Multi-Objective Build Orientation Optimization for Powder Bed Fusion by Laser

Brika, Salah Eddine; Zhao, Yaoyao Fiona; Brochu, Mathieu; Mezzetta, Justin

doi:10.1115/1.4037570

Cited by 51 publications

(20 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This makes attribution of weights even more complex, considering that even a small variation of them can significantly affect the final solution. In this sense, an interesting proposition is given in [20], which evaluates the weights of OFs adopting a fuzzy-based approach. Last but not least, weighted sum-based optimization leads to a single result instead of a set of solutions to be later compared.…”

Section: Optimization Methods and Decision Criteriamentioning

confidence: 99%

“…The validity of this formulation was confirmed by several published types of research that use this definition of cost in their optimization functions, even the most recent. Someone, like Brika et al [20], tried to extend the formulation to the new emerging technologies like PBF. Others, such as Byun and Lee [13], Canellidis et al [14], proposed a simplification of it, observing that in most practical cases the pre-processing cost c pre is negligible compared to the others.…”

Section: Costmentioning

confidence: 99%

“…SLS Phatak et al, 2012 [16] Build time (number of layers), material utilizzation factor. Brika et al, 2017 [20] Build time (number of layers, part volume, part support), energy cost, indirect cost (portion of build-plate).…”

Section: Authors Considered Cost Components Technologymentioning

confidence: 99%

“…Brika et al [20] treated the mechanical properties of AM components in a multi-optimization problem. They investigate the influence of process parameters on mechanical properties through stochastic models.…”

Section: Mechanical Propertiesmentioning

confidence: 99%

See 3 more Smart Citations

Search for the Optimal Build Direction in Additive Manufacturing Technologies: A Review

Angelo

Stefano

Guardiani

2020

JMMP

View full text Add to dashboard Cite

By additive manufacturing technologies, an object is produced deposing material layer by layer. The piece grows along the build direction, which is one of the main manufacturing parameters of Additive Manufacturing (AM) technologies to be set-up. This process parameter affects the cost, quality, and other important properties of the manufactured object. In this paper, the Objective Functions (OFs), presented in the literature for the search of the optimal build direction, are considered and reviewed. The following OFs are discussed: part quality, surface quality, support structure, build time, manufacturing cost, and mechanical properties. All of them are distinguished factors that are affected by build direction. In the first part of the paper, a collection of the most significant published methods for the estimation of the factors that most influence the build direction is presented. In the second part, a summary of the optimization techniques adopted from the reviewed papers is presented. Finally, the advantages and disadvantages are briefly discussed and some possible new fields of exploration are proposed.

show abstract

Section: Optimization Methods and Decision Criteriamentioning

confidence: 99%

Section: Costmentioning

confidence: 99%

Section: Authors Considered Cost Components Technologymentioning

confidence: 99%

Section: Mechanical Propertiesmentioning

confidence: 99%

See 2 more Smart Citations

Search for the Optimal Build Direction in Additive Manufacturing Technologies: A Review

Angelo

Stefano

Guardiani

2020

JMMP

View full text Add to dashboard Cite

show abstract

“…(2) Preventing build failures, such as re-coater crashes by careful calibration of the machine and avoiding malfunction of the optical train, e.g., cleaning and maintenance of the optical lens used for focusing the laser-which is inclined to become coated with residue from spatter and material vaporization, particularly, during long build cycles. (3) Precluding build failures resulting from poor design of the part, e.g., steep overhang geometries, thin walls, overly fine features, ill-suited placement of support structures, and improper orientation of the part [5,[20][21][22]. (4) Maintaining the purity of the input feed stock, such as taking care to avoid material cross-contamination, in the form of organic, e.g., oil, grease, lubricants, hair, plastics, and glue, or inorganic wear debris or other powders ingredients in the feedstock powder.…”

