Laser Beam Milling of Alumina Ceramics - The Impact on Material Removal Efficiency and Machined Surface Morphology

Sugar, Peter F.; Frnčík, Martin; Šugárová, Jana; Sahul, Miroslav

doi:10.4028/www.scientific.net/ssp.261.143

Cited by 6 publications

(3 citation statements)

References 16 publications

(4 reference statements)

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“…The use of ceramics with gradient properties has great prospects, when the surface layer differs in its characteristics from the corresponding parameters of the bulk layers. As shown in works [11][12][13][14][15][16][17][18][19][20][21][22][23][24], surface treatment can be effectively carried out using low energy high current pulsed electron beam (LEHCPEB) [11][12][13], laser treatment [14,15] and high-power pulsed ion beam (HPPIB) [16][17][18][19][20][21][22][23][24]. To study thin modified layers of dielectrics, the method of secondary ion mass spectrometry [25][26][27] and the Rutherford backscattering method [28,29] have proved to be very useful.…”

Section: Introductionmentioning

confidence: 85%

Influence of a high-power pulsed ion beam on the mechanical properties of corundum ceramics

Kostenko

Pavlov

Nikolaeva

2018

IOP Conf. Ser.: Mater. Sci. Eng.

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Abstract. The mechanical properties of near-surface layers of corundum ceramics treated by high-power pulsed ion beam of carbon are investigated. The samples for investigation were prepared from corundum substrate, which is usually used in microelectronic. The ion treatment was carried out at the TEMP-4M facility under the following conditions: an accelerating voltage of 160-200 keV, the current density in the pulse varied within 15-85 A/cm 2 . It was found that ion irradiation changes the structure and properties of near-surface layers of corundum ceramics. At the same time, melting and erosion of the surface layer takes place. These processes are accompanied by the formation of a network of microcracks. Microcracks are propagated only by the depth of melting layer. The mechanical properties were measured using a NanoTest600 nanohardness testing instrument. It was found that the nanohardness depends of the treatment modes. At a current density of 15A/cm 2 , with an increase treatment dose, the nanohardness of the irradiated surface layer increases in comparison with the initial value before irradiation. At higher current densities, the nanohardness of irradiated ceramics decreases relatively to the initial value before irradiation. The dependences of nanohardness off the irradiation dose in this case have the view of a curves with a minimum at irradiation doses of 2.5•10 14 and 1.3•10 14 cm -2 , for current densities of 50 and 85 A/cm 2 , respectively.

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Section: Introductionmentioning

confidence: 85%

Influence of a high-power pulsed ion beam on the mechanical properties of corundum ceramics

Kostenko

Pavlov

Nikolaeva

2018

IOP Conf. Ser.: Mater. Sci. Eng.

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“…Laser machining is a processing technique which has attracted attention due to its convenience, efficiency and high precision, as it overcomes many of the problems of subtractive manufacturing [9,10]. In laser processing, the material is exposed to a high-energy beam causing it to ablate rapidly, resulting in superior cut quality and little to no microcrack formation or thermally affected zone [11][12][13][14]. This capability requires extremely precise and reproducible processing parameter control.…”

Section: Introductionmentioning

confidence: 99%

Machine learning-driven process of alumina ceramics laser machining

et al. 2022

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Laser machining is a highly flexible non-contact manufacturing technique that has been employed widely across academia and industry. Due to nonlinear interactions between light and matter, simulation methods are extremely crucial, as they help enhance the machining quality by offering comprehension of the inter-relationships between the laser processing parameters. On the other hand, experimental processing parameter optimization recommends a systematic, and consequently time-consuming, investigation over the available processing parameter space. An intelligent strategy is to employ machine learning (ML) techniques to capture the relationship between picosecond laser machining parameters for finding proper parameter combinations to create the desired cuts on industrial-grade alumina ceramic with deep, smooth and defect-free patterns. Laser parameters such as beam amplitude and frequency, scanner passing speed and the number of passes over the surface, as well as the vertical distance of the scanner from the sample surface, are used for predicting the depth, top width, and bottom width of the engraved channels using ML models. Owing to the complex correlation between laser parameters, it is shown that Neural Networks (NN) are the most efficient in predicting the outputs. Equipped with an ML model that captures the interconnection between laser parameters and the engraved channel dimensions, one can predict the required input parameters to achieve a target channel geometry. This strategy significantly reduces the cost and effort of experimental laser machining during the development phase, without compromising accuracy or performance. The developed techniques can be applied to a wide range of ceramic laser machining processes.

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“…Laser machining is a processing technique, which has attracted attention due to its convenience, efficiency and high precision, as it overcomes many of the problems of subtractive manufacturing [9,10]. In laser processing, the material is exposed to a high energy beam causing it to rapidly ablate [11,12] resulting in a superior cut quality and little to no microcrack formation or thermally affected zone [13][14][15]. This capability requires extremely precise and reproducible processing parameter control.…”

Section: Introductionmentioning

confidence: 99%