What Data for This Category Looks Like
Daily documents for this category include foundry process cards, material batching lists, heat records, mold history logs, first-piece inspection records, material test reports, and non-conforming product review forms. Data sources cover ERP material master data, melting furnaces and thermometers, spectrometers, MES heat records, mold management systems, customer drawings, and technical specifications. Data updates occur per heat, per batch, or daily. Each single document includes fixed fields such as heat number, alloy composition mass fraction, return charge addition amount, melting temperature, pouring parameters, and defect type. Fields use clear units: temperature in degrees Celsius, and batching in mass parts.
What Constraints These Characteristics Impose on HTTP Interfaces and External Systems
Multiple heterogeneous data sources require interfaces to support cross-system data pulling and field mapping, to match data formats from different workshop systems. High-frequency update cycles require interfaces to have low latency, to avoid losing real-time heat data due to timeout waits. Fixed document fields and units require interfaces to strictly validate field validity and format consistency, preventing invalid data from entering the review process. Cross-document association requirements — such as tracing shrinkage cavities, gas pores, and slag inclusions to heat number and mold number — require interfaces to support query parameters with associated IDs, enabling linked matching of data across multiple systems.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
Authorization | Bearer {workshop system static secret} | Adapts to the static authentication standard used by most industrial on-site systems, simplifying integration workflows |
| Interface timeout period | 30–60 seconds | Matches the average interval for single heat data generation, avoiding real-time data loss from timeouts |
variables | Configure field mapping rules | Enables corresponding conversion between external system fields and standard foundry document fields |
| Data pull granularity | Single heat / single batch | Matches document update frequencies, avoiding bulk data overload that reduces review efficiency |
responseData | Configure JSON format parsing | Compatible with data output formats from most workshop systems, reducing parsing costs |
| Field validation switch | Enable strict validation | Filters invalid data that does not conform to foundry document field specifications, improving review accuracy |
Three Common Mistakes
- Symptom: Interface returns
401 Unauthorizedstatus code, unable to pull external system data. Cause:Authorizationparameter is not configured correctly, and the authentication key required by the workshop system is not included. - Symptom: Melting temperature field is empty in pulled heat records. Cause: No temperature field mapping rule is configured in
variables, so the original temperature field from the external system is not correctly converted to the target document field. - Symptom: Alloy batching ratio validation fails, leading to scrapping of entire heat or batch castings. Cause: No reasonable interval validation is configured for batching ratios, out-of-range return charge addition data is imported into the review process by mistake, and risks are not intercepted in advance.
How to Confirm Configuration Is Complete
- The FastGPT v4.17.0 interface debugging page is accessed, the configured HTTP interface call is manually triggered, and the returned
responseDatais checked for included preset foundry document fields. - Single heat batching list and furnace temperature data are simulated and pushed to the interface, verifying that the interface correctly receives and parses field content without format errors.
- Interface operation logs are reviewed, confirming that
Authorizationauthentication passes, and no 401 or 403 permission errors occur. - Latest heat data from external systems is associated, checking if the latest melting temperature, batching ratio and other information can be pulled according to the configured pull granularity.
The values listed are common starting points, and should be evaluated against the reader's own samples.