This has been a long time coming since, Dassault Systèms acquisition of SolidWorks in 1997. Back then there were discussions on how SolidWorks, Catia V5 and Enovia would work together.
Forward This is part 3 of a multi-part series which goes through the custom joint replacement to a finger due to rheumatoid arthritis. Part 1 Scan data to CAD Part 2 CAD to FEA Part 3 FEA to Fatigue Durability Analysis (fe-safe/Rubber) The two silicone variants reported in Leslie et al (2008) are compared to
Forward This is part 1 of a multi-part series which goes through the custom joint replacement to a finger due to rheumatoid arthritis. Part 1 Scan data to CAD Part 2 CAD to FEA Part 3 FEA to Fatigue
The presentation, paper and magazine articles are provided by clicking here. To jump straight to the animations in a YouTube playlist click here.
Designing a medical device commonly starts with the healthy or diseased anatomy. Geometry without significant simplifications typically comes from various 3D scanning technologies such as CT or MRI. Here we will go through turning scanned point cloud data into usable NURBS CAD geometry with Catia. FOR FREE ACCESS THE FILES CREATED FOR THIS POST PLEASE CLICK HERE
During an interview I was once asked “You’re a mechanical engineer why did you get into programming?” My response was “I’m lazy and repetitive tasks are boring!” Some repetitive tasks do not lend them selves to automation, however, they are still boring and error prone as your mind wanders. This post details how creating a custom toolbar in Catia that can alleviate some of this tedium. These methods can be extrapolated to just about anything. For example I have a specific toolbox that I use when working on: mountain, road or now kids bikes.
Introduction The purpose of this post is to explain how to utilize medical imaging data in the development of a prosthetic implant. The two most common medical imaging technologies are CT and MRI. Both export a stack of 2D grey scale images over a 3D domain in the standard Digital Imaging and Communications in Medicine (DICOM) format. In this post I will go through the development of geometric (CAD) and mechanical (FEA) models based off anatomical imaging data. Through this workflow designs can be tuned for specific biometry based on realistic loading scenarios. As always all of the models used to develop this post are available at the end of the article.
Topology optimization creates an organic geometry flowing material to where it is needed and eroding where it is not efficient. This technology is ideally suited to the limited manufacturing constraints that 3D printing offers. 3D printed parts by virtue of their layer by layer additive manufacturing approach have complex material properties. These properties are similar to wood where there is a stiff direction (with the grain) and a weak direction (across the grain). To gain the highest performance in 3D printed parts these material properties must be considered in the design process.
One NASA adage is: Better, Faster, Cheaper…pick 2 The premise is that the third desire is mutually exclusive. This is obviously an oversimplification of the process however it quickly brings to light the interplay between goals. I like to think of design requirements from an optimization standpoint. There are constraints and goals. Constraints are those that the design must meet otherwise it is not a viable product. Goals are parameters that you would like to improve.
Interview related to this work https://www.youtube.com/watch?v=vGeig6tIvyU&feature=youtu.be Introduction In this post I will go through the methodology to perform topology optimization with Catia (CAD), Abaqus (FEA) and Tosca (Topology Optimization). Topology optimization evolves the geometry to remove unneeded material effectively minimizing weight. This is carried out by automatically scaling individual element’s density and stiffness based on the stress state of the previous simulation. This is an iterative process where material flows to regions to satisfy constraints and minimize the objective function. The created geometry represents the maximum allowable geometry and would be a heavy stiff head. High stiffness is desirable however weight is not. This will be the basis for the objective function of the optimization. The basic workflow is to create CAD geometry with the maximum allowable footprint. Create a standard FEA simulation. Create a topology optimization setting goals and constraints. You can download the files created in this article freely below.