Factor analysis of selective laser melting process parameters with normalised quantities and Taguchi method

Jiang, Hua-Zhen; Li, Zhengyang; Tao, Feng; Wu, Pengyue; Chen, Qi-Sheng; Feng, Yunli; Li, Shiwen; Gao, Huan; Xu, Huawei

doi:10.1016/j.optlastec.2019.105592

Cited by 42 publications

(38 citation statements)

References 28 publications

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“…For optimizing the process parameters, the Taguchi methods with orthogonal array design and dimensional analysis would be valuable [12][13][14]. With the normalized processing map and dimensionless parameters developed by Thomas [15], it is concluded that laser power (corresponding to normalized power q*) is the most significant factor affecting all the response variables, while the normalized equivalent energy density (E 0 *) is also a key processing index for controlling the properties of built parts [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Effect of Process Parameters on Defects, Melt Pool Shape, Microstructure, and Tensile Behavior of 316L Stainless Steel Produced by Selective Laser Melting

Jiang

Tao³

et al. 2020

Acta Metall. Sin. (Engl. Lett.)

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Previous studies have revealed that laser power and energy density are significant factors affecting the quality of parts manufactured by selective laser melting (SLM). The normalized equivalent density E 0 * and dimensionless laser power q*, which can be regarded as a progress on the understanding of the corresponding dimensional quantities, are adopted in this study to examine the defects, melt pool shape, and primary dendrite spacing of the SLM-manufactured 316L stainless steel, because it reflects the combined effect of process parameters and material features. It is found that the number of large defects decreases with increasing E 0 * due to enough heat input during the SLM process, but it will show an increasing trend when excessive heat input (i.e., utilizing a high E 0 *) is imported into the powder bed. The q* plays an important role in controlling maximum temperature rising in the SLM process, and in turn, it affects the number of large defects. A large q* value results in a low value of absolute frequency of large defects, whereas a maximum value of absolute frequency of large defects is achieved at a low q* even if E 0 * is very high. The density of the built parts is greater at a higher q* when E 0 * remains constant. Increasing the melt pool depth at relatively low value of E 0 * enhances the relative density of the parts. A narrow, deep melt pool can be easily generated at a high q* when E 0 * is sufficiently high, but it may increase melt pool instability and cause keyhole defects. It is revealed that a low E 0 * can lead to a high cooling rate, which results in a refined primary dendrite spacing. Relatively low E 0 * is emphasized in selecting the process parameters for the tensile test sample fabrication. It shows that excellent tensile properties, namely ultimate tensile strength, yield strength, and elongation to failure of 773 MPa, 584 MPa, and 46%, respectively, can be achieved at a relatively low E 0 * without heat treatment.

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Section: Introductionmentioning

confidence: 99%

Effect of Process Parameters on Defects, Melt Pool Shape, Microstructure, and Tensile Behavior of 316L Stainless Steel Produced by Selective Laser Melting

Jiang

Tao³

et al. 2020

Acta Metall. Sin. (Engl. Lett.)

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“…Laser power, scanning speed, hatching distance, layer thickness, and scanning pattern are important process parameters in a laser-based AM process that can be adjusted to optimize the manufactured parts’ properties [ 8 , 9 , 10 , 11 ]. Jang et al reported that laser power is the most influential parameter on surface roughness, hardness, and density [ 12 ]. However, Calignano et al [ 13 ] reported that scanning speed has the highest impact on the surface roughness of the fabricated components.…”

Section: Introductionmentioning

confidence: 99%

“…The Taguchi method has been applied to optimize the AM process for various materials including Ti, Inconel, SS316L, AlSi10Mg, etc. [ 12 , 13 , 14 , 15 , 16 , 17 , 18 ]. Similarly, the RSM method has been used to optimize the major process parameters i.e., laser power, scanning speed, scanning pattern, etc.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of Laser Additive Manufacturing of High Layer Thickness Pure Ti and Inconel 718 Alloy Materials Using Finite Element Method

