Inverse Analysis of Inconel 718 Laser-Assisted Milling to Achieve Machined Surface Roughness

Feng, Yixuan; Hung, Tsung-Pin; Lu, Yu-Ting; Lin, Yu-Fu; Hsu, Fu-Chuan; Lin, Chiu-Feng; Lu, Yingcheng; Lü, Xiaohong; Liang, Steven Y.

doi:10.1007/s12541-018-0188-7

Cited by 16 publications

(8 citation statements)

References 15 publications

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“…The ability to predict surface roughness before machining has attracted great interest from many scientists, being the main goals of many research studies. The prediction of surface roughness is currently determined by using various techniques such as theoretical models [ 3 , 32 , 33 , 34 , 35 ], response surface methodology (RSM) [ 3 , 6 , 9 , 36 , 37 , 38 ], the Taguchi procedure [ 3 , 6 , 28 , 38 , 39 , 40 , 41 , 42 , 43 ], multiple linear regression equations [ 44 ], the Monte Carlo (MC) method [ 7 , 24 , 33 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 ], artificial intelligence through the use of the artificial neural networks (ANNs) [ 1 , 3 , 26 , 29 , 30 , 53 , 54 , 55 , 56 ], genetic algorithms (GAs) [ 3 , 57 ], fuzzy logic (FL) [ 3 , 36 , 54 , 58 , …”

Section: Introductionmentioning

confidence: 99%

Procedure for Determining the Uncertainties in the Modeling of Surface Roughness in the Turning of NiTi Alloys Using the Monte Carlo Method

Kowalczyk

Tomczyk

2020

Materials

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The paper presents a procedure for the determination of uncertainties in the modeling of surface roughness in the turning of NiTi alloys. The presented procedure is applicable both to the analysis of the measurement values of the two main roughness factors, as well as to research related to the prediction and optimization of the machining process. Type A and B, total, and expanded uncertainties were considered herein, and the obtained uncertainty values were assessed. A procedure for optimizing machining by applying the Monte Carlo (MC) method is also presented. The solutions presented in this paper are important from the point of view of practical solutions related to the prediction and optimization of the machining process. The considered procedure for determining and assessing uncertainty can be useful for the optimal selection of both machining parameters and measuring tools.

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Section: Introductionmentioning

confidence: 99%

Procedure for Determining the Uncertainties in the Modeling of Surface Roughness in the Turning of NiTi Alloys Using the Monte Carlo Method

Kowalczyk

Tomczyk

2020

Materials

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“…The complex multi-physics of the SLM process occurs at micro time and length scales. As a result of the complex phenomena involved in this process, relying on experimentation to understand the underlying physical aspects of the SLM process alone would be too time-consuming, costly, and complex [14,15,16]. Numerical models such as finite element models and finite volume models are used by many researchers, however, the simulation of the entire process could not be achieved in a traceable amount of time.…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, many simplifications in modeling should be undertaken [17,18,19,20]. In contrast, analytical models, validated by physical experiments, provide a means of both effectively understanding and optimizing the process by allowing for in-situ analysis as well as efficient optimization of process parameters [15,21,22].…”

