BEGIN:VCALENDAR
VERSION:2.0
PRODID:icalendar-ruby
CALSCALE:GREGORIAN
X-WR-CALNAME:Abdelrahman Amin to Present Master's Research
X-WR-TIMEZONE:Eastern Time (US & Canada)
BEGIN:VEVENT
DTSTAMP:20260521T012845Z
UID:tag:localist.com\,2008:EventInstance_49852920248353
DTSTART:20250618T190000Z
DTEND:20250618T210000Z
DESCRIPTION:The UTC Graduate School is pleased to announce that Abdelrahman
  Amin will present Master's research titled\, A MULTISCALE APPROACH TO CON
 TROLLING CORROSION IN MAGNESIUM-BASED MATERIALS: NANOPARTICLE ALLOYING\, H
 YBRID COATINGS\, AND PREDICTIVE MODELING on 06/18/2025 at 3-5 pm in EMCS 4
 03. Everyone is invited to attend. \n\nEngineering\n\nChair: Dr. Hamdy Ibr
 ahim\n\nAbstract:\n\nBiodegradable materials have garnered significant int
 erest over the past decades as alternatives to non-degradable implant mate
 rials used in osteosynthesis surgeries. Among these\, magnesium and its al
 loys have emerged as promising candidates due to their favorable mechanica
 l properties and biocompatibility. However\, the high degradation rate of 
 magnesium in aqueous environments remains a major limitation to its broade
 r application. To address this challenge\, recent research has focused on 
 tailoring the corrosion behavior of magnesium using alloying\, coatings\, 
 and various fabrication processes to suit specific clinical needs. In this
  work\, we explore two distinct approaches to enhance the performance of m
 agnesium-based implants. The first approach involves the use of powder met
 allurgy to incorporate nanoparticles into magnesium alloy powders and inve
 stigate their impact on key performance properties. The second novel appro
 ach focuses on systematically examining the influence of different coating
  parameters for a hybrid micro-arc oxidation (MAO) and sol-gel coating pro
 cess to gain a deeper understanding of the factors’ role in achieving de
 sirable implant characteristics. Additionally\, we present an innovative m
 achine learning (ML) model developed to predict the corrosion behavior of 
 MAO-coated magnesium. This model\, applied for the first time to deal with
  the very complex corrosion behavior of magnesium-based systems\, utilizes
  key process parameters as predictors. Such an ML approach is expected to 
 minimize material usage and reduce experimental time when predicting the c
 orrosion behavior of MAO-coated magnesium materials.
GEO:39.104318;-84.513955
LOCATION:Engineering & Computer Science Building\, 403
SUMMARY:Abdelrahman Amin to Present Master's Research
URL;VALUE=URI:https://calendar.utc.edu/event/abdelrahman-amin-to-present-ma
 sters-research
CATEGORIES:Lectures & Presentations
END:VEVENT
END:VCALENDAR