How do you use simulation and data-driven approaches to enhance your mechanism design optimization results?

For mechanical equipment, the best tool is Finite Element Analysis (FEA). This tool is often included, such as in Autodesk Inventor, or it can be purchased separately, like in the case of Solidworks. Except for cases involving pumps, where studying fluid performance is necessary, FEA will suffice. For analyses using Computational Fluid Dynamics (CFD), I recommend the software Ansys.

As a final-year mechanical engineering student, here's how I enhance my 3D modeling and mechanism design optimization: • Smart 3D Modeling: Start with clear goals, choose appropriate software (e.g., Fusion 360 for collaboration, SolidWorks for complex assemblies), focus on feature-based and parametric design, and always design with manufacturing in mind. • Simulation-Driven Design: Utilize FEA for stress analysis, motion simulations for kinematics, and multi-physics simulations when necessary. Start with simplified models and progressively refine them based on results and real-world data.

I gained hands-on knowledge of software tools while working on machinery design projects. Tools such as Ansys, SolidWorks Simulations, and MBD are nowadays essential for designing mechanical systems. I have used FEA, thermal analysis, and natural frequency simulations in my work. These tools are basically to verify and optimize a part/product for a certain FOS. These tools help save resources and reduce the risk of product failure.

Data-driven optimization occurs in situations where you're either developing through reverse engineering or creating the evolution of an existing piece of equipment. In both scenarios, you start with the design of a pre-existing piece of equipment or part. You can then utilize usage indicators of this equipment and perform statistical analyses to understand the primary reasons for failure. This allows you to reinforce weak regions and reduce weight in oversized regions.

Design of Experiments (DOE) is very useful, but I typically use it primarily in practical tests. Computational tests can also benefit from this experimental planning technique, but in practice, I've found that experimentation by interaction is more interesting. Changing angles might alleviate stress in one part of the piece but introduce mechanical stress in another. Such transfers are difficult to predict when using DOE to create experiments. A visual analysis of the piece or equipment as a whole, and changing one or several parts at once and refining by interaction, brings more results for me.

A Monte Carlo model may be more complicated than what most cases require. In most industrial applications, basic principles of mechanics will suffice to understand which characteristics to modify. The main characteristics to consider and modify are: - Material: type of material, hardness, and mechanical resistances. - Lever: always aim to reduce the force of the lever that a part may be experiencing to improve resistance. - Acute angles and edges: eliminating sharp edges and acute angles whenever possible significantly improves resistance. - Bracing: the more, the better. Keep these principles in mind, and success will follow you.

One thing I know for sure: someone will know more than you. Share your project with your team, even those who are not from the development area. It's incredible the number of good ideas and advice that can come from various places. Do not underestimate the knowledge of those around you, do not overestimate yourself, and always practice clear and inclusive communication.