Prediction Model of Stress Intensity Factor of Circumferential Through Crack in Elbow Based on Neural Network

Table 1

Network connection weights and thresholds.

Weight	Hidden layer node j					Weight	Hidden layer node j
	1	2	3	4	5		6	7	8	9	10
Input node i	4.3785	−3.1792	2.4674	−1.1211	−3.8841	Input node i	1.0034	−0.9732	−3.8699	3.6769	−3.2183
	1.2491	−3.3175	2.8006	4.2375	1.6564		4.0752	2.5648	3.7926	−0.3202	−3.467
	−2.9388	−2.8731	3.929	3.1867	−3.3968		3.4284	4.6736	−0.0861	3.9681	−2.644
Output node j	0.4383	0.5581	−0.6342	0.5707	−0.9485	Output node j	−0.1852	0.8129	−0.3391	0.839	0.3792
Threshold b	−5.4192	5.2291	−5.0389	4.8488	4.6586	Threshold b	−4.4685	4.2783	4.0882	−3.898	3.7079
Weight	Hidden layer node j					Weight	Hidden layer node j
	11	12	13	14	15		16	17	18	19	20
Input node i	−3.5057	−0.437	−0.7508	3.7164	2.6689	Input node i	3.09	−2.321	−2.1968	2.9501	5.1046
	−2.5833	−0.5516	−5.2312	2.7578	3.2876		−1.8204	4.7142	2.0777	2.2599	−1.1519
	−3.2256	−5.3733	1.1993	2.8197	3.3818		−4.0628	1.3257	−4.4972	3.9443	1.4087
Output node j	0.9053	0.7169	0.644	−0.0452	−0.2266	Output node j	0.9053	0.7169	0.644	−0.0452	−0.2266
Threshold b	3.5177	3.3276	3.1374	−2.9473	−2.7572	Threshold b	3.5177	3.3276	3.1374	−2.9473	−2.7572
Weight	Hidden layer node j					Weight	Hidden layer node j
	21	22	23	24	25		26	27	28	29	30
Input node i	−3.4534	2.3038	−1.2402	4.7259	3.7538	Input node i	−2.9538	3.4927	3.542	−4.07	3.6032
	2.4541	−4.5069	0.1324	2.2871	−3.8169		−4.3738	1.1006	2.3476	−2.42	2.0621
	−3.3792	−1.9362	5.2738	−1.3426	−0.8414		1.2301	−3.9947	3.3632	−2.6357	−3.4832
Output node j	0.4937	−0.8105	−0.8095	0.8194	0.5549	Output node j	−0.7003	−0.7726	−0.5565	0.4276	−0.3155
Threshold b	1.6163	−1.4261	1.236	−1.0458	−0.8557	Threshold b	0.6655	−0.4754	−0.2852	0.0951	0.0951
Weight	Hidden layer node j					Weight	Hidden layer node j
	31	32	33	34	35		36	37	38	39	40
Input node i	−1.9891	3.3707	−3.0278	−3.6975	−1.3709	Input node i	2.9496	4.0848	−0.5431	−1.8461	−3.8839
	1.8096	2.1345	−3.1601	3.9596	−5.2084		−1.8193	2.9899	4.0422	−3.2275	2.9865
	4.705	3.6674	3.196	0.1349	−0.6009		4.1663	−1.9346	3.5684	−3.9425	−2.316
Output node j	−0.5323	0.309	−0.4771	−0.1008	0.3014	Output node j	−0.6356	−0.0663	0.0656	0.2326	−0.1816
Threshold b	−0.2852	0.4754	−0.6655	−0.8557	−1.0458	Threshold b	1.236	1.4261	−1.6163	−1.8064	−1.9966
Weight	Hidden layer node j					Weight	Hidden layer node j
	41	42	43	44	45		46	47	48	49	50
Input node i	−4.4054	−4.0902	4.1267	−0.8775	3.8594	Input node i	−3.8526	−0.5874	0.9973	−4.6247	0.6137
	2.9959	3.5286	−1.8715	2.5035	3.3894		−3.0495	2.2212	4.8828	1.2335	−3.7087
	−0.9926	0.4329	2.9725	−4.7255	−1.7276		2.286	4.9081	−2.1287	2.5414	−3.9035
Output node j	−0.178	0.3571	0.861	−0.9113	0.8466	Output node j	−0.1699	−0.9944	−0.3267	−0.5529	0.1583
Threshold b	−2.1867	−2.3769	2.567	−2.7572	2.9473	Threshold b	−3.1374	−3.3276	3.5177	−3.7079	3.898
Weight	Hidden layer node j					Weight	Hidden layer node j				Output node
	51	52	53	54	55		56	57	58		1
Input node i	2.2577	−2.4167	3.017	−5.3484	2.6632	Input node i	−2.6809	−1.3532	−4.0905		−0.1685
	3.6244	1.9388	0.1105	−0.6734	4.4392		−2.8943	−4.034	−1.7712
	3.3369	4.4462	4.5004	−0.5559	1.6027		3.7154	−3.3562	−3.0819
Output node j	0.5577	−0.4144	−0.5542	0.0968	0.728	Output node j	−0.0869	0.4252	−0.3411
Threshold b	4.0882	−4.2783	4.4685	−4.6586	4.8488	Threshold b	−5.0389	−5.2291	−5.4192