To view this presentation:

  • If you have an MRS account, click the Login button above.
  • New to MRS?  Create a free account here

  0      0

Webinars


MRS Communications 10th Anniversary Lectures, Part 1


Oct 8, 2020 2:00pm ‐ Oct 8, 2020 3:30pm

Description

This event, in honor of the 10th anniversary of MRS Communications, will feature talks from Joseph M. DeSimone who holds a joint appointment in the Stanford Department of Chemical Engineering and the Department of Radiology in the Stanford School of Medicine, and Grace Gu, Assistant Professor of Mechanical Engineering at the University of California, Berkeley. Dr. Gu was selected to deliver the 2020 MRS Communications Lecture based on her work in artificial intelligence for materials design and additive manufacturing.

Both speakers will hold live Q&A sessions with the audience at the conclusion of their respective talks.

Talk Presentations:

  • Machine learning for composite materials
    Grace Gu, University of California, Berkeley
  • Digital Transformation in Manufacturing
    Joseph M. DeSimone, Stanford University

Grace Gu's abstract: Machine learning (ML) has been perceived as a promising tool for the design and discovery of novel materials for a broad range of applications. In this talk, we will discuss computational methods and ML algorithms that can be used to investigate design principles and mechanisms embedded in materials with superior properties. Additionally, we will present algorithms and sensor technologies that can be implemented to detect and resolve problems in current additive manufacturing processes, allowing for automated quality assessment and the creation of functional and reliable structural materials. In the future, this algorithmically driven approach will enable materials-by-design of complex architectures, opening up new avenues of research on advanced materials with specific functions and desired properties.

Joseph M. DeSimone abstract:
Until recently, 3D printing was largely relegated to prototyping and small-scale projects due to fundamental limitations—slowness and an inability to generate objects with adequate mechanical strength and thermal properties that would entail widespread, durable utility. A limited range of materials also hindered the ability to make parts comparable to injection molded parts. Rethinking the basic physics and chemistry, we invented Digital Light SynthesisTM (DLS) to address these longstanding major drawbacks. Introduced on the cover of Science [2015, 347, 1349], DLS is now transforming how parts are manufactured in industries including automotive, footwear, and medicine. Its revolutionary nature as a digital manufacturing technology creates possibilities for market-shifting transformations with significant economic and environmental implications. This lecture will discuss opportunities and challenges associated with launching a subscription-based digital manufacturing breakthrough. Digital transformation is easy for software-centric products and businesses but much harder for products and businesses in the physical world. Greater momentum now exists for this digital transformation due to the technological revolution in additive manufacturing (3D printing) and the urgency for greater dynamism and adaptability in supply chains—with the COVID-19 pandemic serving as the clearest example of the economic and societal challenges that occur when supply chains are disrupted on a grand scale. With this transformation, additive manufacturing will enable local-for-local production; continuous digital thread and the smart factory; scaling personalized products (e.g. perfectly fitting prosthetics, surgical implants from advanced imaging modalities); rapid product introductions; and end of inventory with just-in-time production—stimulating greater supply chain dynamism and adaptability.

Speaker(s):

You must be logged in and own this session in order to post comments.

Print Certificate
Completed on: token-completed_on
Print Transcript
Please select the appropriate credit type:
/
test_id: 
credits: 
completed on: 
rendered in: 
* - Indicates answer is required.
token-content

token-speaker-name
token-index
token-content
token-index
token-content
token-index
token-content
token-index
token-content
token-index
token-content
token-index
token-content
/
/
token-index
token-content
token-index
token-content