Modeling and Optimizing the Composite Prepreg Tape Winding Process Based on Grey Relational Analysis Coupled with BP Neural Network and Bat Algorithm

Table 3 Grey relational grade results

No.	Deviation sequence ∆_0i		Grey relational coefficient		GRG
No.	Tensile strength	Void content	Tensile strength	Void content	GRG
1	0.6915	0.8976	0.4196	0.3578	0.4116
2	0.5790	0.7840	0.4634	0.3894	0.4538
3	0.4162	0.2316	0.5457	0.6835	0.5635
4	0.5575	0.8043	0.4728	0.3834	0.4613
5	0.0247	0.0000	0.9529	1.0000	0.9590
6	0.1789	0.3507	0.7365	0.5878	0.7173
7	0.3064	0.6110	0.6201	0.4500	0.5981
8	0.6322	0.8540	0.4416	0.3693	0.4323
9	0.0192	0.0697	0.9630	0.8777	0.9520
10	0.0312	0.0950	0.9412	0.8403	0.9282
11	0.3785	0.8390	0.5691	0.3734	0.5439
12	0.5200	0.7043	0.4902	0.4152	0.4805
13	0.4016	0.6012	0.5546	0.4541	0.5416
14	0.5565	0.6731	0.4732	0.4262	0.4672
15	0.7699	0.5356	0.3937	0.4828	0.4052
16	0.0169	0.0392	0.9673	0.9272	0.9622
17	0.0000	0.1607	1.0000	0.7568	0.9686
18	0.1651	0.2717	0.7518	0.6479	0.7384
19	0.8868	0.6390	0.3606	0.4390	0.3707
20	0.7435	0.3798	0.4021	0.5683	0.4236
21	0.8234	0.9061	0.3778	0.3556	0.3749
22	0.1933	0.3077	0.7212	0.6190	0.7080
23	1.0000	0.9885	0.3333	0.3359	0.3337
24	0.6559	0.7750	0.4326	0.3922	0.4273
25	0.1560	0.2588	0.7622	0.6589	0.7488
26	0.4488	0.7431	0.5270	0.4022	0.5109
27	0.3261	0.6110	0.6053	0.4500	0.5852
28	0.6303	1.0000	0.4424	0.3333	0.4283
29	0.2134	0.6390	0.7008	0.4390	0.6670