Like many other FEA engineers, I have a library full of books describing the theory and basis of FEA. Some are great classics: Bath, Hughes, etc. I am happy to have spent hours with them in my graduated studies, but I mainly use them now to adjust the height of my screen. That said – don’t get me wrong – I really do believe that they made me a better FEA engineer.

In my search for a good applied FEA book over the years, I found Mac Donald’s “Practical Stress Analysis with Finite Elements“. A very good one that I recommend, but not as complete as this new publication by experienced FEA engineer Dominique Madier: “Practical Finite Element Analysis for Mechanical Engineers“. This book totally fills the missing link between theory books and FEA software. Madier explains it himself in this webinar: “How to Learn FEA”. I could not agree more with him…

By the way: another good source of practical FEA literature: NAFEMS Publications!

11 December 2020Comments Off on About FEA BooksNews

A new revision of Midas NFX is available for download at this address. Version 2020-R2 has a series of improvements and new functions. Among them, a new option that includes a user-defined function for external forces of particle analysis. See a complete list of change in the Release Notes.

12 November 2020Comments Off on New version of Midas NFXNews

Prestressing technique is used to decrease the tensile stresses that develop in a metal forming die (powder compaction, extrusion, stamping, etc.). In order to increase die life and to protect the shrink ring, the interference value between the two parts must be optimized.

The iterative variation of the design parameters (interference, diameters, material properties) using numerical simulation leads to a reduction of time and resources invested. This process can be automated in order to obtain an optimal design.

The tendency is usually the following: a higher interference leads to lower tensile stresses in the die during compaction, but to a higher stress in the shrink ring. Consequently, the optimized interference should be find to take advantage of the pre-stress tensional state of the die, without damaging the shrink ring. The failure of the shrink ring would reduce the fatigue resistance of the die.

The maximum principal stress for interference values of 0.05, 0.1 and 0.15 mm are shown on this image (section in the middle of the compacted part):

17 August 2020Comments Off on Numerical study of shrink ring and die interferenceBlog Article

This animation shows a proof of concept geometry that we realized for an electrical water heater.

Two graphite electrodes are immersed in a water tank. Three physics are involved. First, the static current conduction equation is resolved. Then, Joule heating energy is calculated in the entire geometry and used as a body force for the heat equation. Finally, temperature calculated is introduced in the Navier-Stokes equations

17 August 2020Comments Off on Proof of Concept test for Electric Water HeaterBlog Article

Topology optimization determines the distribution of material most suitable to a given objective. It is primarily used to produce a fundamental basis for the engineers at the conceptual design stage, or to generate ideas for new alternatives.

In order to express the distribution of materials in topology optimization, density variables of the finite elements created for analysis are used. The element density of 1 represents a part that requires the element, while 0 represents a part that does not require the element. Unlike parametric optimization, the only design variable is the element’s density. As such, the user does not specify separate design variables but composes an optimization problem using only the combinations of objective functions and constraints.

Like any optimization problem, Topology optimization includes the following fundamental elements:

Objective: In this problem, the objective is to minimized the static compliance (a function of element density expressed in the form of global deformation energy):

Where:

f : Load vector u : Global & element displacement vectors K: Global & element stiffness matrices

Design variables: Volume fraction (the n_nodes density values which determine whether material is present (1) or absent (0))

Geometric constraints: the initial unoptimized geometry:

Design evaluator: the linear elasticity solver of MidasNFX, that calculates deformation energy based on specified loads and boundary conditions.

Since we are looking for general guidelines of an optimal design (in practice, we should say “better design”), the next step consists of modifying the original CAD geometry to remove material when it’s not needed:

Topology optimization often leads to complicated organic-like products that cannot be manufactured using traditional processes (which is not necessarily the case here). Additive manufacturing, like 3D printing, is sometimes more adapted for this kind of design. Here, a printed version of the optimized part is produced.

This video summarizes the whole process:

15 August 2020Comments Off on From Design to Prototype using Topology OptimizationBlog Article