A study on the machining characteristics of specimens with spherical shape using laser-assisted machining

Kim, In-Woo; Lee, Choon-Man

doi:10.1016/j.applthermaleng.2016.02.005

Cited by 34 publications

(7 citation statements)

References 30 publications

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“…Singh et al [87] presented the FEM for optimisation of laser-assisted machining of steel via modelling temperature distribution, flow stress reduction due to laser heating, and cutting forces. Kim et al [88] used the FEM for laser-assisted machining of 3-D shapes for the optimisation of preheating temperature. Finally, Shi et al [89] used the FEM for the optimisation of laser-assisted machining of nickel chromium alloy.…”

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%

“…Kim et al. [88] used the FEM for laser‐assisted machining of 3‐D shapes for the optimisation of preheating temperature. Finally, Shi et al.…”

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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“…Ravindra et al [16] studied high-pressure phase transformation and ductile material removal with silicon nitride using micro-laser assisted machining. Kim and present author [17] studied spherical shape. Laser assisted machining has been studied by many researchers on flat workpiece or micro end-milling.…”

Section: Introductionmentioning

confidence: 90%

A Study on the Machining Characteristics of Curved Workpiece using Laser Assisted Milling with Different Tool Paths in Inconel 718

Kim¹,

Lee²

2018

Preprint

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Difficult-to-cut materials are being increasingly used in many industries because of their superior properties, including high corrosion resistance, heat resistance and specific strength. However, these same properties make the materials difficult to machine using conventional machining techniques. Laser-assisted milling (LAM) is one of the effective methods for machining difficult-to-cut materials. In laser-assisted milling machining occur after the workpiece is locally preheated using a laser heat source. Laser assisted machining has been studied by many researchers on flat workpiece or micro end-milling. However, there is no research on the curved shape using laser assisted milling. This study investigated the use of laser assisted milling to machine a three-dimensional curved shape workpiece based on NURBS. A machining experiment was performed on Inconel 718 using different tool paths (ramping, contouring) under various machining conditions. Finite elements analysis was conducted to determine the depth of cut. Cutting force, specific cutting energy and surface roughness characteristics were measured, analyzed and compared for conventional and LAM machining. LAM significantly improved these machining characteristics, compared to conventional machining. There results can be applied to the laser-assisted machining of various three-dimensional shapes.

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“…The cutting parameters for the induction assisted milling operation are spindle speed (S), feed rate (F) and depth of cut (D). The spindle speed range (6000, 8000 and 10,000 rev/min) and the feed rate range (100, 150 and 200 mm/min) were selected after considering previous studies [9,32]. The depth of the cut range (0.15, 0.2 and 0.25 mm) with sufficient preheating effect was selected based on the finite element analysis result.…”

Section: Experimental Set-upmentioning

confidence: 99%

A Study on the Optimal Machining Parameters of the Induction Assisted Milling with Inconel 718

Kim

Lee

2019

Materials

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This paper focuses on an analysis of tool wear and optimum machining parameter in the induction assisted milling of Inconel 718 using high heat coated carbide and uncoated carbide tools. Thermally assisted machining is an effective machining method for difficult-to-cut materials such as nickel-based superalloy, titanium alloy, etc. Thermally assisted machining is a method of softening the workpiece by preheating using a heat source, such as a laser, plasma or induction heating. Induction assisted milling is a type of thermally assisted machining; induction preheating uses eddy-currents and magnetic force. Induction assisted milling has the advantages of being eco-friendly and economical. Additionally, the preheating temperature can be easily controlled. In this study, the Taguchi method is used to obtain the major parameters for the analysis of cutting force, surface roughness and tool wear of coated and uncoated tools under various machining conditions. Before machining experiments, a finite element analysis is performed to select the effective depth of the cut. The S/N ratio and ANOVA of the cutting force, surface roughness and tool wear are analyzed, and the response optimization method is used to suggest the optimal machining parameters.

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A study on the machining characteristics of specimens with spherical shape using laser-assisted machining

Cited by 34 publications

References 30 publications

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

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

A Study on the Machining Characteristics of Curved Workpiece using Laser Assisted Milling with Different Tool Paths in Inconel 718

A Study on the Optimal Machining Parameters of the Induction Assisted Milling with Inconel 718

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