Intra-layer and inter-layer monitoring of laser powder bed fusion defects based on airborne acoustic and gⁿ-Res model: pore and deformation

Figures & data

Figure 1. The overview of the article.

Figure 2. LPBF acoustic signal monitoring system.

Figure 3. Characterisation of parts and their micromorphology.

Table 1. Part processing parameters.

Class	Scan strategy	Scan power	Scan Speed	Scan line Spacing	Layer Thickness
A	Helical	100 W	800 mm/s	80 μm	40 μm
B	Helical	150 W	800 mm/s	80 μm	40 μm
C	Bidirectional	100 W	800 mm/s	80 μm	40 μm
D	Bidirectional	150 W	800 mm/s	80 μm	40 μm
E	Bidirectional	150 W	150 mm/s	80 μm	40 μm
F	Bidirectional	150 W	100 mm/s	80 μm	40 μm
G	Helical	150 W	150 mm/s	80 μm	40 μm
H	Helical	150 W	100 mm/s	80 μm	40 μm

Figure 4. Microscopic image processing process based on computer vision.

Table 2. Characterisation of pore defect categories and degrees.

Class	NP	PS	APS	APP	Defect definition
A	424	10.650%	1234.80	126.70	Heavily unfused pores (PS:10–15%)
B	116	5.086%	2151.47	166.98	Moderate unfused pores (PS:5–10%)
C	509	15.100%	1457.72	145.99	Severely unfused pores (PS:>15%)
D	21	0.069%	171.11	38.35	Seldom keyhole pores (PS:0–0.5%)
E	371	3.286%	432.13	68.30	Heavily keyhole pores (PS:3–5%)
F	443	5.071%	544.58	77.41	Severely keyhole pores (PS:>5%)
G	116	0.908%	395.67	64.09	Mild keyhole pores (PS:0.5–1%)
H	258	2.734%	523.79	73.03	Moderate keyhole pores (PS:1–3%)

The results are the average of all microscopic images of each type.

Table 3. Deformation defect characterisation.

Class		Max deformation	Mean deformation	Deformation
A/B/C/D		None	None	None
E	E0	467.90 μm	176.31 μm	Moderate
F	F0	801.39 μm	417.25 μm	Heavy
G	G0	>>1 mm	>>1 mm	Severely
	G1	660.76 μm	302.64 μm
	G2	894.04 μm	489.85 μm
	G3	784.06 μm	367.84 μm
	G4	589.01 μm	235.9 μm
H	H0	>>1 mm	>>1 mm	Severely
	H1	851.99 μm	320.73 μm
	H2	680.71 μm	315.15 μm
	H3	817.42 μm	395.81 μm
	H4	1129.80 μm	682.87 μm

Figure 5. Intra-layer detail analysis method (IDA) diagram.

Figure 6. Inter-layer breadth analysis method (IBA) diagram.

Figure 7. Dynamic real-time analysis of unfused pore energy distribution based on IBA.

Figure 8. Dynamic real-time analysis of unfused pore energy fluctuation based on IBA.

Figure 9. Dynamic real-time analysis of high-frequency percentage of pore defects.

Figure 10. Correlation curve between number of keyholes and proportion of high frequency.

Figure 11. High and low frequency distribution interval.

Figure 12. Comprehensive analysis of deformation defect acoustic signal energy phenomenon.

Figure 13. LPBF Online Assessment Generic Process.

Figure 14. Time-frequency feature map of each class.

Figure 15. Schematic diagram of gⁿ-SC structure.

Figure 16. Schematic diagram of gⁿ-CH structure.

Figure 17. gⁿ-Res model structure.

Table 4. Model performance metrics.

Metrics	Equations	Notes
Accuracy	${\displaystyle \frac{\mathrm{TP}+\mathrm{TN}}{\mathrm{TP}+\mathrm{TN}+\mathrm{FP}+\mathrm{FN}}}$	/
Precision	${\displaystyle \frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FP}}}$	Positive Predicted Value (PPV)
Recall	${\displaystyle \frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FN}}}$	True Positive Rate (TPR) or Sensitivity
F1-score	$2\times {\displaystyle \frac{\mathrm{Precision}\times \mathrm{Recall}}{\mathrm{Precision}+\mathrm{Recall}}}$	Harmonic mean of Precision and Recall

Note: TP, FN, TN and FP represent true positive, false negative, true negative and false positive respectively.

Table 5. Parameters used for the model.

Training parameters	Model
Training objective	Classification
Rate of learning	0.001
Optimizer used	Adam
Total epochs	100
Training batch size	128
Shuffle	Ture
Loss	Cross-entropy loss
Train dataset	19,200(2400/class)
Validation dataset	4800(600/class)
Test dataset	4000(500/class)

Table 6. Performance comparison of high-order interactive modules.

Models	Auracy_score	Precision_score	Recall_score	F1_score
HorNet	81.97%	84.36%	81.97%	81.48%
HorNet_CH	82.25%	82.98%	82.25%	81.68%
HorNet_SC	87.65%	88.80%	87.65%	87.33%

Table 7. Network performance comparison.

Models	Auracy score	Precision score	Recall score	F1 score
ANN	47.20%	52.19%	47.20%	43.49%
SVM	42.08%	42.35%	42.08%	41.97%
GRU	76.08%	77.31%	76.08%	73.50%
LSTM	72.75%	72.88%	72.75%	70.78%
AlexNet	81.20%	85.58%	81.20%	80.36%
VGG	88.35%	90.14%	88.35%	87.72%
ViT	87.60%	89.32%	87.60%	87.18%
Efficientnet	87.15%	89.40%	87.15%	87.18%
Res_SE	88.10%	89.80%	88.10%	87.99%
Res_ECA	89.98%	91.88%	89.98%	89.88%
Res_CMBA	89.83%	91.80%	89.83%	89.73%
Res_HorNet	90.17%	92.1%	90.17%	90.05%
g^n-Res_CH	90.47%	91.15%	90.17%	90.10%
g^n-Res_SC	90.70%	91.50%	90.70%	90.40%

Data availability statement

The raw/processed data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study.