Cross-Review of Technical Documentation for Foundry (Sand Casting, Precision Casting, Die Casting): Workflow Orchestration

Daily documents for this category include foundry process cards, batching lists, heat records, mold history records, first article inspection records

What data looks like for this category

Daily documents for this category include foundry process cards, batching lists, heat records, mold history records, first article inspection records, nonconforming product review forms, and material test reports. Data sources include 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. Core fields include alloy batching ratio, melting temperature, pouring duration, mold cycle count, and defect type. Field units include degrees Celsius, seconds, weight parts, and integers.

Constraints imposed by these characteristics on workflow orchestration

Multi-source heterogeneous data sources require workflows to support cross-system data pulling and format alignment, to avoid validation failures caused by inconsistent data formats. High-frequency updated production data requires workflow trigger mechanisms to match production rhythms, and polling intervals should not be set too long. Core validation links must be associated with unique identifiers such as heat and mold cycle counts, to ensure defect tracing can accurately locate the corresponding production batch. Quality characteristics of liquid metal forming require workflows to focus on parameter validation for melting, pouring, and solidification stages, to adapt to the quality control logic of this category.

Configuration settings

This page's configuration applies to FastGPT v4.17.0.

Configuration ItemRecommended SettingRationale
variablesPreset variables for heat ID, mold cycle ID, and alloy gradeUsed to associate unique production identifiers across documents, to avoid mixing data from different heats
Node Trigger ConditionPoll for new heat records every 15-30 minutesMatches the production data update rhythm for this category per heat
Parallel Dialogue Node CountProcess up to 3 documents to be validated in parallelThe number of documents to validate per heat typically does not exceed 3, to avoid resource overload
nodeIdAssign independent IDs to batching validation, parameter interpretation, and defect tracing nodesFacilitates locating abnormal execution nodes in workflow logs
Timeout Threshold600 secondsCovers the average time range for melting parameter reading and spectrometer data pulling

Three common misconfigurations

  • Tool call modules do not invoke tools. Workflow tool node status shows unexecuted, and logs have no tool call records. Causes include failure to configure production identifier parameters required for tool triggering in variables, or failure to bind node trigger conditions to new heat record generation events.
  • Variables do not take effect. Dialogue node output fields are empty, and validation results lack core parameters. Causes include failure to correctly declare key-value mappings for variables, or failure to bind fields such as batching ratio and temperature from documents to workflow variables.
  • Parallel node execution errors. Output results from multiple dialogue nodes overlap. Causes include failure to assign independent nodeId values to each parallel node, leading to context data being overwritten by subsequent nodes.

How to confirm proper configuration

  • View workflow execution logs, confirm that each node assigned a nodeId has a corresponding execution record.
  • Import test documents for a single heat, check that heat ID and mold cycle ID in variables are correctly mapped to fields in the corresponding documents.
  • Trigger tool call nodes, verify that production data from melting furnaces and spectrometers can be pulled successfully.
  • Start parallel node tests, confirm that multiple nodes execute simultaneously and output results do not interfere with each other.

The values provided here are common starting points, and should be evaluated against the reader's own samples.