A novel layer-based optimization method for stabilizing the early printing stage of LPBF process

Figures & data

Figure 1. .#Workflow of the proposed method.

Figure 2. (a, b) The FE model of the multi-layer LPBF process and (c) laser scanning pattern during.

Figure 3. BP neural network model.

Table 1. Finite element simulation process parameters.

Parameter	Value
Powder layer thickness, D (μm)	30
Laser spot radius, r (μm)	50
Ambient temperature, T0 (°C)	25
Laser power, P (W)	95, 110, 125, 140, 155, 170
Scan speed, V (mm/s)	400, 500, 600, 700, 800, 900, 1000, 1100, 1200
Hatch spacing, a (μm)	60

Table 2. Material parameters of solid Invar.

Parameter	Value
Density, ρ (kg·m^−3)	8100
Liquidus temperature, TL (°C)	1538
Solidus temperature, TS (°C)	1430
Latent heat of fusion, L (J·kg^−1)	2.74 × 10^−5

Table 3. Thermal property parameters of solid Invar.

Temperature T (°C)	Thermal conductivity K (W·m^−1·°C^−1)	Specific heat capacity c (J·kg^−1·°C^−1)
20	10.4	531.1
100	10.5	554.6
300	12.2	591.6
500	14.2	623.7
700	15	683.4
1200	18.6	805.8
1500	69.8	909.2
3000	70.41	910.4

Figure 4. (a) Invar alloy LPBF single pass scanning forming sample diagram. (b) The 16th group of samples melt track morphology.

Table 4. Laser processing parameters for single tracks simulation and printing.

No	1	2	3	4	5	6	7	8	9	10
P (W)	95	110	125	140	155	170	140	140	140	140
V (mm/s)	800	800	800	800	800	800	400	600	1000	1200

Figure 5. Numerical simulation and experimental values of the width of the melt track of stabilized working layer during LPBF of Invar alloy power process using different laser power (V = 800 mm/s) (a) and scanning speed (P = 140 W) (b).

Figure 6. The loss curve of neural network.

Figure 7. Sample value and BP neural network output value.

Figure 8. Test value and BP neural network output value.

Table 5. Layer-by-layer optimization results.

	GA output		BP neural network output		Numerical simulation results
Layer index n	Laser power P (W)	Scanning speed V (mm/s)	Width W (μm)	Depth H (μm)	Width W (μm)	Depth H (μm)	Energy density E (J/mm^2)
1	148	905	98.77	47.25	99.54	46.73	3.21
2	123	842	94.44	40.46	97.27	41.41	2.86
3	154	1098	99.48	43.6	97.26	42.45	2.75
4	120	889	95.24	42.88	95.6	42.46	2.65
5	148	1063	97.96	43.65	98.75	44.83	2.73
6	122	916	96.01	44.28	96.86	44.08	2.61
7	113	858	96.36	45.94	97.5	45.33	2.58
8	125	1041	93.75	45.06	89.31	44.32	2.35
9	120	957	96.66	45.96	92.66	45	2.46
10	109	904	95.04	46.51	90	44	2.36
11–300	109	904	95.04	46.51	–	–	2.36

Figure 9. Comparison between layer-by-layer optimized process parameters and mono process parameters.

Figure 10. The boxes with different heights used in the printing experiment.

Table 6. LPBF cubic fabricating process parameters.

Part	Laser power P (W)	Scanning speed V (mm/s)	Energy input E (J/mm^2)
1	170	600	5.55
2	155	700	4.34
3	BP-GA algorithm prediction
4	125	600	4.08
5	95	700	2.66

Figure 11. The relationship between surface roughness and input energy density and the number of processing layers.

Figure 12. The typical topography and three-dimensional scan image of the upper surface of the cube sample made with the process parameters in Table 6 observed with an ultra-depth-of-field microscope.

Data availability statement

The data that support the findings of this study are available from the corresponding author [Y. Zhang], upon reasonable request.