Machine Learning‐Evolutionary Algorithm Enabled Design for 4D‐Printed Active Composite Structures

As an emerging field, Automated Machine Learning (AutoML) aims to reduce or eliminate manual operations that require expertise in machine learning. In this paper, a graph-based architecture is employed to represent flexible combinations of ML models, which provides a large searching space compared to tree-based and stacking-based architectures. Based on this, an evolutionary algorithm is proposed to search for the best architecture, where the mutation and heredity operators are the key for architecture evolution. With Bayesian hyper-parameter optimization, the proposed approach can automate the workflow of machine learning. On the PMLB dataset, the proposed approach shows the state-of-the-art performance compared with TPOT, Autostacker, and auto-sklearn. Some of the optimized models are with complex structures which are difficult to obtain in manual design.

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Structural Damage Diagnosis and Prediction Using Machine Learning and Deep Learning Models: Comprehensive Review of Advances

10.20944/preprints201912.0149.v1 ◽

2019 ◽

Author(s):

Amir Mosavi

Keyword(s):

Machine Learning ◽

Composite Structures ◽

Structural Damage ◽

Structural Integrity ◽

State Of The Art ◽

Future Trends ◽

Learning Models ◽

Damage Diagnosis ◽

Comprehensive Review ◽

Initiation And Propagation

The loss of integrity and adverse effect on mechanical properties can be concluded as attributing miro/macro-mechanics damage in structures, especially in composite structures. Damage as a progressive degradation of material continuity in engineering predictions for any aspects of initiation and propagation requires to be identified by a trustworthy mechanism to guarantee the safety of structures. Besides the materials design, structural integrity and health are usually prone to be monitored clearly. One of the most powerful methods for the detection of damage is machine learning (ML). This paper presents the state of the art of ML methods and their applications in structural damage and prediction. Popular ML methods are identified and the performance and future trends are discussed.

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Comparison of synchronous and asynchronous parallelization of extreme surrogate-assisted multi-objective evolutionary algorithm

Natural Computing ◽

10.1007/s11047-020-09806-2 ◽

2020 ◽

Author(s):

Tomohiro Harada ◽

Misaki Kaidan ◽

Ruck Thawonmas

Keyword(s):

Machine Learning ◽

Evolutionary Algorithm ◽

Optimization Problems ◽

Computing Time ◽

The Other ◽

Multi Objective Optimization ◽

Multi Objective ◽

Evaluation Time ◽

Surrogate Function

Abstract This paper investigates the integration of a surrogate-assisted multi-objective evolutionary algorithm (MOEA) and a parallel computation scheme to reduce the computing time until obtaining the optimal solutions in evolutionary algorithms (EAs). A surrogate-assisted MOEA solves multi-objective optimization problems while estimating the evaluation of solutions with a surrogate function. A surrogate function is produced by a machine learning model. This paper uses an extreme learning surrogate-assisted MOEA/D (ELMOEA/D), which utilizes one of the well-known MOEA algorithms, MOEA/D, and a machine learning technique, extreme learning machine (ELM). A parallelization of MOEA, on the other hand, evaluates solutions in parallel on multiple computing nodes to accelerate the optimization process. We consider a synchronous and an asynchronous parallel MOEA as a master-slave parallelization scheme for ELMOEA/D. We carry out an experiment with multi-objective optimization problems to compare the synchronous parallel ELMOEA/D with the asynchronous parallel ELMOEA/D. In the experiment, we simulate two settings of the evaluation time of solutions. One determines the evaluation time of solutions by the normal distribution with different variances. On the other hand, another evaluation time correlates to the objective function value. We compare the quality of solutions obtained by the parallel ELMOEA/D variants within a particular computing time. The experimental results show that the parallelization of ELMOEA/D significantly reduces the computational time. In addition, the integration of ELMOEA/D with the asynchronous parallelization scheme obtains higher quality of solutions quicker than the synchronous parallel ELMOEA/D.

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Early Performance Prediction for CS1 Course Students using a Combination of Machine Learning and an Evolutionary Algorithm

2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT) ◽

10.1109/icalt.2019.00066 ◽

2019 ◽

Cited By ~ 5

Author(s):

Filipe Dwan Pereira ◽

Elaine H. T. Oliveira ◽

David Fernandes ◽

Alexandra Cristea

Keyword(s):

Machine Learning ◽

Evolutionary Algorithm ◽

Performance Prediction ◽

Early Performance

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Linear Two-Port Model of an Active Thermoplastic Composite Structure

Volume 1: Development and Characterization of Multifunctional Materials; Modeling, Simulation and Control of Adaptive Systems; Integrated System Design and Implementation ◽

10.1115/smasis2013-3310 ◽

2013 ◽

Author(s):

Anja Winkler ◽

Uwe Marschner ◽

Eric Starke ◽

Niels Modler ◽

Wolf-Joachim Fischer ◽

...

Keyword(s):

Composite Structure ◽

Manufacturing Process ◽

Composite Structures ◽

Matrix Material ◽

Manufacturing Technology ◽

Experimental Results ◽

Thermoplastic Composite ◽

Starting Point ◽

Modal Behavior ◽

Active Composite

This paper describes new active composite structures based on thermoplastic matrices which contain material homogeneous embedded piezoceramic modules. Starting point is the development of novel thermoplastic compatible piezoceramic modules, so called TPMs. By the utilization of the same matrix material for the composite structure and for the TPM carrier films, these modules afford an opportunity to become directly embedded into the component during its manufacturing process. In this context, the manufacturing technology of the TPMs and of the active composite structure is presented. Furthermore, selected test samples are investigated concerning their modal behavior. Based on the determined characteristics a linear two-port model is used for the reproduction of the experimental results.

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