E
Eric Lacoste
Researcher at University of Bordeaux
Publications - 43
Citations - 686
Eric Lacoste is an academic researcher from University of Bordeaux. The author has contributed to research in topics: Welding & Liquid metal. The author has an hindex of 12, co-authored 39 publications receiving 494 citations. Previous affiliations of Eric Lacoste include Arts et Métiers ParisTech & Centre national de la recherche scientifique.
Papers
More filters
Journal ArticleDOI
Main defects observed in aluminum alloy parts produced by SLM: From causes to consequences
TL;DR: In this article, a bibliographical study is presented to identify and classify the parameters and phenomena which influence the appearance of defects in aluminum alloy parts produced using the SLM process and hence the final properties of these parts.
Journal ArticleDOI
An investigation on thermal, metallurgical and mechanical states in weld cracking of Inconel 738LC superalloy
TL;DR: In this paper, the influence of various conditions of Inconel 738 superalloy welding or deposition welding has been studied in order to shed light on the coupling between thermal, metallurgical and mechanical states in the heat affected zone (HAZ) in which cracking may occur particularly during welding and post-weld heat treatment.
Journal ArticleDOI
Benzimidazole‐pyrrolidine/H+ (BIP/H+), a Highly Reactive Organocatalyst for Asymmetric Processes
Eric Lacoste,Emilie Vaique,Muriel Berlande,Isabelle Pianet,Jean-Marc Vincent,Yannick Landais +5 more
TL;DR: In this article, a chiral benzimidazole-pyrrolidine was devised, which exhibits excellent activities in aminocatalyzed aldol reactions, leading to high yields and enantioselectivities in the presence of an equimolar amount of a Bronsted acid.
Journal ArticleDOI
Benzoimidazole–pyrrolidine (BIP), a highly reactive chiral organocatalyst for aldol process
TL;DR: A new chiral benzoimidazole–pyrrolidine ligand was shown to catalyze an aldol process in the presence of an equimolar amount of a Bronsted acid, leading to the aldl adduct in high yield and enantioselectivity.
Journal ArticleDOI
In Situ Monitoring Systems of The SLM Process: On the Need to Develop Machine Learning Models for Data Processing
TL;DR: The types of process defects that can be monitored via process signatures captured by in situ sensing devices and recent advancements in the field of data analytics for easy and automated defect detection are reviewed.