Comparison of machine learning methods and finite element analysis on the fracture behavior of polymer composites

Balcıoğlu, H. Ersen; Seçkin, Ahmet Çağdaş

doi:10.1007/s00419-020-01765-5

Cited by 22 publications

(12 citation statements)

References 51 publications

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“…Fracture mechanics is the area of mechanics that study crack propagation in materials. [40][41][42] The subdomains of fracture mechanics are defined as follows:…”

Section: Machine Learning Applications In Crack Mechanicsmentioning

confidence: 99%

“…There are serious challenges for detecting faults and failure of mechanical systems, parts and machinery. [40][41][42] The major concerns founded in the current studies in the fracture mechanics field are as shown in Figure 4. In some cases, both empirical and analytical cannot handle some complicated engineering difficulties such as complex and nonlinear relationships amongst higher-dimensional data.…”

Section: Machine Learning Applications In Crack Mechanicsmentioning

confidence: 99%

“…Fracture mechanics is the area of mechanics that study crack propagation in materials. 40–42 The subdomains of fracture mechanics are defined as follows: • Crack diagnosis and detection: Crack might be in macro, meso and micro scale and take the lead to the mechanical failure. Mechanical damage is a physical characterisation that defines the existed defect on the structure. • Fracture parameters and mechanical fracture: Different parameters such as fracture energy, fracture toughness, crack propagation and fractography are concerned with mechanical fracture.…”

Section: Machine Learning Applications In Crack Mechanicsmentioning

confidence: 99%

See 2 more Smart Citations

Compatibility and challenges in machine learning approach for structural crack assessment

Omar

Khan

Starr

2022

Structural Health Monitoring

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Structural health monitoring and assessment (SHMA) is exceptionally essential for preserving and sustaining any mechanical structure’s service life. A successful assessment should provide reliable and resolute information to maintain the continuous performance of the structure. This information can effectively determine crack progression and its overall impact on the structural operation. However, the available sensing techniques and methods for performing SHMA generate raw measurements that require significant data processing before making any valuable predictions. Machine learning (ML) algorithms (supervised and unsupervised learning) have been extensively used for such data processing. These algorithms extract damage-sensitive features from the raw data to identify structural conditions and performance. As per the available published literature, the extraction of these features has been quite random and used by academic researchers without a suitability justification. In this paper, a comprehensive literature review is performed to emphasise the influence of damage-sensitive features on ML algorithms. The selection and suitability of these features are critically reviewed while processing raw data obtained from different materials (metals, composites and polymers). It has been found that an accurate crack prediction is only possible if the selection of damage-sensitive features and ML algorithms is performed based on available raw data and structure material type. This paper also highlights the current challenges and limitations during the mentioned sections.

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“…Fracture mechanics is the area of mechanics that study crack propagation in materials. [40][41][42] The subdomains of fracture mechanics are defined as follows:…”

Section: Machine Learning Applications In Crack Mechanicsmentioning

confidence: 99%

Section: Machine Learning Applications In Crack Mechanicsmentioning

confidence: 99%

Section: Machine Learning Applications In Crack Mechanicsmentioning

confidence: 99%

See 1 more Smart Citation

Compatibility and challenges in machine learning approach for structural crack assessment

Omar

Khan

Starr

2022

Structural Health Monitoring

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“…In addition, the results revealed that the classification ability of used algorithms was excellent for sorting films into conforming and non-conforming parts. Balcioglu et al compared finite element analysis with machine learning algorithms (DT, KNN, RF, SVR) for fracture analysis of polymer composites [ 94 ]. Fracture behavior of laminated composites reinforced with pure carbon, glass and carbon/glass composition were tested and compared with standard samples.…”

Section: Classification Based On Textile Processesmentioning

confidence: 99%

Classification of Textile Polymer Composites: Recent Trends and Challenges

2021

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Polymer based textile composites have gained much attention in recent years and gradually transformed the growth of industries especially automobiles, construction, aerospace and composites. The inclusion of natural polymeric fibres as reinforcement in carbon fibre reinforced composites manufacturing delineates an economic way, enhances their surface, structural and mechanical properties by providing better bonding conditions. Almost all textile-based products are associated with quality, price and consumer’s satisfaction. Therefore, classification of textiles products and fibre reinforced polymer composites is a challenging task. This paper focuses on the classification of various problems in textile processes and fibre reinforced polymer composites by artificial neural networks, genetic algorithm and fuzzy logic. Moreover, their limitations associated with state-of-the-art processes and some relatively new and sequential classification methods are also proposed and discussed in detail in this paper.

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“…The used approach follows predictive modeling technique, which is defined as the process of applying a model or mining algorithm to data in order to predict new or future observations [46]. This definition includes temporal prediction, in which observations up to time t are used to predict future values at time t1 > t. A simple statistical analysis of numerous observations to establish a relationship between the current and future state of the system is most relevant when it is impossible to analytically express such a relationship [47,48].…”

Section: Introductionmentioning

confidence: 99%

Multi-Criteria Decision Approach to Design a Vacuum Infusion Process Layout Providing the Polymeric Composite Part Quality

Shevtsov

Zhilyaev

Chang³

et al. 2022

Polymers

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The increasingly widespread use of vacuum assisted technologies in the manufacture of polymer-composite structures does not always provide the required product quality and repeatability. Deterioration of quality most often appears itself in the form of incomplete filling of the preform with resin as a result of the inner and outer dry spot formation, as well as due to premature gelation of the resin and blockage of the vacuum port. As experience shows, these undesirable phenomena are significantly dependent on the location of the resin and vacuum ports. This article presents a method for making a decision on the rational design of a process layout. It is based on early forecasting of its objectives in terms of quality and reliability when simulating its finite element model, on the correlation analysis of the preliminary and final quality assessments, as well as on the study of the cross-correlation of a group of early calculated sub-criteria. The effectiveness of the proposed method is demonstrated by the example of vacuum infusion of a 3D thin-walled structure of complex geometry.

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Comparison of machine learning methods and finite element analysis on the fracture behavior of polymer composites

Cited by 22 publications

References 51 publications

Compatibility and challenges in machine learning approach for structural crack assessment

Compatibility and challenges in machine learning approach for structural crack assessment

Classification of Textile Polymer Composites: Recent Trends and Challenges

Multi-Criteria Decision Approach to Design a Vacuum Infusion Process Layout Providing the Polymeric Composite Part Quality

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