Knowledge Acquisition and Reasoning Model for Welding Information Integration Based on CNN and Knowledge Graph
Abstract
:1. Introduction
- The model with a CNN as the information-aware layer and a domain knowledge graph as the data center can produce deeply implicit knowledge acquisition (knowledge obtained from intuitive information through domain reasoning) and reasoning with certain cognitive abilities.
- The proposed model accomplishes the acquisition and reasoning of unstructured knowledge and provides a feasible solution for the integrated manufacturing of welding information.
- The design concept, which is based on a networked data structure considering a wide range of knowledge forms, enriches the driving model of intelligent systems.
2. Methods and Models
2.1. Information Acquisition
2.2. Knowledge Graph Reasoning
2.3. Model Design
3. Experimental Section
3.1. Data Processing
3.2. Knowledge Graph Construction
3.3. Model Training and Evaluation
4. Results Analysis
5. Discussion
5.1. Qualitative Comparison with Other Methods
5.2. Engineering Applications
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Objects | Data Category | Example |
---|---|---|
Entities | Standard | Number and name of the standard, name of production requirements, etc. |
Technology | Welding methods, auxiliary welding materials, related technologies, etc. | |
Design | Welding joint size, bevel form, welding position, weld performance design, etc. | |
Department | Manufacturing department, manufacturer, user, testing department, personnel, etc. | |
Manufacture | Products, equipment, tooling, welding machines, clamping, etc. | |
Quality | Product quality, defect detection, checking means, etc. | |
Experiment | Magnetic particle inspection, flaw detection test, fatigue test, etc. | |
Relations | belong_to | PA belongs to the welding position (PA, belong_to, welding position). |
reference | Bogie welding reference EN 15085 (Bogie welding, reference, EN 15085). | |
requirement | Welding-grade CP CA requires defect grade B (CPCA, requirement, B). | |
based_on | WPS was developed based on WPQR (WPS, based_on, WPQR). | |
applicable_to | ISO 17638:2016 applies to the magnetic particle inspection in the non-destructive testing of welds (ISO 17638:2016, applicable_to, magnetic particle inspection). |
Classified tasks | Category | Describe |
---|---|---|
thickness relation | material 1 > material 2 | Thickness of base material 1 is greater than base material 2 |
material 1 < material 2 | Thickness of base material 1 is less than base material 2 | |
material 1 = material 2 | Equal thickness of base material 1 and base material 2 | |
groove form | a | Fillet welds without groove |
V | Weld seam with Y-shaped bevel | |
HY | Weld seam with a one-sided “Y” bevel | |
joint type | T-joint | Base material forms a right-angle or near-right-angle joint shape |
butt joint | Relatively parallel joint shape of the base material | |
lap joint | Joint shape with base material partially overlapping | |
base material form | P + P | Both base materials are assembled in plates |
P + T | Base material consists of plate and tube |
ID | Categories | Train | Test | Total |
---|---|---|---|---|
1 | thickness relation | 1931 | 483 | 2414 |
2 | groove form | 1968 | 492 | 2460 |
3 | joint type | 1779 | 445 | 2224 |
4 | base material form | 2070 | 517 | 2587 |
- | Total | 7748 | 1937 | 9685 |
Network Structure | Categories | |||
---|---|---|---|---|
Thickness Relation | Groove Form | Joint Type | Base Material Form | |
InceptionV1 | 0.758 | 0.642 | 0.704 | 0.835 |
MobileNet | 0.759 | 0.636 | 0.696 | 0.821 |
ResNet | 0.755 | 0.640 | 0.696 | 0.790 |
VGG16 | 0.753 | 0.633 | 0.700 | 0.788 |
Joint Images | Acquired Knowledge | Reasoned Knowledge |
---|---|---|
Thickness relation is base material 1 > material 2. Groove form is a. Joint type is T-joint. Base material form is P + P (plate and panel). | Preheating temperature can be determined based on base material 1 preheating temperature. Welding position is PB. Assembly gap is 0–1. | |
Thickness relation is base material 1 = material 2. Groove form is V. Joint type is butt joint. Base material form is P + P (plate and panel). | Preheating temperature can be determined based on base material 1 preheating temperature. Welding position is PA. Assembly gap is 2–4. | |
Thickness relation is base material 1 < material 2. Groove form is HY. Joint type is lap joint. Base material form is P + P (plate and panel). | Preheating temperature can be determined based on base material 2 preheating temperature. Welding position is PB. Assembly gap is 2–4. | |
Thickness relation is base material 1 < material 2. Groove form is HY. Joint type is T-joint. Base material form is P + T (plate and tube). | Preheating temperature can be determined based on base material 2 preheating temperature. Welding position is PA. Assembly gap is 2–4. |
Methods | Accuracy | Interpretive | Knowledge Coverage | Scalability | Portability |
---|---|---|---|---|---|
CBR | normal | normal | normal | high | normal |
RBR | high | high | normal | low | low |
Model-based method | normal | low | normal | normal | normal |
Our method | high | high | high | high | high |
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Guan, K.; Sun, Y.; Yang, G.; Yang, X. Knowledge Acquisition and Reasoning Model for Welding Information Integration Based on CNN and Knowledge Graph. Electronics 2023, 12, 1275. https://doi.org/10.3390/electronics12061275
Guan K, Sun Y, Yang G, Yang X. Knowledge Acquisition and Reasoning Model for Welding Information Integration Based on CNN and Knowledge Graph. Electronics. 2023; 12(6):1275. https://doi.org/10.3390/electronics12061275
Chicago/Turabian StyleGuan, Kainan, Yang Sun, Guang Yang, and Xinhua Yang. 2023. "Knowledge Acquisition and Reasoning Model for Welding Information Integration Based on CNN and Knowledge Graph" Electronics 12, no. 6: 1275. https://doi.org/10.3390/electronics12061275
APA StyleGuan, K., Sun, Y., Yang, G., & Yang, X. (2023). Knowledge Acquisition and Reasoning Model for Welding Information Integration Based on CNN and Knowledge Graph. Electronics, 12(6), 1275. https://doi.org/10.3390/electronics12061275