What Data Looks Like for This Category
Business data for forging and die forging comes from raw material quality certificates, furnace heating systems, press collection data, MES systems, non-destructive testing equipment, and customer forging drawings. Data updates occur for each individual forging, each batch of forgings, or each furnace run. Daily documents include forging process specifications, cutting lists, furnace heating records, forging process cards, mold life records, heat treatment work orders, and ultrasonic testing reports. Fields include furnace batch number, blank weight, forging ratio, heating temperature, defect type and location, among others. Units include kilograms, degrees Celsius, millimeters, and similar measurements.
Constraints Imposed by These Characteristics on Workflow Orchestration
Data sources are scattered across multiple independent systems. Workflows must support pulling information from different data sources across nodes to eliminate manual entry errors. Data updated per furnace run or batch requires workflows to support on-demand triggering, to adapt to small-batch, multi-batch production models. The need for validation across multiple documents and fields requires workflows to include built-in field consistency check nodes. For example, verify that material furnace batches match forging batches. Severe error consequences require workflows to be configured with exception branches. Trigger alerts and batch isolation processes when defects such as folds or cracks are detected.
Configuration Settings
This page applies to FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Trigger Condition | Trigger per furnace run / single batch of forgings | Matches the update rhythm of forgings produced in furnace batches |
variables | Bind furnace batch number, forging ratio, defect type fields | Covers core business data validation and circulation requirements |
| Tool Call Timeout | 300–600 seconds | Adapts to time-consuming scenarios including pulling furnace heating data and parsing ultrasonic testing reports |
| Exception Branch Configuration | Split processing by error type | Addresses different exception scenarios such as forging scrap and batch isolation |
nodeId | Bind MES data node, non-destructive testing node | Links scattered multi-source business data |
| Result Rendering | Display defect location annotation diagrams | Matches the visual display requirements of inspection records |
Three Common Configuration Mistakes
- Chart tools render empty after being called. This occurs when correct inspection data
variablesare not bound, or data source mapping rules for chart rendering are not configured. - Workflow nodes return 400 errors. This occurs when required field integrity is not verified, such as failing to pass the furnace batch number parameter and triggering interface verification failure.
- AI chat nodes return empty results or errors without triggering exception handling. This occurs when branch jumps for empty results or errors are not configured, skipping the exception verification process directly.
How to Confirm Proper Configuration
- Trigger a simulated furnace run process, and verify that data pulled by each node matches actual business documents.
- Intentionally pass non-compliant fields, and confirm that the exception branch triggers the corresponding processing logic.
- Call the chart tool, confirm that the generated chart renders normally in the chat interface, and check that returned content only contains links.
- View workflow logs, and confirm that execution time for each node falls within the preset timeout configuration range.
The values provided here are common starting points and should be measured against the reader's own samples.