Experimental analysis and numerical simulation of the stainless AISI 304 steel friction drilling process

Krasauskas, P.; Kilikevičius, Sigitas; Česnavičius, Ramūnas; Pačenga, D.

doi:10.5755/j01.mech.20.6.8664

Cited by 29 publications

(18 citation statements)

References 6 publications

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“…Similar work was performed by P. Krasauskas et al 21 on the thermal drilling into the AISI 304 steel. P. D. Pantawane and B.…”

Section: Introductionmentioning

confidence: 53%

Optimum bushing length in thermal drilling of galvanized steel using artificial neural network coupled with genetic algorithm

Rajesh¹,

Hynes²,

Kumar³

et al. 2017

Mater. Tehnol.

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Thermal drilling is a novel sheet-metal-hole-making technique that utilizes the heat produced at the interface of the rotating conical tool and workpiece in order to soften the workpiece and pierce a hole into it. In this work, experiments with thermal drilling of galvanized steel were conducted based on the Taguchi L27 orthogonal array. Significant process parameters such as rotational speed, tool angle and workpiece thickness were varied during the experimentation. In thermal drilling, the thermal-drill tool pushes aside a large amount of workpiece material to form a sleeve, which is often referred to as the bushing length. A predictive model for the bushing length was developed using a feed-forward artificial neural network based on experimental data. As the bushing length is closely associated with the tapping process, the influences of the input process parameters play a vital role in fastening galvanized steel with threaded fasteners in diverse engineering applications. The optimization problem was solved by implementing a genetic algorithm under constraint limits to maximize the bushing length. Further, a confirmation test was conducted with the intention to compare the optimum value and its corresponding bushing length predicted by the genetic algorithm. Good agreement was observed between the predicted and the experimental values. Keywords: thermal drilling, artificial neural network, genetic algorithm, galvanized steel, bushing length Termi~no vrtanje je nova, za vrtanje lukenj v plo~evino, uporabljena tehnika, ki izkori{~a toploto, proizvedeno na povr{ini vrte~e se konice orodja na obdelovancu z namenom, da ga zmeh~a in vanj naredi luknjo. V delu so bili izvedeni preizkusi na osnovi metode Taguchi L27 z ortogonalno matriko s termi~nim vrtanjem galvaniziranega jekla. Pomembni parametri postopka, kot so: hitrost vrtenja, kot orodja in debelina obdelovanca, so se med eksperimentiranjem spreminjali. Pri toplotnem vrtanju, vrtanje orodja potisne stran ve~materiala obdelovanca tako, da se tvori navarek (rokav) okoli luknje, ki se pogosto omenja kot dol`ina {ablone. Napovedni model za dol`ino {ablone, je razvit z uporabo umetne nevronske mre`e, ki temelji na znanstvenih podatkih. Ker je dol`ina {ablone precej povezana s procesom izdelave navoja, vplivi teh vhodnih procesnih parametrov igrajo klju~no vlogo pri pritrditvi galvaniziranega jekla z navojem pritrdilnih elementov v razli~nih in`enirskih aplikacijah. Problem optimizacije je bil re{en z implementacijo genetskega algoritma na podlagi omejitev za pove~anje dol`ine {ablone. Ugotovljeno je bilo dobro ujemanje med napovedano in eksperimentalno vrednostjo. Klju~ne besede: termi~no vrtanje, umetna nevronska mre`a, genetski algoritem, galvanizirano jeklo, dol`ina {ablone

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“…Similar work was performed by P. Krasauskas et al 21 on the thermal drilling into the AISI 304 steel. P. D. Pantawane and B.…”

Section: Introductionmentioning

confidence: 53%

Optimum bushing length in thermal drilling of galvanized steel using artificial neural network coupled with genetic algorithm

Rajesh¹,

Hynes²,

Kumar³

et al. 2017

Mater. Tehnol.

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“…In addition, sharp crack edges increase the friction forces in the process. This causes the tool to heat up more intensively on its surface [10]. As we know, overheating of the processing zone is undesirable due to the possibility of structural changes on the surface of the shaped bore.…”

Section: Tool Durabilitymentioning

confidence: 99%

Characteristics of tools used in the friction drilling method

Szwałek

Nadolny

2018

Journal of Mechanical and Energy Engineering

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 (CC BY 4.0) International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Abstract: Friction drilling as a non-chip drilling method requires a special formed tool. This tool can be combined with conventional drilling machines as well as milling machines, also CNC ones. The choice depends only on the factors related to production volumes. Tool must have high durability properties to reach proper efficiency level. This means the number of boreholes that can be formed while maintaining the shape and structure of the surface within the dimensions tolerances limits. High temperature in the process also causes high durability properties. These article presents a review of possible solutions on tools mounting devices and tools functionality used in friction drilling process.

