Surface Morphology Analysis and Roughness Prediction of 316L Stainless Steel by Selective Laser Melting

Weihao Mu; Xuehui Chen; Yu Zhang; Lei Huang; Darong Zhu; Bichun Dong

doi:10.3788/LOP202259.0714011

Laser & Optoelectronics Progress, Volume. 59, Issue 7, 0714011(2022)

Surface Morphology Analysis and Roughness Prediction of 316L Stainless Steel by Selective Laser Melting

Weihao Mu, Xuehui Chen^*, Yu Zhang, Lei Huang, Darong Zhu, and Bichun Dong

Author Affiliations

School of Mechanical and Electrical Engineering, Anhui Jianzhu University, Hefei , Anhui 230601, China

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Figures & Tables(14)

Fig. 1. Surface topography of 316L stainless steel powders

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Fig. 2. Forming strategy of the SLM

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Fig. 3. OM image of samples surface fabricated by SLM. (a) X-Y direction; (b) Y-Z direction

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Fig. 4. SEM image of 316L stainless steel microstructure fabricated by SLM

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Fig. 5. SEM images of the surface of SLM-formed samples under different LED. (a) 270 J/m; (b) 240 J/m; (c) 210 J/m; (d) 180 J/m; (e) 150 J/m

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Fig. 6. SEM images of the surface of SLM-formed samples under different powers. (a) 140 W; (b) 160 W;

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Fig. 7. Relationship between LED and surface roughness of samples under different laser power

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Fig. 8. Structure of the BP neural network

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Fig. 9. Flow chart of the GA-BP neural network

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Fig. 10. Prediction error of GA-BP and BP neural networks

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Table 1. Chemical components of 316L stainless steel powder
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Table 1. Chemical components of 316L stainless steel powder
Component Fe Cr Ni Mo Mn Si P C S O N
Mass fraction bal 17.94 11.92 2.46 0.051 0.56 <0.01 0.0094 0.02 0.015 0.0086

Table 2. Process parameters of the SLM

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Table 2. Process parameters of the SLM

Parameter	Value
LED /（J·m^-1）	150/180/210/240/270
Laser power P /W	140/160/180/200/220
Scanning speed v /（mm·s^-1）	500‒1500
thickness of powder layer d /mm	0.3
Spot diameters D /µm	35
Scanning strategy	adjacent layers rotate 67°

Table 3. Surface roughness of samples with different laser power and LED

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Table 3. Surface roughness of samples with different laser power and LED

LED /（J·m^-1）	Laser power /W
LED /（J·m^-1）	140	160	180	200	220
150	17.657	17.602	17.965	15.710	24.198
180	14.083	16.058	16.253	19.389	15.877
210	13.347	14.840	14.628	19.000	17.764
240	16.487	16.897	18.306	16.923	20.066
270	16.394	24.686	21.497	24.200	29.270

Table 4. Experimental and predicted results of surface roughness

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Table 4. Experimental and predicted results of surface roughness

P /W	v/（m·s^-1）	Experimental value /μm	BP neural network		GA-BP neural network
P /W	v/（m·s^-1）	Experimental value /μm	Predicted value /μm	MAPE /%	Predicted value /μm	MAPE /%
140	0.52	16.394	25.5811	56.0	17.8396	8.8
160	0.67	16.897	25.5622	51.3	17.2842	2.3
180	0.86	14.628	25.3940	73.6	16.3784	12.0
200	1.11	19.389	20.6881	6.7	17.7661	8.4
220	1.47	24.198	7.5586	68.8	24.2610	0.3

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Weihao Mu, Xuehui Chen, Yu Zhang, Lei Huang, Darong Zhu, Bichun Dong. Surface Morphology Analysis and Roughness Prediction of 316L Stainless Steel by Selective Laser Melting[J]. Laser & Optoelectronics Progress, 2022, 59(7): 0714011

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