Section: Introductionmentioning

confidence: 99%

In-Process Monitoring of Material Cross-Contamination Defects in Laser Powder Bed Fusion

Montazeri

Yavari

Rao

et al. 2018

Journal of Manufacturing Science and Engineering

View full text Add to dashboard Cite

The goal of this work is to detect the onset of material cross-contamination in laser powder bed fusion (L-PBF) additive manufacturing (AM) process using data from in situ sensors. Material cross-contamination refers to trace foreign materials that may be introduced in the powder feedstock used in the process due to reasons such as poor cleaning of the machine after previous builds or inadequate quality control during production and storage of the powder. Material cross-contamination may lead to deleterious changes in the microstructure of the AM part and consequently affect its functional properties. Accordingly, the objective of this work is to develop and apply a spectral graph theoretic approach to detect the occurrence of material cross-contamination in real-time as the part is being built using in-process sensors. The central hypothesis is that transforming the process signals in the spectral graph domain leads to early and more accurate detection of material cross-contamination in L-PBF compared to the traditional delay-embedded Bon-Jenkins stochastic time series analysis techniques, such as autoregressive (AR) and autoregressive moving average (ARMA) modeling. To test this hypothesis, Inconel alloy 625 (UNS alloy 06625) test parts were made at Edison Welding Institute (EWI) on a custom-built L-PBF apparatus integrated with multiple sensors, including a silicon photodetector (with 300 nm to 1100 nm optical wavelength). During the process, two types of foreign contaminant materials, namely, tungsten and aluminum particulates, under varying degrees of severity were introduced. To detect cross-contamination in the part, the photodetector sensor signatures were monitored hatch-by-hatch in the form of spectral graph transform coefficients. These spectral graph coefficients are subsequently tracked on a Hotelling T2 statistical control chart. Instances of Type II statistical error, i.e., probability of failing to detect the onset of material cross-contamination, were verified against X-ray computed tomography (XCT) scans of the part to be within 5% in the case of aluminum contaminant particles. In contrast, traditional stochastic time series modeling approaches, e.g., ARMA, had corresponding Type II error exceeding 15%. Furthermore, the computation time for the spectral graph approach was found to be less than one millisecond, compared to nearly 100 ms for the traditional time series models tested.

show abstract

A Multi-objective Approach to Solve the Build Orientation Problem in Additive Manufacturing

Matos

Rocha

Costa

et al. 2019

Computational Science and Its Applications – ICCSA 2019

View full text Add to dashboard Cite

Additive manufacturing (AM) has been increasingly used in the creation of three-dimensional objects, layer-by-layer, from threedimensional (3D) computer-aided design (CAD) models. The problem of determining the 3D model printing orientation can lead to reduced amount of supporting material, build time, costs associated with the deposited material, labor costs, and other factors. This problem has been formulated and studied as a single-objective optimization problem. More recently, due to the existence and relevance of considering multiple criteria, multi-objective approaches have been developed. In this paper, a multi-objective optimization approach is proposed to solve the part build orientation problem taking into account the support area characteristics and the build time. Therefore, the weighted Tchebycheff scalarization method embedded in the Electromagnetism-like Algorithm will be used to solve the part build orientation bi-objective problem of four 3D CAD models. The preliminary results seem promising when analyzing the Pareto fronts obtained for the 3D CAD models considered. Concluding, the multi-objective approach effectively solved the build orientation problem in AM, finding several compromise solutions.

show abstract

Multi-Objective Build Orientation Optimization for Powder Bed Fusion by Laser

Cited by 51 publications

References 10 publications

Search for the Optimal Build Direction in Additive Manufacturing Technologies: A Review

Search for the Optimal Build Direction in Additive Manufacturing Technologies: A Review

In-Process Monitoring of Material Cross-Contamination Defects in Laser Powder Bed Fusion

A Multi-objective Approach to Solve the Build Orientation Problem in Additive Manufacturing

Contact Info

Product

Resources

About