Browsing by Author "Joy, Ranjit"

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Effect Of Inter-Layer Dwell Time on Residual Stresses in Directed Energy Deposition of High Strength Steel Alloy
(University of Texas at Austin, 2023) Joy, Ranjit; Wu, Sung-Heng; Tariq, Usman; Mahmood, Muhammad Arif; Liou, Frank
Adoption of metal additive manufacturing by various industries is being hindered by the presence of residual stresses and distortion in the deposited parts. Large thermal gradients during directed energy deposition often led to residual stresses in the final deposit. Parameter optimization is predominantly used for residual stress mitigation. However, the effect of process parameters is material specific. Current research aims to study the effect of inter-layer dwell time on residual stresses in directed energy deposition of high strength steel alloy. Specimens were deposited at three levels of inter-layer dwell time. Surface as well as bulk residual stresses were measured using X-ray diffraction. Both surface as well as bulk residual stresses were found to increase with an increase in the inter-layer dwell time.
OPTIMIZATION OF COMPUTATIONAL TIME FOR DIGITAL TWIN DATABASE IN DIRECTED ENERGY DEPOSITION FOR RESIDUAL STRESSES
(University of Texas at Austin, 2023) Tariq, Usman; Joy, Ranjit; Wu, Sung-Heng; Arif Mahmood, Muhammad; Woodworth, Michael M.; Liou, Frank
Metal Additive Manufacturing (MAM) has experienced rapid growth and demonstrated its cost-effectiveness in the production of high-quality products. However, MAM processes introduce significant thermal gradients that result in the formation of residual stresses and distortions in the final parts. Finite Element Analysis (FEA) is a valuable tool for predicting residual stresses, but it requires substantial computational power. This study aims to reduce computational time by incorporating a thermo-mechanical model specifically designed for the Directed Energy Deposition (DED) process using Ti6Al4V. This model predicts the thermal history and subsequent residual stresses in the deposited material. Various FEA methods, including “chunk”, layer, and conventional methods are examined, providing a comparative analysis of computational cost and numerical accuracy. These findings contribute towards the realization of a digital twin database, where the incorporation of efficient and accurate FEA models can optimize part quality and strength while reducing computational time.
Role of In-situ Monitoring Technique for Digital Twin Development using Direct Energy Deposition: Melt Pool Dynamics and Thermal Distribution
(University of Texas at Austin, 2023) Wu, Sung-Heng; Joy, Ranjit; Tariq, Usman; Mahmood, Muhammad Arif; Liou, Frank
Direct energy deposition (DED) is a promising additive manufacturing technique that enables the fabrication of complex structures with excellent mechanical properties. The quality of the final product depends on several parameters, including melt pool dynamics and thermal distribution. For process monitoring and continuous improvement of digital twins, in-situ monitoring allows real-time tracking of these parameters, providing valuable data for process optimization. However, existing monitoring methods are limited in their accuracy due to emissivity issues. To address this challenge, an in-house visible spectrum camera has been proposed for real-time process monitoring via dual-wavelength technique. Based on the analyses, the area and thermal distribution inside the melt pool can be estimated accurately. The data from the camera can be integrated into a digital twin’s continuous improvement, providing efficiency, and reducing the manufacturing cost.
State-of-the-art Cyber-enabled Physical and Digital Systems Deployed in Distributed Digital Factory Using Additive and Subtractive Manufacturing Systems: Open, Scalable, and Secure Framework
(University of Texas at Austin, 2023) Joy, Ranjit; Wu, Sung-Heng; Tariq, Usman; Mahmood, Muhammad Arif; Isanaka, Sriram Praneeth; Malik, Asad Waqar; Liou, Frank
A distributed digital factory (DDF) integrates physical and digital systems, leveraging additive manufacturing (AM) and subtractive manufacturing (SM), to enable the dispersed production of components. Existing work focuses on digital twins, AM and SM systems, and some security aspects. Nevertheless, a holistic view of integrating devices with dynamic provisions to invoke digital twins has limited supporting research. This paper will detail cyber-physical and digital systems deployed in DDFs. The components of cyber systems, including AM & SM equipment, sensors, communication protocols, and monitoring software, are covered. Challenges associated with the design and deployment of DDFs, such as security, scalability, and interoperability, are detailed. The assessment emphasizes an open framework for DDF development, allowing system integration from vendors & participants across diverse locations and capabilities. The article also examines the significance of a scalable and secure framework for the implementation of DDFs, which ensures the dependability and availability of on-demand manufacturing.