What this category's data looks like
Data for this category covers the entire machining process flow. Daily documentation includes process routes, operation cards, tool lists, CNC programs, first-piece inspection records, patrol inspection forms, and measuring tool calibration records. Data sources include product drawings and 3D models, CAPP/CAM systems, machine tool collection data, tool management systems, MES systems, and measuring and inspection tool data. Data updates are triggered by work orders, single-part machining, spot inspections, or tool replacements. Fields include process number, tool model, cutting parameters, inspection dimensions, machine tool operation logs, and other relevant fields. Units include millimeters, revolutions per minute, newtons, and other machining-specific units.
What constraints these characteristics impose on the "workflow orchestration" link
This page is written for FastGPT v4.17.0. Multi-source heterogeneous data sources require workflows to bind multiple types of data source nodes, and cross-system data association rules must be configured via variables. The high-frequency update trigger rhythm requires workflow trigger conditions to match real-time events such as work order startup and tool replacement, to avoid invalid execution. The diverse document structures and specialized field units require each workflow node to preset parsing rules for corresponding fields, ensuring that information from different operation cards and inspection records can be uniformly identified. At the same time, error consequences include workpiece scrap and machine tool downtime, so workflows must be configured with abnormal verification nodes to intercept parameters that do not meet process requirements in advance.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
variables | Bind data sources from machine tool collection, MES, and tool management systems | Data for this category comes from multiple systems, so variables must be used to uniformly associate data across nodes |
| Trigger condition configuration | Bind work order ID and tool replacement events | Data updates are triggered by work orders and tool replacements, matching the business update rhythm |
nodeId | Assign a unique identifier to each process processing node | Distinguish parsing logic for different processes, adapting to diverse operation card structures |
| Single recall result count | Top 5 entries | Match the typical case display needs for defect analysis, avoiding information overload |
| Concurrency configuration | 20-30 concurrency per node | Adapt to 4c16g hardware resources, avoiding operational overload |
| Knowledge base selection rules | Associate corresponding knowledge bases by process type | Match process parameters and defect analysis case data for different processes |
Three Common Misconfigurations
- No image recognition results appear after the workflow starts, and the interface displays empty input fields. The cause is failure to bind the association rule between the image upload node and the AI recognition node via
variables, and failure to configure automatically triggered trigger conditions. - Calls fail after publishing as an API interface, with status code 400 returned. The cause is failure to configure required parameter validation for
variablesin the workflow, leading to failure to pass necessary fields such as work order ID during calls. - Process crashes occur when a single node runs at 50 concurrency. The cause is failure to adjust the concurrency upper limit based on hardware configuration, and failure to set current limiting rules for the request queue, exceeding the resource carrying capacity of 4c16g hardware.
How to Confirm Proper Configuration
- Trigger the workflow with test work order data, check whether the input and output of each node match the field structure of the corresponding data source.
- Call the API interface with preset parameters, check whether the returned results include corresponding content for defect analysis, confirming that no parameters are missed during transmission.
- Simulate multiple concurrent requests, observe the node operation status, confirm that resource usage does not exceed the hardware carrying range.
- Switch knowledge bases of different process types, check whether the workflow automatically associates the corresponding knowledge base and completes matching of defect cases.
The values provided are common starting points and should be measured against the reader's own samples.