Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Lecture

Flexible Guides: Design and Optimization

In courses (2)

MICRO-200: Mechanism Design I

Ce cours introduit les bases de la mécanique des structures : calcul des contraintes et déformations provoquées par les forces extérieures et calcul des déformations. Ces enseignements théoriques sont appliqués à la conception des éléments importants des m ...

MICRO-201: Mechanism Design II

Le second semestre du cours « Conception de mécanismes » développe les capacités créatrices de conception appliquées au domaine des mécanismes microtechniques. Il s'attache tant aux connaissances (composants, principes physiques, dimensionnement) qu'au pro ...

Description

This lecture covers the design and optimization of flexible guides, focusing on topics such as stiffness calculation, admissible stroke calculation, bending moment distribution, and different types of flexure-pivots. The instructor discusses the importance of optimal driving points, stiffness in circular notch hinges, and the critical load in compression. Various hinge shapes, thickness considerations, and natural stiffness are also explored, along with the comparison of different pivot designs. The lecture concludes with insights on the impact of hinge length optimization on stiffness and the considerations for stress concentration effects in parallel stages.

This video is available exclusively on Mediaspace for a restricted audience. Please log in to MediaSpace to access it if you have the necessary permissions.

Instructors (4)

Born in 1973 and of Egyptian-Swiss nationality, Simon Henein grew up in Cairo. He obtained an engineering degree at the Ecole Polytechnique Fédérale de Lausanne (EPFL) in 1996 and went on to complete his doctorate at the EPFL in 2000. In 2001, he published the book “Conception des guidages flexibles” which has become a reference in precision engineering. He then joined the Centre Suisse d’Electronique et Microtechnique (CSEM), Neuchatel, Switzerland, where he conceived and developed mechanisms for robotic, aerospace, medical and watchmaking applications. He pursued his research career at the Paul Scherrer Institute, Villigen, Switzerland, where he developed instruments for the Swiss Light Source Synchrotron (SLS). Since November 1st, 2012, he is associate professor in microengineering at the EPFL, holder of the Patek Philippe Chair and director of the Micromechanical and horological design laboratory (Instant-Lab).

Danick Briand

Official source

Related concepts (27)

Bending moment

In solid mechanics, a bending moment is the reaction induced in a structural element when an external force or moment is applied to the element, causing the element to bend. The most common or simplest structural element subjected to bending moments is the beam. The diagram shows a beam which is simply supported (free to rotate and therefore lacking bending moments) at both ends; the ends can only react to the shear loads. Other beams can have both ends fixed (known as encastre beam); therefore each end support has both bending moments and shear reaction loads.

Bending

In applied mechanics, bending (also known as flexure) characterizes the behavior of a slender structural element subjected to an external load applied perpendicularly to a longitudinal axis of the element. The structural element is assumed to be such that at least one of its dimensions is a small fraction, typically 1/10 or less, of the other two. When the length is considerably longer than the width and the thickness, the element is called a beam.

Mathematical optimization

Mathematical optimization (alternatively spelled optimisation) or mathematical programming is the selection of a best element, with regard to some criterion, from some set of available alternatives. It is generally divided into two subfields: discrete optimization and continuous optimization. Optimization problems arise in all quantitative disciplines from computer science and engineering to operations research and economics, and the development of solution methods has been of interest in mathematics for centuries.

Data compression

In information theory, data compression, source coding, or bit-rate reduction is the process of encoding information using fewer bits than the original representation. Any particular compression is either lossy or lossless. Lossless compression reduces bits by identifying and eliminating statistical redundancy. No information is lost in lossless compression. Lossy compression reduces bits by removing unnecessary or less important information.

Lossless compression

Lossless compression is a class of data compression that allows the original data to be perfectly reconstructed from the compressed data with no loss of information. Lossless compression is possible because most real-world data exhibits statistical redundancy. By contrast, lossy compression permits reconstruction only of an approximation of the original data, though usually with greatly improved compression rates (and therefore reduced media sizes).

Related lectures (42)

Efforts in Structures II: Maximums and Signs

Analyzes efforts in trusses, emphasizing maximum values and sign distribution in different members.

Applications of GAMP

Delves into applying the GAMP algorithm to simplify the lasso problem and analyze optimization challenges in neural networks.

Compressed and Bent Posts: Interaction Verification and Second-Order Effects

Explores the resistance of compressed and bent columns, interaction verification, and second-order effects in structural analysis.

Degree of Freedom Analysis

Explores degree of freedom analysis, redundancy, and data reconciliation in process modeling and optimization.

Compression: Prefix-Free Codes