3D Thermal Simulation of a Laser Drilling Process with Meshfree Methods

Afrasiabi, Mohamadreza; Wegener, Konrad

doi:10.3390/jmmp4020058

Cited by 15 publications

(18 citation statements)

References 37 publications

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“…Similarly, work by Romer et al [78] showed the finite difference time domain for modelling laser-produced nanostructures on a range of materials. Afrasiabi et al [79] showed the application of a particle strength exchange model that offers some advantages over more conventional FEM for modelling metal removal in laser drilling, and they later showed a mesh-free method for accurate three-dimensional thermal simulation during laser drilling [80].…”

Section: Introductionmentioning

confidence: 99%

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Lasers that learn: The interface of laser machining and machine learning

Mills

Grant-Jacob

2021

IET Optoelectronics

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Laser machining is a highly flexible non-contact fabrication method used extensively across academia and industry. Whilst simulations based on fundamental understanding offer some insight into the processes, both the highly non-linear interactions between laser light and matter and the variety of materials involved mean that theoretical modelling is not particularly applicable to practical experimentation. However, recent breakthroughs in machine learning have resulted in neural networks that are capable of accurate and rapid modelling of laser machining at a scale, speed, and precision well beyond those of existing theoretical approaches with applications including 3-D surface visualisation and real-time error correction. A perspective at the intersection of laser machining and machine learning is presented, followed by a discussion of future milestones and challenges for this field.10 steps each, that corresponds to a total of 10^5 experimental trials. Because of the often highly non-linear relationships amongst parameters, the standard practice is generally the systematic collection of laser-machining data for all parameter combinations to identify the optimal combination. However, this process is both time-consuming and unfocussed and can take days or weeks, hence costing unnecessary effort, time, and money. Even when the optimal parameters have been determined, small changes during manufacturing, for example in laser power or beam shape, can result in final product quality that is below the required standard, again with associated costs in time and money. What is needed, therefore, is a set of modelling methodologies for identifying optimal parameters and providing real-time monitoring and error correction during manufacturing.However, the processes that describe laser machining, such as the light-matter interaction, heat conduction, phase change of material, and material removal, are particularly complex and hence challenging to model precisely and directly from a theoretical standpoint. The exact details of these processes depend on many factors, including laser parameters (e.g. wavelength, fluence, and pulse length), associated diffraction effects, and sample properties (e.g. absorption, reflectivity, melting temperature, and ablation threshold). In addition, different interaction effects become dominant as the temporal interaction length varies from continuous wave to ultrafast (i.e. femtosecondThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Introductionmentioning

confidence: 99%

“…Afrasiabi et al. [79] showed the application of a particle strength exchange model that offers some advantages over more conventional FEM for modelling metal removal in laser drilling, and they later showed a mesh‐free method for accurate three‐dimensional thermal simulation during laser drilling [80].…”

Section: Introductionmentioning

confidence: 99%

Lasers that learn: The interface of laser machining and machine learning

Mills

Grant-Jacob

2021

IET Optoelectronics

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“…While relatively new in AM, the notion of SPH in modeling laser-based manufacturing processes is not entirely unique. Examples contain the use of SPH for modeling heat transfer in the laser ablation of aluminum (Alshaer et al , 2017) and an efficient thermal simulation of the laser drilling process with mesh-free schemes (Afrasiabi et al , 2018a, Afrasiabi and Wegener, 2020).…”

Section: Simulation Of Multimaterials Powder Bed Fusion Techniquesmentioning

confidence: 99%

Multimaterial powder bed fusion techniques

Mehrpouya

Tuma

Vaneker

et al. 2022

RPJ

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Purpose This study aims to provide a comprehensive overview of the current state of the art in powder bed fusion (PBF) techniques for additive manufacturing of multiple materials. It reviews the emerging technologies in PBF multimaterial printing and summarizes the latest simulation approaches for modeling them. The topic of “multimaterial PBF techniques” is still very new, undeveloped, and of interest to academia and industry on many levels. Design/methodology/approach This is a review paper. The study approach was to carefully search for and investigate notable works and peer-reviewed publications concerning multimaterial three-dimensional printing using PBF techniques. The current methodologies, as well as their advantages and disadvantages, are cross-compared through a systematic review. Findings The results show that the development of multimaterial PBF techniques is still in its infancy as many fundamental “research” questions have yet to be addressed before production. Experimentation has many limitations and is costly; therefore, modeling and simulation can be very helpful and is, of course, possible; however, it is heavily dependent on the material data and computational power, so it needs further development in future studies. Originality/value This work investigates the multimaterial PBF techniques and discusses the novel printing methods with practical examples. Our literature survey revealed that the number of accounts on the predictive modeling of stresses and optimizing laser scan strategies in multimaterial PBF is low with a (very) limited range of applications. To facilitate future developments in this direction, the key information of the simulation efforts and the state-of-the-art computational models of multimaterial PBF are provided.

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“…The utilization of SPH in more industrially-oriented fields of application can also be found in the literature. Examples in laser-based manufacturing problems are the modeling of material removal in [8,9] and pulsed-laser ablation of aluminum in [10].…”

Section: Introductionmentioning

confidence: 99%

Smoothed Particle Hydrodynamics Simulation of Orthogonal Cutting with Enhanced Thermal Modeling

et al. 2021

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Smoothed Particle Hydrodynamics (SPH) is a mesh-free numerical method that can simulate metal cutting problems efficiently. The thermal modeling of such processes with SPH, nevertheless, is not straightforward. The difficulty is rooted in the computationally demanding procedures regarding convergence properties and boundary treatments, both known as SPH Grand Challenges. This paper, therefore, intends to rectify these issues in SPH cutting models by proposing two improvements: (1) Implementing a higher-order Laplacian formulation to solve the heat equation more accurately. (2) Introducing a more realistic thermal boundary condition using a robust surface detection algorithm. We employ the proposed framework to simulate an orthogonal cutting process and validate the numerical results against the available experimental measurements.

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3D Thermal Simulation of a Laser Drilling Process with Meshfree Methods

Cited by 15 publications

References 37 publications

Lasers that learn: The interface of laser machining and machine learning

Lasers that learn: The interface of laser machining and machine learning

Multimaterial powder bed fusion techniques

Smoothed Particle Hydrodynamics Simulation of Orthogonal Cutting with Enhanced Thermal Modeling

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