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“…The fitting parameters for 316L, obtained by Olleak, A. et al (2015), was also included in the table below so that a comparison between the material properties of these two different grades of stainless steel could be made. The fitting parameters obtained by Frontán, J. et al (2012) matched those obtained by Krasauskas, P. et al (2015). Thus, 304 Stainless Steel III could be ignored from the analysis since it was already represented by 304 Stainless Steel I.…”

Section: Figure B14 -View Of Force Reaction Probe Settingsmentioning

confidence: 58%

“…In order to evaluate the influence that the fitting parameters from the Johnson-Cook material model had on the stress-strain curve for 304 stainless steel, the fitting parameters obtained by Frontán, J. et al (2012) was compared to that obtained by Mori, L. et al (2007) and Krasauskas, P. et al (2015). These fitting parameters can be found in the table below.…”

Section: Figure B14 -View Of Force Reaction Probe Settingsmentioning

confidence: 99%

“…While Frontán, J. et al (2012), Mori, L. et al (2007) and Krasauskas, P. et al (2015) all claimed to achieve accurate numerical results compared to their respective experimental investigations, it is apparent that there is no universally accepted fitting parameters for a material. For this thesis, the fitting parameters obtained by Frontán, J. et al (2012) will be selected.…”

Section: Figure B14 -View Of Force Reaction Probe Settingsmentioning

confidence: 99%

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Explicit finite element analysis of high speed piston impacts

Yusuf¹

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The University of Queensland operates three large scale free-piston drivers which consists of the T4, X2 and X3 facilities. These facilities are designed for the worst case scenarios based on conservative analytical impact mechanics, in terms of avoiding catastrophic piston impacts. However, these do not accurately predict the mechanical response for complex real geometry in the event of a piston impact.In 2009, there was a high speed impact of a heavy piston that rendered the facility inoperable for two months. Repeated attempts were made to relieve enough stress in the plastically deformed piston to dislodge it from the compression tube. This impact reiterated the potential damage that can be caused by incorrect operation of the free-piston drivers and revealed that there was no structured methodology to estimate the condition of the facility to inform the repair procedure.In this study, the AUTODYN solver was used to investigate three main explicit structural analysis problems including recreating the 2009 T4 high speed piston impact, perfoming a transient stress analysis of the peak pressure loadings acting on X2's lightweight piston using high strain rate material data and analysing the buffer for the X3 facility.The approach to this study involved testing the solver's ability to capture the physical mechanisms involved during a high speed piston impact. An axisymmetric analysis of T4 shot 10509 was later developed where the numerical results were compared to observational comments of the facility made by the technician following the repair job. This approach to using an axisymmetric model was also used to analyse the peak pressure loadings acting on the X2 piston and the results from this were validated against a static structural analysis. An axisymmetric model of the X3 buffer was developed and this was validated against a 3D model. This study has shown that the AUTODYN solver can be used to model high speed piston impacts with relative confidence. Furthermore, there was enough evidence to show that the numerical recreation of T4 shot 10509 did agree with the technician's observations following T4 shot 10509. This allowed for the response of the facility at higher impact speeds and with varying material properties to be investigated. The transient analysis conducted for the X2 lightweight piston agreed closely with the static structural analysis conducted prior. Since the X3 lightweight piston was made out of similar materials to the X2 piston, this validated the material data used for the X3 piston. It was also discovered that the nylon studs in the X3 facility would not likely fracture from an impact speed of 30 m/s by the X3 lightweight piston. This quantification of safe impact speeds for the X3 buffer was deemed important following the development of new driver operating conditons for the X3 facility.ii Acknowledgements Foremost, I would like to thank my supervisor Dr Gildfind for his continued support throughout the year, and faith in my ability to overcome all obstacles that got in my way....

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Experimental analysis and numerical simulation of the stainless AISI 304 steel friction drilling process

Cited by 29 publications

References 6 publications

Optimum bushing length in thermal drilling of galvanized steel using artificial neural network coupled with genetic algorithm

Optimum bushing length in thermal drilling of galvanized steel using artificial neural network coupled with genetic algorithm

Characteristics of tools used in the friction drilling method

Explicit finite element analysis of high speed piston impacts

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