A review of Industry 4.0 and additive manufacturing synergy

Khorasani, Mahyar; Loy, Jennifer; Ghasemi, Arman; Sharabian, Elmira; Leary, Martin; Mirafzal, Hamed; Cochrane, Peter; Rolfe, Bernard; Gibson, Ian

doi:10.1108/rpj-08-2021-0194

Cited by 39 publications

(24 citation statements)

References 83 publications

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“…AM is on the way to digitalization and will soon emerge as key technology in the next few decades in the context of Industry 4.0. 7,8 The forecast for the next ve years shows tremendous growth in the AM technology. According to Wohler's Report, the global revenue of all AM products and services grew by 19.5% to $15.2 billion.…”

Section: Introductionmentioning

confidence: 99%

Vat photopolymerization-based 3D printing of polymer nanocomposites: current trends and applications

et al. 2023

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Section: Introductionmentioning

confidence: 99%

Vat photopolymerization-based 3D printing of polymer nanocomposites: current trends and applications

et al. 2023

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“…In addition, machine learning is widely applied in different areas such as industry [27], manufacturing [28,29], composites [30], and so on.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning Based Design of Railway Prestressed Concrete Sleepers

et al. 2022

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The state-of-the-art design methods for railway prestressed concrete sleepers are currently based on the quasi-static stresses resulting from a simplification of dynamic wheel loads, and subsequently the quasi-static responses of concrete sleepers. This method has been widely used in practices to overcome the complexity of dynamic analysis and testing. A single load factor (or called dynamic impact factor) for a partial safety-factored design (or k factors for the test criteria) is commonly used to crudely account for dynamic train–track interactions over different levels of track irregularities. The dynamic impact factors for either design or testing are usually obtained from either (i) railway infrastructure managers (i.e., in EN 13230), or (ii) prescribed standardised factors (i.e., AS 1085.14, AREMA Chapter 30, JSA—JIS E 1201). The existing design concepts for prestressed concrete sleepers using either (i) an allowable stress design or (ii) the limit state design method require many iterations for calculations and optimisations. The design process to achieve optimal products suitable for track, operational, and environmental parameters is, thus, very time-consuming. On this ground, this study investigates the potential capability of machine learning (ML) to learn from large amounts of design data sets and then to facilitate the design and capacity prediction of railway prestressed concrete sleepers. Three ML algorithms are developed, namely deep learning, Bayesian Neural Network, and random forest. Through a combination of hand-calculated design data, industry design data, and experimental investigations in compliance with EN 13230, over 3000 sets of design data have been collected. These data sets are used to assimilate a comprehensive database for machine learning. Four indicators, namely mean squared error (MSE), root-mean-square error (RMSE), mean absolute error (MAE), and R2 are used to benchmark the accuracy and precision of machine learning models. Our results reveal that the random forest algorithm offers the best performance. The values of MSE, RMSE, MAE, and R2 are 0.54, 0.74, 0.25, and 0.99, respectively. Note that the Bayesian neural network also performs very well. In contrast, the deep learning algorithm performs worse than the others. The insight demonstrates machine learning’s capability to aid in the design of railway prestressed concrete sleepers, to satisfy both serviceability and, ultimately, a limit state.

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“…On the other hand, machine; earning (ML), as a sub-field of artificial intelligence, has attracted attention in various industries, such as the medical, mechanical, and manufacturing industries [ 21 , 22 , 23 ]. ML has been greatly applied to the material sciences and civil engineering within the past decade, mainly for the prediction of the mechanical properties of different types of concretes [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Mechanical Properties of Basalt Fiber Reinforced High-Performance Concrete Using Machine Learning Techniques

et al. 2022

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In this research, we present an efficient implementation of machine learning (ML) models that forecast the mechanical properties of basalt fiber-reinforced high-performance concrete (BFHPC). The objective of the present study was to predict compressive, flexural, and tensile strengths of BFHPC through ML techniques and propose some correlations between these properties. Moreover, the modulus of elasticity (ME) values and compressive stress–strain curves were simulated using ML techniques. In this regard, three predictive algorithms, including linear regression (LR), support vector regression (SVR), and polynomial regression (PR), were considered. LR, SVR, and PR were utilized to forecast the compressive, flexural, and tensile strengths of BFHPC, and the PR technique was employed to simulate the compressive stress–strain curves. The performance of the models was also determined by the coefficient of determination (R2), mean absolute errors (MAE), and root mean square errors (RMSE). According to the obtained values of R2, MAE, and RMSE, the performance of PR was better than other types of algorithms in estimating the compressive, tensile, and flexural strengths. For example, R2 values were 0.99, 0.94, and 0.98 in predicting the compressive, flexural, and tensile strengths using PR, respectively. This shows the higher accuracy and reliability of the PR technique compared with other predictive algorithms. Finally, we concluded that ML techniques can be appropriately applied to assess the mechanical characteristics of BFHPC.

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A review of Industry 4.0 and additive manufacturing synergy

Cited by 39 publications

References 83 publications

Vat photopolymerization-based 3D printing of polymer nanocomposites: current trends and applications

Vat photopolymerization-based 3D printing of polymer nanocomposites: current trends and applications

Machine Learning Based Design of Railway Prestressed Concrete Sleepers

Prediction of the Mechanical Properties of Basalt Fiber Reinforced High-Performance Concrete Using Machine Learning Techniques

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