Section: Introductionmentioning

confidence: 99%

Heat Source Modeling in Selective Laser Melting

et al. 2019

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Selective laser melting (SLM) is an emerging additive manufacturing (AM) technology for metals. Intricate three-dimensional parts can be generated from the powder bed by selectively melting the desired location of the powders. The process is repeated for each layer until the part is built. The necessary heat is provided by a laser. Temperature magnitude and history during SLM directly determine the molten pool dimensions, thermal stress, residual stress, balling effect, and dimensional accuracy. Laser-matter interaction is a crucial physical phenomenon in the SLM process. In this paper, five different heat source models are introduced to predict the three-dimensional temperature field analytically. These models are known as steady state moving point heat source, transient moving point heat source, semi-elliptical moving heat source, double elliptical moving heat source, and uniform moving heat source. The analytical temperature model for all of the heat source models is solved using three-dimensional differential equations of heat conduction with different approaches. The steady state and transient moving heat source are solved using a separation of variables approach. However, the rest of the models are solved by employing Green’s functions. Due to the high temperature in the presence of the laser, the temperature gradient is usually high which has a substantial impact on thermal material properties. Consequently, the temperature field is predicted by considering the temperature sensitivity thermal material properties. Moreover, due to the repeated heating and cooling, the part usually undergoes several melting and solidification cycles, and this physical phenomenon is considered by modifying the heat capacity using latent heat of melting. Furthermore, the multi-layer aspect of the metal AM process is considered by incorporating the temperature history from the previous layer since the interaction of the layers have an impact on heat transfer mechanisms. The proposed temperature field models based on different heat source approaches are validated using experimental measurement of melt pool geometry from independent experimentations. A detailed explanation of the comparison of models is also provided. Moreover, the effect of process parameters on the balling effect is also discussed.

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“…The complex multi-physics of the SLM process occurs at micro time and length scales. As a result of complex phenomena involved in this process, relying on experimentation to understand the underlying physical aspects of the SLM process alone would be too time consuming, costly, and complex [14][15][16]. Numerical models such as finite element models and finite volume models are used by many researchers, however, the simulation of the entire process could not be achieved in a traceable amount of time.…”

Section: Introductionmentioning

confidence: 99%

“…In contrast, analytical models, validated by physical experiments provide a means of both effectively understanding and optimizing the process by allowing for in-situ analysis as well as efficient optimization of process parameters [15,21,22].…”

Section: Introductionmentioning

confidence: 99%

Heat Source Modeling in Selective Laser Melting

Mirkoohi

Seivers

Garmestani

et al. 2019

Preprint

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Selective laser melting is an emerging Additive Manufacturing (AM) technology for metals. Intricate three-dimensional parts can be generated from the powder bed by selectively melting the desired location of the powders. The process is repeated for each layer until the part is built. The necessary heat is provided by a laser. Temperature magnitude and history during SLM directly determine the molten pool dimensions, thermal stress, residual stress, balling effect, and dimensional accuracy. Laser-matter interaction is a crucial physical phenomenon in the SLM process. In this paper, five different heat source models are introduced to predict the three-dimensional temperature field analytically. These models are known as steady state moving point heat source, transient moving point heat source, semi-elliptical moving heat source, double elliptical moving heat source, and uniform moving heat source. The analytical temperature model for all of the heat source models are solved using three-dimensional differential equation of heat conduction with different approaches. The Steady state and transient moving heat source are solved using separation of variables approach. However, the rest of models are solved by employing the Green’s functions. Due to the high magnitude of the temperature in the presence of the laser, the temperature gradient is usually high which has a substantial impact on thermal material properties. Consequently, the temperature field is predicted by considering the temperature sensitivity thermal material properties. Moreover, due to the repeated heating and cooling, the part usually undergoes several melting and solidification cycles, this physical phenomenon is considered by modifying the heat capacity using latent heat of melting. Furthermore, the multi-layer aspect of metal AM process is considered by incorporating the temperature history from the previous layer since the interaction of the layers have an impact on heat transfer mechanisms. The proposed temperature field models based on different heat source approaches are validated using experimental measurement of melt pool geometry from independent experimentations. The detailed explanation of the comparison of models is also provided. Moreover, the effect of process parameters on the balling effect is also discussed.

show abstract

Inverse Analysis of Inconel 718 Laser-Assisted Milling to Achieve Machined Surface Roughness

Cited by 16 publications

References 15 publications

Procedure for Determining the Uncertainties in the Modeling of Surface Roughness in the Turning of NiTi Alloys Using the Monte Carlo Method

Procedure for Determining the Uncertainties in the Modeling of Surface Roughness in the Turning of NiTi Alloys Using the Monte Carlo Method

Heat Source Modeling in Selective Laser Melting

Heat Source Modeling in Selective Laser Melting

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