The Society of Engineering Science (SES) 2024 Conference will take place in Hangzhou, China from August 20 through 23, 2024.
Please consider submitting an abstract to Mini-Symposium 2.1 titled "Multi-Physical Processes in Granular Media: Experiments, Theory, and Modeling". The description and list of organizers follow.
Hard-magnetic soft materials (HMSMs) consisting of an elastomer matrix filled with high remnant magnetic particles can exhibit flexible programmability and rapid shape changing under non-contact activation, showing promising potential applications in soft robotics, biomedical devices and flexible electronics. Precise predictions of large deformations of hard-magnetic soft materials would be a key for relevant applications.
Dear Zhigang,
As of this writing, none of the Twitter (now called `X' or `x', you know best) accounts I follow have gone against me. Or, the Chinese either, for that matter.
Just noting, for the time being,
Just the way, you know, I could've left a PhD program (that one because I already had had an MTech from IIT Madras), and joined American Industry,
or,
Title: Modeling and Simulation Engineer
Category: Full-time position in industry
Employer: SLB
Location: United States, Texas, Sugar Land
Opening Date: 02/01/2023
Intro
The research is concerned with modeling of electroactive soft materials. Preference will be given to candidates with background in nonlinear finite element methods and constitutive modeling.
If you are interested, please send an email to me at: cvjmah [at] technion.ac.il with a single PDF file containing your CV, grades, names of two references, and a brief description of how your experience and background meet the requirements for this position.
Dear iMechanicians,
Let me bring your attention what I believe is the most comprehensive study in the area of hydrogen embrittlement of additively manufactured materials. You can find all details here:
G. Álvarez, Z. Harris, K. Wada, C. Rodríguez, E. Martínez-Pañeda.
Hydrogen embrittlement susceptibility of additively manufactured Stainless Steel 316L: influence of postprocessing, printing direction, temperature and pre-straining. Additive Manufacturing 78, 103834 (2023)
One the main challenges of developing data-driven models is the data-hungry nature of Artificial Neural Networks (ANNs). In our recent paper, we introduced a data augmentation approach to expand a small original dataset without conducting extra expensive high-fidelity simulations. We then used the original and augmented datasets for developing ANN models for non-linear path dependent composites. The obtained results showed the great impact of the data augmentation approach on the accuracy of the data-driven models.
