Orientation Optimization in Additive Manufacturing: Evaluation of Recent Trends

2021 ◽  
Author(s):  
Jannatul Bushra ◽  
Hannah D. Budinoff

Abstract Build orientation in additive manufacturing influences the mechanical properties, surface quality, build time, and cost of the product. Rather than relying on trial-and-error or prior experience, the choice of build orientation can be formulated as an optimization problem. Consequently, orientation optimization has been a popular research topic for several decades, with new optimization methods being proposed each year. However, despite the rapid pace of research in additive manufacturing, there has not been a critical comparison of different orientation optimization methods. In this study, we present a critical review of 50 articles published since 2015 that proposes a method for orientation optimization for additive manufacturing. We classify included papers by optimization methods used, AM process modeled, and objective functions considered. While the pace of research in recent years has been rapid, most approaches we identified utilized similar objective functions and computational optimization techniques to research from the early 2000s. The most common optimization method in the included research was exhaustive search. Most methods focused on broad applicability to all additive manufacturing processes, rather than a specific process, but a few works focused on powder bed fusion and material extrusion. We also identified several areas for future work including integration with other design and process planning tasks such as topology optimization, more focus on practical implementation with users, testing of computational efficiency, and experimental validation of utilized objective functions.

2012 ◽  
Vol 16 (3) ◽  
pp. 873-891 ◽  
Author(s):  
W. J. Vanhaute ◽  
S. Vandenberghe ◽  
K. Scheerlinck ◽  
B. De Baets ◽  
N. E. C. Verhoest

Abstract. The calibration of stochastic point process rainfall models, such as of the Bartlett-Lewis type, suffers from the presence of multiple local minima which local search algorithms usually fail to avoid. To meet this shortcoming, four relatively new global optimization methods are presented and tested for their ability to calibrate the Modified Bartlett-Lewis Model. The list of tested methods consists of: the Downhill Simplex Method, Simplex-Simulated Annealing, Particle Swarm Optimization and Shuffled Complex Evolution. The parameters of these algorithms are first optimized to ensure optimal performance, after which they are used for calibration of the Modified Bartlett-Lewis model. Furthermore, this paper addresses the choice of weights in the objective function. Three alternative weighing methods are compared to determine whether or not simulation results (obtained after calibration with the best optimization method) are influenced by the choice of weights.


2020 ◽  
Vol 108 (1-2) ◽  
pp. 263-276 ◽  
Author(s):  
Luca Di Angelo ◽  
Paolo Di Stefano ◽  
Azam Dolatnezhadsomarin ◽  
Emanuele Guardiani ◽  
Esmaile Khorram

Author(s):  
Michael Barclift ◽  
Andrew Armstrong ◽  
Timothy W. Simpson ◽  
Sanjay B. Joshi

Cost estimation techniques for Additive Manufacturing (AM) have limited synchronization with the metadata of 3D CAD models. This paper proposes a method for estimating AM build costs through a commercial 3D solid modeling program. Using an application programming interface (API), part volume and surface data is queried from the CAD model and used to generate internal and external support structures as solid-body features. The queried data along with manipulation of the part’s build orientation allows users to estimate build time, feedstock requirements, and optimize parts for AM production while they are being designed in a CAD program. A case study is presented with a macro programmed using the SolidWorks API with costing for a metal 3D-printed automotive component. Results reveal that an imprecise support angle can under-predict support volume by 34% and build time by 20%. Orientation and insufficient build volume packing can increase powder depreciation costs by nearly twice the material costs.


Author(s):  
Ibrahim Sobhi ◽  
Abdelmadjid Dobbi ◽  
Oussama Hachana

AbstractThe rate of penetration (ROP) optimization is one of the most important factors in improving drilling efficiency, especially in the downturn time of oil prices. This process is crucial in the well planning and exploration phases, where the selection of the drilling bits and parameters has a significant impact on the total cost and time of the drilling operation. Thus, the optimization and best selection of the drilling parameters are critical. Optimization of ROP is difficult due to the complexity of the relationship between the drilling variables and the ROP. For this reason, the development of high-performance computer systems, predictive models, and algorithms will be the best solution. In this study, a new investigation approach for ROP optimization has been done regarding different ROP models (Maurer, Bingham, Bourgoyne and Young models), algorithms (Multiple regression, ant colony optimization (ACO), fminunc, fminsearch, fsolve, lsqcurvefit, lsqnonlin), and different objective functions. The well-known data from the Louisiana field in an offshore well have been used to compare the used parameter estimation approach with other techniques. Indeed, datasets from an onshore well in the Hassi Messaoud Algerian field are explored. The results confirmed the superiority and the effectiveness of B&Y models compared to Bingham and Maurer models. Fminsearch, lsqcurvefit, ACO, and Excel (GRG) algorithms give the best results in ROP prediction while the application of the MNLR approach. Using the mean squared error (MSE) and the determination coefficient (R$$^{2}$$ 2 ) as objective functions significantly increases the accuracy prediction where the results given are ($$R=0.9522$$ R = 0.9522 , $$RMSE=2.85$$ R M S E = 2.85 ) and ($$R= 0.9811$$ R = 0.9811 , $$RMSE=4.08$$ R M S E = 4.08 ) for Wells 1 and 2, respectively. This study validates the application of B&Y model in both onshore and offshore wells. The findings reveal to deal with data limitation problems in ROP prediction. Simple and effective optimization techniques that require less memory space and computational time have been provided.


