Improving precision in the prediction of laser texturing and surface interference of 316L assessed by neural network and adaptive neuro-fuzzy inference models

Sohrabpoor, Hamed; Mousavian, R. Taherzadeh; Obeidi, Muhannad Ahmed; Ahad, Inam Ul; Brabazon, Dermot

doi:10.1007/s00170-019-04291-z

Cited by 13 publications

(3 citation statements)

References 18 publications

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“…As shown in Figure 9, the input parameters of peak power, pulse width, pulse frequency, number of pulses, assist gas pressure and focal plane position were optimised to predict the hole entrance diameter, circularity of hole entrance and hole exit, and hole taper. Sohrabpoor et al [146] applied an ANN and adaptive inference model to predict the surface quality of laser-processed 316L stainless steel cylindrical pins when machined with a CO 2 laser. Velli et al [147] showed that the laser-induced periodic surface structures [148][149][150] produced via a Yb:KBW femtosecond source on stainless steel (and titanium alloy and crystalline silicon) could be predicted via a range of machine-learning models.…”

Section: Steelmentioning

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: Steelmentioning

confidence: 99%

“…Sohrabpoor et al. [146] applied an ANN and adaptive inference model to predict the surface quality of laser‐processed 316L stainless steel cylindrical pins when machined with a CO 2 laser. Velli et al.…”

Section: Machine Learning and Laser Machiningmentioning

confidence: 99%

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

Mills

Grant-Jacob

2021

IET Optoelectronics

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“…In addition, due to the use of Si 3 N 4 in sensitive locations, such as turbine blades, high surface quality is required. To solve the machining problem on ceramic parts, some researchers presented laser-machining method [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Investigation on rotary ultrasonic assisted end grinding of silicon nitride ceramics

Baraheni

Amini

2019

SN Appl. Sci.

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In this study, rotary ultrasonic assisted end grinding (RUAEG) of silicon nitride (Si 3 N 4) was discussed. In addition, comparison of RUAEG with conventional end grinding was carried out. Machining parameters effect on the process was experimentally and statistically distinguished. Moreover, interaction of the influential parameters was discussed and the optimum condition was obtained. It is concluded from interaction plots that ultrasonic vibration influence on surface roughness was greater in lower cutting depths and higher rotational speeds. Moreover, by RUAEG process, it could acquire higher material removal speed with lower thrust force. Exerting ultrasonic vibration on the tool was more influential comparing to other grinding parameters. From morphological examinations, that was concluded RUAEG of ceramics results in lower amount of scratches, diminishing grinding lines and smoothing the surface due to be knocked continuously by the tool.

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