<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The paper establishes a related differential equation model about changes in financial interest rates. It uses information related to liquidity to feedback the law and stability of differential equations in interest rate changes. The article applies stochastic processes and partial differential equations to complex financial networks to confirm node yields in financial market networks. It confirms the existence of interest rate stickiness in Chinese financial markets. The advantage of this interest rate model is that when the external economic environment changes, the state of interest rates will also change accordingly.</p>
</abstract>

The paper establishes a related differential equation model about changes in financial interest rates. It uses information related to liquidity to feedback the law and stability of differential equations in interest rate changes. The article applies stochastic processes and partial differential equations to complex financial networks to confirm node yields in financial market networks. It confirms the existence of interest rate stickiness in Chinese financial markets. The advantage of this interest rate model is that when the external economic environment changes, the state of interest rates will also change accordingly.

Mathematical simulation experiment based on optimisation of heat treatment process of aluminium alloy materials

Department of Accounting and Finance, Faculty of Administrative Sciences

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

The paper establishes a related differential equation model about changes in financial interest rates. It uses information related to liquidity to feedback the...

Process parameters	Production line tradition	Neural network model optimisation
Annealing temperature/°C	360	330
Annealing time/h	7	5.5
Solution temperature/°C	530	510
Solution time/h	4.5	6
Aging temperature/°C	180	160
Aging time/h	17	14

Sample	Heat treatment process				HRB hardness
Sample	θ1/°C	t1/h	θ2/°C	t2/h	Actual value	Predictive value	Relative value/%
1	460	0.5	110	20	41.2	40.9	0.71
2	460	1	120	48	55.7	55.6	0.18
3	460	1	130	75	61.2	61	0.32
4	460	1.167	110	100	60.3	60.5	−0.33
5	460	1.167	120	30	45.2	45.3	−0.22
6	460	1.167	130	50	51.3	51.3	0
7	470	0.5	120	100	62.5	62.4	0.16
8	470	0.5	130	125	54.3	53.9	0.73
9	470	1	110	20	38.7	37.8	2.3
10	470	1	120	48	55.7	55.9	−1.4
11	470	1	130	75	49.5	48.7	1.6
12	470	1.167	110	100	59.8	60.2	−0.67
13	470	1.167	120	30	41.7	42.1	−0.95
14	470	1.167	130	75	56.8	57	−0.35
15	480	0.5	110	45	53.9	54.2	−0.55
16	480	1	120	40	50.8	50.6	0.39
17	480	1	120	20	53.2	52.9	0.56
18	480	1.167	130	30	55.6	56.3	−1.23
19¹⁾	460	1	120	55	57.5	57.9	−0.69
20¹⁾	470	1	110	50	56.8	56.9	−1.7
21¹⁾	470	1.617	110	55	58.3	58.3	0
22¹⁾	470	1.617	120	50	62.5	61.9	0.96
23¹⁾	480	1	120	45	60.1	59.8	0.49

Mathematical simulation experiment based on optimisation of heat treatment process of aluminium alloy materials

Publicado en línea: 15 dic 2021

Páginas: 609 - 616

Recibido: 16 jun 2021

Aceptado: 24 sept 2021

DOI: https://doi.org/10.2478/amns.2021.1.00083

Palabras clave
neural network, aluminium alloy, heat treatment, process optimisation, wear performance, numerical simulation

© 2021 Jing Su et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Process parameters of 2618 aluminium alloy for automobiles

Artificial neural network prediction points and verification points

Mathematical simulation experiment based on optimisation of heat treatment process of aluminium alloy materials

Publicado en línea: 15 dic 2021

Páginas: 609 - 616

Recibido: 16 jun 2021

Aceptado: 24 sept 2021

DOI: https://doi.org/10.2478/amns.2021.1.00083

Palabras claveneural network, aluminium alloy, heat treatment, process optimisation, wear performance, numerical simulation

© 2021 Jing Su et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Process parameters of 2618 aluminium alloy for automobiles

Artificial neural network prediction points and verification points

Palabras clave
neural network, aluminium alloy, heat treatment, process optimisation, wear performance, numerical simulation