et al. 2021

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Investigation of the selective laser melting (SLM) process, using finite element method, to understand the influences of laser power and scanning speed on the heat flow and melt-pool dimensions is a challenging task. Most of the existing studies are focused on the study of thin layer thickness and comparative study of same materials under different manufacturing conditions. The present work is focused on comparative analysis of thermal cycles and complex melt-pool behavior of a high layer thickness multi-layer laser additive manufacturing (LAM) of pure Titanium (Ti) and Inconel 718. A transient 3D finite-element model is developed to perform a quantitative comparative study on two materials to examine the temperature distribution and disparities in melt-pool behaviours under similar processing conditions. It is observed that the layers are properly melted and sintered for the considered process parameters. The temperature and melt-pool increases as laser power move in the same layer and when new layers are added. The same is observed when the laser power increases, and opposite is observed for increasing scanning speed while keeping other parameters constant. It is also found that Inconel 718 alloy has a higher maximum temperature than Ti material for the same process parameter and hence higher melt-pool dimensions.

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“…Donstov et al [12] found the application of this method useful in improving the physical, mechanical, and tribological properties of FDM-manufactured metal-polymer composite samples in terms of uniformity of powder mixing as well as the structural uniformity of the produced samples. The Taguchi method has also been used for the L-PBF process optimization for various materials, including SS316 L [13,14], AlSi10 Mg [15,16], CoCrMo [17], Inconel [18], and Titanium alloys [17,19]. Joguet et al [17] used this method to evaluate the effects of four L-PBF processing parameters, including laser spot size, hatch spacing, exposure time, and laser focal point distance, on porosity content of CoCrMo and Ti40 parts.…”

Section: Introductionmentioning

confidence: 99%

“…Sathish et al [18] employed the Taguchi method to examine the influence of build orientation and heat treatment on the coefficient of friction in Inconel 718 samples fabricated by L-PBF. Jiang et al [13] examined the effects of three factors-laser power, scanning speed, and hatch spacing-at three levels on three properties of L-PBF parts: top surface roughness, hardness, and density. They reported laser power as the most important parameter affecting all the examined properties.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Optimization Approaches of Laser-Based Powder-Bed Fusion Process for Ti-6Al-4V Alloy

2020

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Laser-based powder-bed fusion (L-PBF) is a widely used additive manufacturing technology that contains several variables (processing parameters), which makes it challenging to correlate them with the desired properties (responses) when optimizing the responses. In this study, the influence of the five most influential L-PBF processing parameters of Ti-6Al-4V alloy—laser power, scanning speed, hatch spacing, layer thickness, and stripe width—on the relative density, microhardness, and various line and surface roughness parameters for the top, upskin, and downskin surfaces are thoroughly investigated. Two design of experiment (DoE) methods, including Taguchi L25 orthogonal arrays and fractional factorial DoE for the response surface method (RSM), are employed to account for the five L-PBF processing parameters at five levels each. The significance and contribution of the individual processing parameters on each response are analyzed using the Taguchi method. Then, the simultaneous contribution of two processing parameters on various responses is presented using RSM quadratic modeling. A multi-objective RSM model is developed to optimize the L-PBF processing parameters considering all the responses with equal weights. Furthermore, an artificial neural network (ANN) model is designed and trained based on the samples used for the Taguchi method and validated based on the samples used for the RSM. The Taguchi, RSM, and ANN models are used to predict the responses of unseen data. The results show that with the same amount of available experimental data, the proposed ANN model can most accurately predict the response of various properties of L-PBF components.

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Factor analysis of selective laser melting process parameters with normalised quantities and Taguchi method

Cited by 42 publications

References 28 publications

Effect of Process Parameters on Defects, Melt Pool Shape, Microstructure, and Tensile Behavior of 316L Stainless Steel Produced by Selective Laser Melting

Effect of Process Parameters on Defects, Melt Pool Shape, Microstructure, and Tensile Behavior of 316L Stainless Steel Produced by Selective Laser Melting

A Comparative Analysis of Laser Additive Manufacturing of High Layer Thickness Pure Ti and Inconel 718 Alloy Materials Using Finite Element Method

Modeling and Optimization Approaches of Laser-Based Powder-Bed Fusion Process for Ti-6Al-4V Alloy

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