Allocation and scheduling of deposition paths in a layer for multi-robot coordinated wire and arc additive manufacturing of large-scale parts

Figures & data

Figure 1. Path planning for MRC-AM by directly segmenting a slice into sub-regions.

Figure 2. Overall workflow for allocating deposition paths for MRC-WAAM.

Figure 3. The computational mechanism for assigning paths to multiple robots.

Figure 4. Generation of deposition tasks.

Figure 5. Defining the work periods (WPs) of a deposition plan.

Figure 6. The mechanism for checking the swept area of robots within a WP.

Figure 7. The workflow for scheduling a deposition plan for the MRC-WAAM system.

Figure 8. The implemented test platform.

Figure 9. Spatial relationship among robots and the centre of part.

Table 1. Chemical composition of substrate and fed-wire (wt.%).

Materials	C	Mn	Si	S	P	Ni	Cr	Fe
ER70S-6	0.06–0.15	1.40–1.85	0.80–1.15	≤0.025	≤0.025	≤0.15	≤0.15	rest
Q235B	0.12-0.2	0.3–0.7	≤0.30	≤0.045	≤0.045	≤0.30	0.30	rest

Table 2. Parameters applied for experimental validation.

Parameters	Value
Deposition speed	6 mm/s
Voltage of deposition	20.9 V
Current of deposition	165 A
Nozzle-to-substrate distance	12.0 mm
Height of a single bead	2.5 mm
Width of a single bead	7.5 mm
Step-over distance between adjacent beads	5 mm
Interlayer temperature	27°C
Height of a layer	2.5 mm

Figure 10. The models used for validation.

Figure 11. The influence of k on the algorithm performance.

Figure 12. The path allocation results using the proposed top k% method.

Figure 13. The results of assigning paths individually.

Table 3. Comparison between the two developed approaches.

	Computation time	EWL	GOA	Ω
Model 1 (327 paths)
Individual allocation	0.125 s	99.9%	70.0%	0.85
Top k%	0.027 s	87.0%	98.5%	0.93
Model 2 (137 paths)
Individual allocation	0.046 s	99.3%	53.3%	0.76
Top k%	0.011 s	94.3%	97.1%	0.96
Model 3 (268 paths)
Individual allocation	0.071 s	70.2%	98.1%	0.84
Top k%	0.014 s	73.4%	99.3%	0.86
Model 4 (180 paths)
Individual allocation	0.055 s	92.7%	88.9%	0.91
Top k%	0.013 s	80.9%	93.3%	0.87

Figure 14. The Gantt diagram of the deposition process.

Figure 15. Results of deposition tasks generation and scheduling.

Figure 16. The distances among the swap areas over the work periods.

Figure 17. The appearance of the deposited slice.

Figure 18. The deposition plan scheduled using the strategy proposed by Bhatt et al. [31].

Figure 19. The deposition plan scheduled using the strategy proposed by Shen et al. [33].

Table 4. Comparison of the proposed method with the state-of-the-art.

Methods	SEM	Number of start-arc point	Number of turns
Bhatt et al. [31]	N.A.	10	183
Shen et al. [33]	80.9%	13	293
This paper	92.1%	10	127

Bhatt PM, Nycz A, Gupta SK. Optimizing multi-robot placements for wire arc additive manufacturing, in: Proceedings of the 2022 International Conference on Robotics and Automation (ICRA), 2022, pp. 7942–7948.

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Shen H, Pan L, Qian J. Research on large-scale additive manufacturing based on multi-robot collaboration technology. Addit Manuf. 2019;30:100906. doi:10.1016/j.addma.2019.100906

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Data availability statement

The data presented in this study are available on request from the corresponding author.