What data looks like for this category
The data for sheet metal blanking and bending scenarios mainly comes from CAD/CAM software, order BOM systems, sheet metal inventory databases, laser cutting machine and bending machine device logs, and MES production management systems. Daily produced documents include part unfolding drawings, CNC blanking programs, bending process cards, nesting diagrams, first-piece inspection records, and process flow cards. Order generation, daily inventory checks, and each production changeover trigger data updates. The data includes dedicated fields such as sheet metal thickness, bending radius, unfolding dimensions, hole position coordinates, and scrap material ratio. Units are mostly millimeters, square meters, and kilograms.
Constraints on model access and configuration
Sheet metal blanking and bending data comes from multiple dispersed sources with varied formats. Model access must support unified parsing and association of multi-source data. High-frequency data updates require configured call links to support real-time performance, to avoid quotation deviations caused by data lag. Under the multi-variety, small-batch business model, the model must adapt to sheet metal part drawings of different specifications, and cannot rely on fixed templates. Incorrect process parameters will directly lead to sheet material waste or assembly failure. The configuration link must strengthen verification and output constraints for dedicated fields.
How to set configurations
The following configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to long context input from multiple sheet metal drawings and process cards, avoiding content truncation |
maxTokens | 4000 token | Controls the output length of single-round quotations and process descriptions, ensuring complete information without timeout |
quoteMaxToken | 2000 token | Limits the length of referenced historical process documents, avoiding call failures caused by context overload |
temperature | 0.3–0.5 | Ensures consistency of sheet metal process parameters such as bending coefficients and nesting schemes, reducing random deviations |
| Workflow/API tool selection | auto | Automatically triggers process calculation tools to adapt to the dedicated business needs of sheet metal blanking and bending |
AIPROXY_API_TOKEN | Valid key generated by the proxy platform | Verifies large model call permissions, ensuring legitimate interface access |
Three common configuration errors
- Some nodes fail to complete execution during batch large model calls, and the interface returns a 500 status code. Cause:
maxContextis not configured to adapt to the context length of multiple sheet metal drawings, leading to context overflow and interruption. - Quotation results returned by the large model lack bending coefficient compensation parameters. Cause: The call prompt does not clearly require including sheet metal process dedicated fields, or the
temperaturevalue is too high, leading to loss of key parameters. - The visual model fails to parse uploaded CAD unfolding drawings. Cause: The
visionconfiguration item is not enabled, or the image format is not converted to a type supported by the large model.
How to verify a successful configuration
- Upload a single sheet metal unfolding drawing and the corresponding order BOM, initiate a model call, and check that the returned result includes dedicated fields such as bending coefficients and nesting schemes.
- Initiate a batch call test to verify that all nodes complete execution without context overflow errors.
- View the large model call log to confirm that
AIPROXY_API_TOKENverification passes, with no permission errors. - Adjust
temperatureto 0.2, compare the results of two calls, and confirm that the consistency of process parameters meets expectations.
The values provided are common starting points and should be measured against the reader's own samples.