Quantitative suggestions for build orientation selection

Qie, Longfei; Jing, Shikai; Lian, Ruichao; Chen, Ying; Liu, Jihong

doi:10.1007/s00170-018-2295-0

Cited by 14 publications

(21 citation statements)

References 42 publications

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“…In the method of [107], the features of 3D models were divided into hole, revolution surface, round surface, thin AM processes Convex hull generation MMFs [111] AM processes Feature recognition WSM [112] AM processes Feature recognition WSM [113] LPBF Feature recognition WSM [114] SLA Quaternion rotation OWAO [115] AM processes Feature recognition FAOs [116] SLA, FDM Quaternion rotation WSM [117] AM processes Facet clustering WSM [118,119] LPBF Facet clustering WSM OOF stands for overall objective function; PP stands for part property; PA stands for part accuracy; SQ stands for surface quality; SS stands for support structure; BT stands for build time; BC stands for build cost; PT stands for post-processing time; PC stands for post-processing cost; MMFs stands for min-max functions; WSM stands for weighted sum model; DF stands for deviation function; 4 processes include SLA, SLS, FDM, LOM; OWAO stands for ordered weighted averaging operator; FAOs stands for fuzzy aggregation operators structure, inter/external surface, plane, freeform surface, inclined plane, and overhang. On the basis of these features, a set of rules were designed to generate the ABOs for an SLA part with an objective of improving the surface quality of the part.…”

Section: Two-step Methods Based On Feature Recognitionmentioning

confidence: 99%

Status, issues, and future of computer-aided part orientation for additive manufacturing

Qin

Shi

et al. 2021

Int J Adv Manuf Technol

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Part orientation is a critical task in the process of additive manufacturing product realisation. Recently, various computer-aided methods for this task have been presented in the literature. The coexistence of different methods generates a series of questions: What are the common characteristics of these methods? What are the specific characteristics of each method? What are the main issues in computer-aided part orientation for additive manufacturing currently? What are the potential research directions in this field in the future? To approach these questions, a review of the existing computer-aided part orientation methods for additive manufacturing is presented in this paper. This review starts with a clarification of a part orientation problem and a classification of the existing methods into two categories according to their process of solving the problem. An overview of the representative methods in each category is then carried out from the aspects of approaches for orientation search, generation, or selection, estimation of build orientation factors, determination of weights of factors, establishment of overall objective function, and demonstration of effectiveness. After that, a discussion about the main issues in computer-aided part orientation for additive manufacturing is documented based on the overview. Finally, a suggestion of some future research directions in this field is reported.

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Section: Two-step Methods Based On Feature Recognitionmentioning

confidence: 99%

Status, issues, and future of computer-aided part orientation for additive manufacturing

Qin

Shi

et al. 2021

Int J Adv Manuf Technol

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“…The basic principle of this category of methods is to use specific MADM techniques to estimate the overall score of each ABO in the case of comprehensively considering multiple attributes, and to sort all ABOs according to their overall scores (from high to low), and to select the ABO ranked first as the OBO. Representative methods based on this principle are the methods proposed by Pham et al (1999), West et al (2001), Byun and Lee (2006), Chen et al (2008), Zhang et al (2016Zhang et al ( , 2018, Ransikarbum and Kim (2017), and Qie et al (2018). A brief summarisation of these methods is listed in Table 2.…”

Section: Moo Methodsmentioning

confidence: 99%

“…Benchmark Relationships Ranking OBO Cheng et al (1995) Example 1 Independent Pham et al (1999) Example 1 Independent -O 1 Pandey et al (2004) Example 1 Independent -O 1 Byun and Lee (2006) Example 1 Independent -O 1 Canellidis et al (2009) Example 1 Independent -O 1 Zhang et al (2016) Example 1 Correlative Ransikarbum and Kim (2017) Qie et al (2018) Example 1 Independent Zhang et al (2016) Example 2 Correlative…”

Section: Methodsmentioning

confidence: 99%

“…The first example is a classic example of OBO determination. It was developed by Cheng et al (1995) and has been used to illustrate the OBO determination methods presented by Pham et al (1999), Pandey et al (2004), Byun and Lee (2006), Canellidis et al (2009), Zhang et al (2016), Ransikarbum and , and Qie et al (2018). In this example, a part whose 3D model is shown in Fig.…”

Section: Examples and Comparisonsmentioning

confidence: 99%

“…, Paul and Anand (2015),Ezair et al (2015),Zhang et al (2016),Brika et al (2017),Ransikarbum and Kim (2017), Chowdhury et al(2018),Qie et al (2018),Zhang et al (2018), andGolmohammadi and Khodaygan (2019), volume of support structure is considered as a key attribute.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Determination of optimal build orientation for additive manufacturing using Muirhead mean and prioritised average operators

Qin

Scott

et al. 2019

J Intell Manuf

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Build orientation determination is one of the essential process planning tasks in additive manufacturing since it has crucial effects on the part quality, post-processing, build time and cost, etc. This paper introduces a method based on fuzzy multiattribute decision making to determine the optimal build orientation from a finite set of alternatives. The determination process includes two major steps. In the first step, attributes that are considered in the determination and heterogeneous relationships of which are firstly identified. A fuzzy decision matrix is then constructed and normalised based on the values of the identified attributes, which are quantified by a set of fuzzy numbers. In the second step, two fuzzy number aggregation operators are developed to aggregate the fuzzy information in the normalised matrix. By comparing the aggregation results, a ranking of all alternative build orientations can then be generated. Two determination examples are used to demonstrate the working process of the proposed method. Qualitative and quantitative comparisons between the proposed method and other methods are carried out to demonstrate its feasibility, effectiveness, and advantages.

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A reliable build orientation optimization method in additive manufacturing: the application to FDM technology

Angelo

Stefano

Dolatnezhadsomarin

et al. 2020

Int J Adv Manuf Technol

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Quantitative suggestions for build orientation selection

Cited by 14 publications

References 42 publications

Status, issues, and future of computer-aided part orientation for additive manufacturing

Status, issues, and future of computer-aided part orientation for additive manufacturing

Determination of optimal build orientation for additive manufacturing using Muirhead mean and prioritised average operators

A reliable build orientation optimization method in additive manufacturing: the application to FDM technology

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