the rocky road ahead and show that this technology may not be as disruptive, at least in the short term, as the media wants the public to believe. The list is by no means exhaustive and it represents our understanding of and opinion about the technology. We selected what we believed to be the most relevant papers for this review. Challenge 1: shape optimizationOptimization of the design space is made possible with additive manufacturing since the process has the ability to fill the interior part of the object in practically infinite ways. The design space is any area of the model that can be Abstract Three dimensional printing has gained considerable interest lately due to the proliferation of inexpensive devices as well as open source software that drive those devices. Public interest is often followed by media coverage that tends to sensationalize technology. Based on popular articles, the public may create the impression that 3D printing is the Holy Grail; we are going to print everything as one piece, traditional manufacturing is at the brink of collapse, and exotic applications, such as cloning a human body by 3D bio-printing, are just around the corner. The purpose of this paper is to paint a more realistic picture by identifying ten challenges that clearly illustrate the limitations of this technology, which makes it just as vulnerable as anything else that had been touted before as the next game changer.

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Section: Challenge 5: Error Controlmentioning

confidence: 99%

“…These errors can typically be seen and calculated before the part is actually printed and can be assessed. Tessellation error correction is dependent on the original CAD model being accurately represented [21]. The problem lies in approximating freeform shapes with triangles.…”

Section: Errors Before Printingmentioning

confidence: 99%

Ten challenges in 3D printing

Oropallo

Piegl

2015

Engineering with Computers

320

170

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“…Among them [6], [7] mapped the "confounded effects of all errors of SLA machine and FDM machine" into a "virtual" parametric machine error model and a polynomial regression model, which are used to analyze the individual deviations along x, y and z axes and compensate the shape deviations of final products. [8] 2014 IEEE International Conference on Automation Science and Engineering (CASE) Taipei, Taiwan, August 18-22, 2014 978-1-4799-5283-0/14/$31.00 ©2014 IEEE claimed to establish a generic approach to model and predict part deviations and subsequently derived an optimal compensation plan to achieve dimensional accuracy.…”

Section: B Related Work On Shape Compensation Studies In Ammentioning

confidence: 99%

Shape deviation modeling for fused deposition modeling processes

Song

Wang

Huang

et al. 2014

2014 IEEE International Conference on Automation Science and Engineering (CASE)

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3D Printing or Additive Manufacturing (AM), refers to a new class of technologies of making products directly from any three-dimensional digital models. Broader applications of AM require to lower the cost of AM machines. However, products fabricated by low-end machines suffer from the issue of low dimensional accuracy. In this paper, we intend to address this issue for Fused Deposition Modeling (FDM) process -one of mostly adopted AM technologies. Based on the FDM mechanism, we attribute the dimensional inaccuracy to two significant error sources that affect the shape of the product consecutively: (i) positioning error of the extruder and (ii) shape deformation induced by processing error including phase change and other variations occurred. We first adopt Kriging method to model the extruder positioning error, which is treated as design input error under a novel framework of modeling the product shape deformation. Experimental results and case studies demonstrate the effectiveness of the predictive modeling framework, which can be applied to compensate dimensional error of 3D printed products.

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“…At present there is no general procedure for calibrating this equipment established by national metrology institutes, although work has been carried out in this field [1,3]. In particular, there is abundant scientific literature where you can find different proposals for calibration procedures or verification of the vision system (camera) as well as for the machine system based on verification procedures commonly used in the CMMs [4][5][6].…”

Section: Introductionmentioning

confidence: 98%