Optimal design of electric machine with efficient handling of constraints and surrogate assistance

Khoshoo, Bhuvan; Blank, Julian; Pham, Thang Q.; Deb, Kalyanmoy; Foster, Shanelle N.

doi:10.1080/0305215x.2022.2152805

Cited by 4 publications

(2 citation statements)

References 44 publications

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“…Other, more complicated operations such as rotation of the wiring, quick rolls, bends, and/or translation of the terminations are also automatically performed. This situation enables the automatic spreading of the 3D harness model into a 2D Formboard without errors [34].…”

Section: Automation Scriptmentioning

confidence: 99%

Artificial Intelligence Approach in Aerospace for Error Mitigation

Bautista-Hernández,

Martín-Prats

2024

Aerospace

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Many of the reports created at assembly lines, where all components of an aircraft are installed, frequently indicate that errors threaten safety. The proposed methodology in this study evaluates error prediction and risk mitigation to prevent failures and their consequences. The results linked to a typical electrical harness manufacture of a military aircraft estimated reductions of 93% in time and 90% in error during the creation of engineering manufacturing processes using AI techniques. However, traditional risk assessments methods struggle to identify and mitigate errors effectively. Thus, developing an advanced methodology to ensure systems safety is needed. This paper addresses how innovative AI technology solutions can overcome these challenges, mitigate error risks, and enhance safety in aerospace. Technologies, such as artificial intelligence, predictive algorithms, machine learning, and automation, can play a key role in enhancing safety. The aim of this study is to develop a model that considers the factors that can potentially contribute to error creation, through an artificial intelligence (AI) approach. The specific AI techniques used such as support vector machine, random forest, logistic regression, K-nearest neighbor, and XGBoost (Python 3.8.5) show good performance for use in error mitigation. We have compared the modeled values obtained in this study with the experimental ones. The results confirm that the best metrics are obtained by using support vector machine and logistic regression. The smallest deviation between the measured and modeled values for these AI methods do not exceed 5%. Furthermore, using advancements in machine learning methods can enhance error mitigation in aerospace. The use of AutoML can play a key role in automatically finding an appropriate model which provides the best performance metrics and therefore the most reliable forecast for data prediction and error mitigation.

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Section: Automation Scriptmentioning

confidence: 99%

Artificial Intelligence Approach in Aerospace for Error Mitigation

Bautista-Hernández,

Martín-Prats

2024

Aerospace

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“…Large-Scale Multi-Objective Optimization Problems (LSMOPs), applied in territories like engineering design and logistics [1][2][3][4], share the conflicting objectives characteristic of Multi-Objective Optimization Problems (MOPs) [5][6][7]. However, their high-dimensional decision variables necessitate specialized strategies for effective resolution.…”

Section: Introductionmentioning

confidence: 99%

Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

Chen,

Zhang,

et al. 2024

CMES

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The large-scale multi-objective optimization algorithm (LSMOA), based on the grouping of decision variables, is an advanced method for handling high-dimensional decision variables. However, in practical problems, the interaction among decision variables is intricate, leading to large group sizes and suboptimal optimization effects; hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables (MOEAWOD) is proposed in this paper. Initially, the decision variables are perturbed and categorized into convergence and diversity variables; subsequently, the convergence variables are subdivided into groups based on the interactions among different decision variables. If the size of a group surpasses the set threshold, that group undergoes a process of weighting and overlapping grouping. Specifically, the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision variables. The decision variable with the highest interaction in the group is identified and disregarded, and the remaining variables are then reclassified into subgroups. Finally, the decision variable with the strongest interaction is added to each subgroup. MOEAWOD minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups, which contributed to the optimized direction of convergence and diversity exploration with different groups. MOEAWOD was subjected to testing on 18 benchmark large-scale optimization problems, and the experimental results demonstrate the effectiveness of our methods. Compared with the other algorithms, our method is still at an advantage.

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A Novel Constrained Latin Hypercube Sampling for Electric Machine

Li,

Tan,

et al. 2023

2023 IEEE Industrial Electronics and Applications Conference (IEACon)

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Optimal design of electric machine with efficient handling of constraints and surrogate assistance

Cited by 4 publications

References 44 publications

Artificial Intelligence Approach in Aerospace for Error Mitigation

Artificial Intelligence Approach in Aerospace for Error Mitigation

Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

A Novel Constrained Latin Hypercube Sampling for Electric Machine

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