Maple Connector for SysML is an add-on library for the Maple mathematical software platform. It enables users to connect Maple directly to SysML-based system models, allowing the extraction, transformation, and analysis of parametric information—such as parameter values, engineering units, and mathematical relationships—stored within SysML models. The connector supports integration with MBSE (Model-Based Systems Engineering) platforms like Teamwork Cloud and is evolving to support the SysML v2 standard. Its primary purpose is to automate the transfer of engineering data between system models and analytical tools, reducing manual errors and improving traceability and efficiency in engineering workflows.

Key Features

Automated Extraction of Parametric Data Automatically pulls parameter values, units, and mathematical relationships from SysML models.

• Eliminates manual transcription errors
• Supports both explicit and implicit relationships

Transformation to Executable Mathematical Assets Converts SysML model data into executable mathematical expressions for analysis in Maple and other environments.

• Enables direct computation and simulation
• Supports integration with various computational tools

Consistency Checking and Error Flagging Provides built-in checks for data consistency and flags modeling errors.

• Identifies missing or inconsistent data
• Reduces risk of calculation and design errors

Engineering Units and Dimensional Analysis Maintains and utilizes engineering units for all extracted parameters.

• Prevents unit-conversion mistakes
• Supports rigorous dimensional analysis

Traceability and Analysis Monitoring Enables traceability from analytical results back to SysML model elements.

• Monitors system design progress
• Flags properties exceeding design limits

Support for SysML v2 and MBSE Workflows Integrates with current and emerging SysML standards and MBSE platforms.

• YAML-configurable API for flexible integration
• Designed for future SysML 2.0 analysis semantics

Benefits

Reduced Errors and Improved Data Integrity Automation and consistency checks lower the risk of human error.

• Fewer manual transcription mistakes
• Early detection of modeling inconsistencies

Faster Time-to-Solution Streamlines the process from model to analysis.

• Rapid formulation and execution of solutions
• Reduces project delays and unbudgeted costs

Enhanced Traceability and Knowledge Management Links analytical results to their origins in the system model.

• Facilitates troubleshooting and design validation
• Centralizes engineering calculations for reuse