Geometric Quality Prediction in Direct Energy Deposition Using Scanning Technology Based on Process Parameters

Jayden Gaydosh

Additive manufacturing (AM) is a developing manufacturing process that provides unique capabilities compared to more traditional manufacturing, producing components not previously possible without assembly or additional processes. Metal AM specifically has a promise of huge impacts in automotive, aerospace, and other industries reliant on high-quality and complex metal components. A form of metal AM known as Directed Energy Deposition (DED) has additional unique strengths like metal alloy abilities, and sight-specific deposition and repair on existing parts, in addition to the typical advantages of AM over more conventional manufacturing methods. The main issue with DED and other metal additive manufacturing processes like it stems from the inability to maintain strict quality control of manufactured parts. Current manufacturing attempts to optimize process parameters of DED prior to manufacturing to ensure quality control, but can require iteration, and due to the nature of AM can depend on the geometry of the given part. Using real-time monitoring has the potential to monitor the effects of DED process parameters as a manufacturing process is actively being done. Based on this challenge, the authors proposed a methodology that monitors the geometry discrepancy of  DED additive manufacturing using the characteristics of process parameters. The parameters considered in this study are exposure time, a distance of scanner, reflection, and spread of powder. Real-time monitoring can effectively be done using laser line scanner technology.. The ability to actively record geometrical data of specimens produced during the DED process creates the ability to further investigate real-time control of DED process parameters for quality control of specimens

Major: 
Mechanical Engineering
Exhibition Category: 
Engineering
Exhibition Format: 
Poster Presentation
Campus: 
University Park
Faculty Sponsor: 
Eden Yemesegen
Poster Number: 
51839