Effect of fractal parameters on optical properties of cold rolled aluminum alloy strips with induced surface deflection: Simulations and experimental correlations

Mahmood, Muhammad Arif; Tsai, Tsung‐Yen; Hwu, Yhu Jen; Lin, Wei Jr; Liu, Lee Cheng; Lai, Jiing Yih; Pan, Jui Wen; Li, Wang Long; Lin, Jen Fin

doi:10.1016/j.jmatprotec.2019.116554

Cited by 9 publications

(4 citation statements)

References 12 publications

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“…This model was able to predict results with 8% mean absolute deviations. Various analytical models have been presented to estimate temperature distribution within deposited coating using non-Fourier and two-temperature model techniques [115][116][117][118][119]. These models can be used for process optimization by linking them with optimization software or an artificial neural network before performing actual experiments [120].…”

Section: Modelling Approachesmentioning

confidence: 99%

Laser Coatings via State-of-the-Art Additive Manufacturing: A Review

et al. 2021

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Ceramics and ceramic-reinforced metal matrix composites (CMMCs) demonstrate high wear resistance, excellent chemical inertness, and exceptional properties at elevated temperatures. These characteristics are suitable for their utilization in biomedical, aerospace, electronics, and other high-end engineering industries. The aforementioned performances make them difficult to fabricate via conventional manufacturing methods, requiring high costs and energy consumption. To overcome these issues, laser additive manufacturing (LAM) techniques, with high-power laser beams, were developed and extensively employed for processing ceramics and ceramic-reinforced CMMCs-based coatings. In respect to other LAM processes, laser melting deposition (LMD) excels in several aspects, such as high coating efficiency and lower labor cost. Nevertheless, difficulties such as poor bonding between coating and substrate, cracking, and reduced toughness are still encountered in some LMD coatings. In this article, we review recent developments in the LMD of ceramics and CMMCs-based coatings. Issues and solutions, along with development trends, are discussed and summarized in support of implementing this technology for current industrial use.

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Section: Modelling Approachesmentioning

confidence: 99%

Laser Coatings via State-of-the-Art Additive Manufacturing: A Review

et al. 2021

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“… These heat equations have not been explored in the case of laser–ceramic interaction. Ceramics are porous materials having voids and pores of various dimensions [ 95 ]. Furthermore, ceramics behave as insulators and present complex rheological properties [ 96 ].…”

Section: Future Outlookmentioning

confidence: 99%

“…A series of computations can be carried out based on inputs and corresponding outputs [ 101 ]. These values can be used to train ANN and further used to estimate output based on desired inputs [ 102 ]. Recently, Mahmood et al used the same approach to link ANN with analytical modeling for process automation [ 103 ].…”

Section: Future Outlookmentioning

confidence: 99%

A State-of-the-Art Review on Integral Transform Technique in Laser–Material Interaction: Fourier and Non-Fourier Heat Equations

2021

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Heat equations can estimate the thermal distribution and phase transformation in real-time based on the operating conditions and material properties. Such wonderful features have enabled heat equations in various fields, including laser and electron beam processing. The integral transform technique (ITT) is a powerful general-purpose semi-analytical/numerical method that transforms partial differential equations into a coupled system of ordinary differential equations. Under this category, Fourier and non-Fourier heat equations can be implemented on both equilibrium and non-equilibrium thermo-dynamical processes, including a wide range of processes such as the Two-Temperature Model, ultra-fast laser irradiation, and biological processes. This review article focuses on heat equation models, including Fourier and non-Fourier heat equations. A comparison between Fourier and non-Fourier heat equations and their generalized solutions have been discussed. Various components of heat equations and their implementation in multiple processes have been illustrated. Besides, literature has been collected based on ITT implementation in various materials. Furthermore, a future outlook has been provided for Fourier and non-Fourier heat equations. It was found that the Fourier heat equation is simple to use but involves infinite speed heat propagation in comparison to the non-Fourier heat equation and can be linked with the Two-Temperature Model in a natural way. On the other hand, the non-Fourier heat equation is complex and involves various unknowns compared to the Fourier heat equation. Fourier and Non-Fourier heat equations have proved their reliability in the case of laser–metallic materials, electron beam–biological and –inorganic materials, laser–semiconducting materials, and laser–graphene material interactions. It has been identified that the material properties, electron–phonon relaxation time, and Eigen Values play an essential role in defining the precise results of Fourier and non-Fourier heat equations. In the case of laser–graphene interaction, a restriction has been identified from ITT. When computations are carried out for attosecond pulse durations, the laser wavelength approaches the nucleus-first electron separation distance, resulting in meaningless results.

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“…Further, the part is printed by a 3D printer using computer numerical control (CNC) codes developed from the sliced file. CNC codes define the smooth and jerk-free movements of the deposition head, giving efficient and better-quality results [ 27 , 28 ].…”

Section: Three-dimensional (3d) Printing Processesmentioning

confidence: 99%

Artificial Neural Network Algorithms for 3D Printing

et al. 2020

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Additive manufacturing with an emphasis on 3D printing has recently become popular due to its exceptional advantages over conventional manufacturing processes. However, 3D printing process parameters are challenging to optimize, as they influence the properties and usage time of printed parts. Therefore, it is a complex task to develop a correlation between process parameters and printed parts’ properties via traditional optimization methods. A machine-learning technique was recently validated to carry out intricate pattern identification and develop a deterministic relationship, eliminating the need to develop and solve physical models. In machine learning, artificial neural network (ANN) is the most widely utilized model, owing to its capability to solve large datasets and strong computational supremacy. This study compiles the advancement of ANN in several aspects of 3D printing. Challenges while applying ANN in 3D printing and their potential solutions are indicated. Finally, upcoming trends for the application of ANN in 3D printing are projected.

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Effect of fractal parameters on optical properties of cold rolled aluminum alloy strips with induced surface deflection: Simulations and experimental correlations

Cited by 9 publications

References 12 publications

Laser Coatings via State-of-the-Art Additive Manufacturing: A Review

Laser Coatings via State-of-the-Art Additive Manufacturing: A Review

A State-of-the-Art Review on Integral Transform Technique in Laser–Material Interaction: Fourier and Non-Fourier Heat Equations

Artificial Neural Network Algorithms for 3D Printing

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