2015 ◽  
Vol 80 (2) ◽  
pp. 253-264 ◽  
Author(s):  
N. Anu ◽  
S. Rangabhashiyam ◽  
Antony Rahul ◽  
N. Selvaraju

Balance (CMB) model has been extensively used in order to determine source contribution for particulate matters (size diameters less than 10 ?m and 2.5 ?m) in the air quality analysis. A comparison of the source contribution estimated from the three CMB models (CMB 8.2, CMB-fmincon and CMB-GA) have been carried out through optimization techniques such as ?fmincon? (CMB-fmincon) and genetic algorithm (CMB-GA) using MATLAB. The proposed approach has been validated using San Joaquin Valley Air Quality Study (SJVAQS) California Fresno and Bakersfield PM10 and PM2.5 followed with Oregon PM10 data. The source contribution estimated from CMB-GA was better in source interpretation in comparison with CMB8.2 and CMB-fmincon. The performance accuracy of three CMB approaches were validated using R-square, reduced chi-square and percentage mass tests. The R-square (0.90, 0.67 and 0.81, 0.83), Chi-square (0.36, 0.66 and 0.65, 0.43) and percentage mass (67.36 %, 55.03 % and 94.24 %, 74.85 %) of CMB-GA showed high correlation for PM10, PM2.5 Fresno and Bakersfield data respectively. To make a complete decision, the proposed methodology has been bench marked with Portland, Oregon PM10 data with best fit with R2 (0.99), Chi-square (1.6) and percentage mass (94.4 %) from CMB-GA. Therefore, the study revealed that CMB with genetic algorithm optimization method holds better stability in determining the source contributions.


Geophysics ◽  
2021 ◽  
Vol 86 (3) ◽  
pp. E209-E224
Author(s):  
Daniele Colombo ◽  
Ersan Turkoglu ◽  
Weichang Li ◽  
Ernesto Sandoval-Curiel ◽  
Diego Rovetta

Machine learning, and specifically deep-learning (DL) techniques applied to geophysical inverse problems, is an attractive subject, which has promising potential and, at the same time, presents some challenges in practical implementation. Some obstacles relate to scarce knowledge of the searched geologic structures, a problem that can limit the interpretability and generalizability of the trained DL networks when applied to independent scenarios in real applications. Commonly used (physics-driven) least-squares optimization methods are very efficient local optimization techniques but require good starting models close to the correct solution to avoid local minima. We have developed a hybrid workflow that combines both approaches in a coupled physics-driven/DL inversion scheme. We exploit the benefits and characteristics of both inversion techniques to converge to solutions that typically outperform individual inversion results and bring the solution closer to the global minimum of a nonconvex inverse problem. The completely data-driven and self-feeding procedure relies on a coupling mechanism between the two inversion schemes taking the form of penalty functions applied to the model term. Predictions from the DL network are used to constrain the least-squares inversion, whereas the feedback loop from inversion to the DL scheme consists of the network retraining with partial results obtained from inversion. The self-feeding process tends to converge to a common agreeable solution, which is the result of two independent schemes with different mathematical formalisms and different objective functions on the data and model misfit. We determine that the hybrid procedure is converging to robust and high-resolution resistivity models when applied to the inversion of the synthetic and field transient electromagnetic data. Finally, we speculate that the procedure may be adopted to recast the way we solve inverse problems in several different disciplines.


Author(s):  
Sushmit Chowdhury ◽  
Kunal Mhapsekar ◽  
Sam Anand

Significant advancements in the field of additive manufacturing (AM) have increased the popularity of AM in mainstream industries. The dimensional accuracy and surface finish of parts manufactured using AM depend on the AM process and the accompanying process parameters. Part build orientation is one of the most critical process parameters, since it has a direct impact on the part quality measurement metrics such as cusp error, manufacturability concerns for geometric features such as thin regions and small fusible openings, and support structure parameters. In conjunction with the build orientation, the cyclic heating and cooling of the material involved in the AM processes lead to nonuniform deformations throughout the part. These factors cumulatively affect the design conformity, surface finish, and the postprocessing requirements of the manufactured parts. In this paper, a two-step part build orientation optimization and thermal compensation methodology is presented to minimize the geometric inaccuracies resulting in the part during the AM process. In the first step, a weighted optimization model is used to determine the optimal build orientation for a part with respect to the aforementioned part quality and manufacturability metrics. In the second step, a novel artificial neural network (ANN)-based geometric compensation methodology is used on the part in its optimal orientation to make appropriate geometric modifications to counteract the thermal effects resulting from the AM process. The effectiveness of this compensation is assessed on an example part using a new point cloud to part conformity metric and shows significant improvements in the manufactured part's geometric accuracy.


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