Computational and Experimental Optimization of Injection-Molded Compliant Constant-Torque Mechanisms in Polymeric...

Area of Science:

• Mechanical Engineering
• Materials Science
• Robotics

Background:

• Compliant constant-torque mechanisms (CTMs) are crucial for applications requiring consistent rotational resistance.
• Injection molding of polymeric materials offers a scalable manufacturing route for CTMs.
• Optimizing CTM performance requires understanding geometric influences on torsional behavior.

Purpose of the Study:

• To computationally and experimentally optimize polymeric CTMs fabricated via injection molding.
• To investigate the impact of geometric variations on the torsional strength of CTMs.
• To validate the predictive accuracy of artificial neural network (ANN) models against experimental data.

Main Methods:

• Utilized computer-aided engineering (CAE) for numerical simulations of CTM geometries.
• Performed experimental validation to quantify the mechanical performance of CTMs.
• Developed and employed artificial neural network (ANN) models for performance prediction.

Main Results:

• Geometric configuration significantly influences CTM torsional behavior; circular cross-sections exhibit superior strength.
• ANN models demonstrated high prediction accuracy, closely aligning with experimental outcomes (97% similarity for Model 4).
• CAE simulations predicted a 23-44 degree range at 0.142 N·m, while experiments and ANN showed a wider 20-46 degree range at 0.164 N·m.

Conclusions:

• Integrating CAE, ANN, and experimental techniques is vital for optimizing polymer-based CTMs.
• Discrepancies highlight the need for refined material modeling and manufacturing processes.
• Optimized polymer CTMs show promise for precision engineering, biomedical devices, and soft robotics.