GGCM is based on a breakthrough non-linear equation solving technology that uniquely solves a large set of configurations, including:

• All types of 3D configurations, including complex and non-separable configurations of arbitrary size in linear time
• Configurations when the initial shape is far from the solution
• Degenerate problems
• Configurations with an arbitrary mixture of over- and under-constraining

GGCM provides a simple C/C++ API for easy integration with software applications. GGCM offers superior solvability, performance and flexibility, and is extensible, giving vast control and independence to the application.

With GGCM you can:

• Provide innovative solutions to solve complex engineering problems (e.g. large complex assemblies, linkages, weldments, surface design, kinematics with smooth behavior, part design)
• Deliver a high-performance application with variational solving performance on par with history-based solvers, linear on problem size
• Reduce development time by integrating a single, extensible 2D/3D solver to meet a full range of engineering and application needs

Key Features of GGCM:

• Unmatched, high-performance
• Sophisticated, non-linear equation solver for complex design and engineering problems
• 2D/3D integrated solver in one software system
• User can define highly complex expression (engineering) constraints, including new objects and constraints, solved simultaneously with the rest of the configuration. This allows:
  • Equilibrium analysis (e.g. hanging chains and pulleys, force analysis on assemblies with weights and springs),
  • Modeling physical properties (e.g. shearing force, bending moment, elasticity, air drag),
  • Complex assembly interactions (e.g. synchronized gears)
• Fine control over solver behavior for under-constrained configurations and dragging
• Solver automatically identifies Rigid Equivalence Classes (virtual subassemblies), i.e. assembly parts that are rigidly “glued” together to highlight design intent and diagnose problems
• Powerful and meaningful failure diagnostics that identify the smallest failing part of a problem
• Advanced variational constraints (e.g. curve length and area)