Uncertainty quantification and robust modeling of selective laser melting process using stochastic multi-objective approach

Gholaminezhad, Iman; Assimi, Hirad; Jamali, Ali; Vajari, Dina Ashouri

doi:10.1007/s00170-015-8238-0

Cited by 21 publications

(5 citation statements)

References 56 publications

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“…In this work, previously developed multi-objective uniform-diversity genetic-programming code is used for Pareto modeling of RSOCs. Further details of this algorithm can be found in [24,25]. Experimental data of reversible operation with Ni-YSZ anode and LSM-YSZ cathode materials are gathered from the literature [26,27].…”

Section: Modeling and Optimization Methodsmentioning

confidence: 99%

Multi-objective modeling, uncertainty analysis, and optimization of reversible solid oxide cells

Salehi

Gholaminezhad

2018

Int J Energy Environ Eng

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Reversible solid oxide cells can provide efficient and cost-effective scheme for electrical-energy storage applications. However, this technology faces many challenges from material development to system-level operational parameters , which should be tackle for practical purposes. Accordingly, this study focuses on developing novel robust artificial intelligence-based blackbox models to optimize operational variables of the system. A genetic-programming algorithm is used for Pareto modeling of reversible solid oxide cells in a multi-objective fashion based on experimental input-output data. The robustness of the obtained optimal model evaluated using Monte Carlo simulations technique. An optimization study adopted to optimize the operating parameters, such as temperature and fuel composition using a differential evolution algorithm. The objective functions that have been considered for Pareto multi-objective modeling process are training error and model complexity. In addition, the discrepancy between maximum and minimum output voltage in the whole operation of the system is chosen as the optimization process objective function. The robustness of the optimal trade-off model is shown in terms of statistical indices for varied uncertainty levels from 1 to 10%. The optimized operational condition based on the suggested model reveals optimal intermediate temperature of 762 °C and fuel mixture of about 29% H 2 , 25% H 2 O, and 14% CO.

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Section: Modeling and Optimization Methodsmentioning

confidence: 99%

Multi-objective modeling, uncertainty analysis, and optimization of reversible solid oxide cells

Salehi

Gholaminezhad

2018

Int J Energy Environ Eng

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“…Here the information used to validate the simplified model can be used to estimate the model uncertainty, which can then be treated as an additional source of uncertainty in the propagation step, using a statistical model of the error based on the results of the validation. This approach has been used in AM applications to evaluate uncertainty associated with the use of a multiscale model [141,142].…”

Section: General Overviewmentioning

confidence: 99%

Metal powder bed fusion process chains: an overview of modelling techniques

et al. 2021

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Metal powder bed fusion (MPBF) is not a standalone process, and other manufacturing technologies, such as heat treatment and surface finishing operations, are often required to achieve a high-quality component. To optimise each individual process for a given component, its progression through the full process chain must be considered and understood, which can be achieved through the use of validated models. This article aims to provide an overview of the various modelling techniques that can be utilised in the development of a digital twin for MPBF process chains, including methods for data transfer between physical and digital entities and uncertainty evaluation. An assessment of the current maturity of modelling techniques through the use of technology readiness levels is conducted to understand their maturity. Summary remarks highlighting the advantages and disadvantages in physics-based modelling techniques used in MPBF research domains (i.e. prediction of: powder distortion; temperature; material properties; distortion; residual stresses; as well as topology optimisation), post-processing (i.e. modelling of: machining; heat treatment; and surface engineering), and digital twins (i.e. simulation of manufacturing process chains; interoperability; and computational performance) are provided. Future perspectives for the challenges in these MPBF research domains are also discussed and summarised.

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“…2.3 and 2.4. [19][20][21]29,35] is a numerical algorithm that relies on repeated random sampling. N samples of X are first generated from the distributions of X, and then the limit-state function is evaluated at the samples of X, resulting in samples of Y.…”

Section: Uncertainty Quantification and Reliability Analysismentioning

confidence: 99%

Probabilistic Feasibility Design of a Laser Powder Bed Fusion Process Using Integrated First-Order Reliability and Monte Carlo Methods

Meng

McWilliams

et al. 2021

Journal of Manufacturing Science and Engineering

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Quality inconsistency due to uncertainty hinders the extensive applications of laser powder bed fusion (L-PBF) additive manufacturing process. To address this issue, this study proposes a new and efficient probabilistic method for the reliability analysis and design of the L-PBF process. The method determines a feasible region of the design space for given design requirements at specified reliability levels. If a design point falls into the feasible region, the design requirement will be satisfied with a probability higher or equal to the specified reliability. Since the problem involves the inverse reliability analysis that requires calling the direct reliability analysis repeatedly, directly using Monte Carlo simulation (MCS) is computationally intractable, especially for a high reliability requirement. In this work, a new algorithm is developed to combine MCS and the First Order Reliability Method (FORM). The algorithm finds the initial feasible region quickly by FORM and then updates it with higher accuracy by MCS. The method is applied to several case studies, where the normalized enthalpy criterion is used as a design requirement. The feasible regions of the normalized enthalpy criterion are obtained as contours with respect to the laser power and laser scan speed at different reliability levels, accounting for uncertainty in seven processing and material parameters. The results show that the proposed method dramatically alleviates the computational cost while maintaining high accuracy. This work provides a guidance for the process design with required reliability.

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Uncertainty quantification and robust modeling of selective laser melting process using stochastic multi-objective approach

Cited by 21 publications

References 56 publications

Multi-objective modeling, uncertainty analysis, and optimization of reversible solid oxide cells

Multi-objective modeling, uncertainty analysis, and optimization of reversible solid oxide cells

Metal powder bed fusion process chains: an overview of modelling techniques

Probabilistic Feasibility Design of a Laser Powder Bed Fusion Process Using Integrated First-Order Reliability and Monte Carlo Methods

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