Importance of the Process Database
In 3D printing enterprises, process knowledge is the most critical technical asset. Building a process database can effectively accumulate process experience, improve process design efficiency, reduce the cost of process trial-and-error, and support intelligent transformation. It is an important lever for enhancing a company’s technical capabilities. This article systematically explains how to build a process database from four dimensions: data collection, data structure, knowledge extraction, and application scenarios.
Data Collection System
1. Scope of Data Collection
| Data Category | Data Content | Collection Source | Collection Frequency |
|---|---|---|---|
| Material data | Material grade, batch, properties, supplier | Incoming inspection, supplier information | Each batch |
| Equipment data | Equipment parameters, status, maintenance records | Equipment monitoring, maintenance records | Real-time/periodic |
| Process data | Printing parameters, environmental parameters, process data | Equipment sensors, operation records | Each batch |
| Quality data | Dimensional, appearance, and performance test results | Inspection equipment, inspection records | Each batch |
| Project data | Customer information, requirements, solutions, feedback | Project records, customer feedback | Each project |
| Cost data | Material cost, labor cost, equipment cost | Financial system, production records | Each batch |
2. Data Collection Methods
- Automatic collection: Automatic collection by equipment sensors and monitoring systems
- Manual entry: Operators enter production records and inspection records
- System integration: Integration with ERP, MES, PLM, and other systems
- Document scanning: Scanning and archiving paper documents
- External import: Importing supplier information, standards, and specifications
3. Data Quality Control
- Data completeness: Ensure all required fields have data
- Data accuracy: Reliable data sources and accurate entry
- Data consistency: Keep the same data consistent across different systems
- Data timeliness: Collect and enter data promptly
- Data security: Data backup and access permission control
Data Structure Design
1. Material Database Structure
- Basic information: Material grade, category, supplier
- Physical properties: Density, melting point, thermal conductivity
- Mechanical properties: Tensile strength, yield strength, elongation
- Powder characteristics: Particle size distribution, flowability, oxygen content
- Process parameters: Recommended process parameter ranges
- Application cases: Links to successful application cases
2. Process Parameter Database Structure
| Parameter Category | Specific Parameters | Unit | Remarks |
|---|---|---|---|
| Scanning parameters | Laser power, scanning speed, hatch spacing | W, mm/s, mm | Core parameters |
| Layer thickness parameters | Powder spreading thickness, slice layer thickness | mm | Affects accuracy |
| Environmental parameters | Build chamber temperature, oxygen content, shielding gas | °C, ppm | Affects quality |
| Support parameters | Support type, support density, support gap | - | Affects post-processing |
| Scanning strategy | Scan path, scan direction, rotation angle | ° | Affects stress |
3. Quality Database Structure
- Dimensional data: Measured values of key dimensions, deviations, trends
- Appearance data: Surface roughness, defect types, defect locations
- Performance data: Mechanical test results, fatigue performance
- Process data: Monitoring data during the printing process
- Statistical data: Pass rate, Cpk, process capability
4. Project Database Structure
- Project information: Project number, customer, requirements, solution
- Process information: Material, parameters, equipment, operators
- Quality information: Test results, acceptance conclusion
- Cost information: Material cost, labor cost, total cost
- Feedback information: Customer evaluations, issue feedback, improvement suggestions
Knowledge Extraction Methods
Submit a model, drawing, image or written notes. Engineers will review material, process, finishing and delivery based on actual use.