About

Dr. Gracious Ngaile is a faculty member in the Department of Mechanical and Aerospace Engineering at NC State University. His long-term goal is to contribute to the dramatic reduction of the use of toxins in metal forming lubricants. He teaches several courses including Modern Manufacturing Processes, Mechanical Engineering Design, Materials Processing by Deformation, and Applied Finite Element Method. His teaching emphasizes hands-on experiences, modern manufacturing techniques, and comprehensive understanding of design processes. Dr. Ngaile's research interests focus on solid mechanics, metal forming processes, and numerical modeling, with his graduate students gaining experience in machine design and computational methods. Outside of his academic pursuits, he enjoys traveling.

Research topics

• Computer Science
• Materials science
• Chromatography
• Physics
• Mechanics
• Nanotechnology
• Polymer chemistry
• Metallurgy
• Chemistry
• Mechanical engineering
• Manufacturing engineering
• Electrical engineering
• Process engineering
• Chemical engineering
• Engineering
• Composite material

Selected publications

• A Material Removal Prediction Framework for Ball EEM Polishing in Precision Lens Manufacturing

  World Journal of Engineering and Technology · 2026-01-01

  articleOpen accessSenior author

  Lens surface parameters are critical to optical system performance and require increasingly stringent precision due to rising performance demands and continued technological miniaturization. Although machining processes such as diamond turning can produce lenses with high form accuracy, they are not free from surface defects. These defects are typically addressed through post-processing techniques such as polishing; however, depending on defect size and the selected removal process, polishing can become a complex and iterative task. This complexity can be mitigated through the application of machine learning algorithms to predict material removal behavior. This paper presents the development of a material removal prediction framework for ball elastic emission machining (EEM) polishing of lens surfaces, incorporating machine learning tools to improve process predictability and efficiency. The resulting model accepts surface characteristics and process parameters as inputs and predicts the final surface parameters following polishing. The model’s root mean square error (RMSE) is approximately 0.1 µm. Surface parameters achieved using the removal strategy on which this model is based include a peak-to-valley (PV) value of 0.2841 µm and a root mean square (RMS) roughness of 0.032 µm.

  PublisherOA PDFDOI

• Physics-Informed Preform Design for Flashless 3D Forging via Material Point Backtracking and Finite Element Simulations

  Journal of Manufacturing and Materials Processing · 2025-06-18 · 2 citations

  articleOpen access1st authorCorresponding

  Accurate preform design in forging processes is critical for improving part quality, conserving material, reducing manufacturing costs, and eliminating secondary operations. This paper presents a finite element (FE) simulation-based methodology for preform design aimed at achieving flashless and near-flashless forging. The approach leverages material point backtracking within FE models to generate physics-informed preform geometries that capture complex material flow, die geometry interactions, and thermal gradients. An iterative scheme combining backtracking, surface reconstruction, and point-cloud solid modeling was developed and applied to several three-dimensional forging case studies, including a cross-joint and a three-lobe drive hub. The methodology demonstrated significant reductions in flash formation, particularly in parts that traditionally exhibit severe flash under conventional forging. Beyond supporting the development of new flashless forging sequences, the method also offers a framework for modifying preforms during production to minimize waste and for diagnosing preform defects linked to variability in frictional conditions, die temperatures, or material properties. Future integration of the proposed method with design of experiments (DOE) and surrogate modeling techniques could further enhance its applicability by optimizing preform designs within a localized design space. The findings suggest that this approach provides a practical and powerful tool for advancing both new and existing forging production lines toward higher efficiency and sustainability.

  PublisherOA PDFDOI

• Harnessing Hydrodynamic Cavitation for Surface Modification and Strengthening

  Journal of Micro and Nano-Manufacturing · 2023-09-01

  articleSenior author

  Abstract Hydrodynamic cavitation (HC) shows promise for surface modification and strengthening. While previous research has explored its potential for surface hardening and polishing, the application of cavitation for surface texturing remains relatively unexplored. This paper aims to investigate the feasibility of using hydrodynamic cavitation for surface texturing and hardening, as well as identify the key process parameters that influence the outcomes. Computational fluid dynamics (CFD) simulations are utilized to analyze the behavior of cavitation under various conditions, and experimental validation is conducted. The study examines the influence of different chamber insert geometries on cavitation intensity and energy release. It also investigates the effect of process parameters on surface morphology and hardness. The results demonstrate that hydrodynamic cavitation can effectively strengthen specific regions of interest when the cavitation intensity is controlled. However, the formation of surface texture through plastic deformation may be limited to ductile materials or those with low yield strength. The study highlights the significance of utilizing suitable cavitation generators capable of continuously generating cavitation for consistent and controlled intensity. Preliminary results suggest that innovative vortex-based devices have the potential to deliver controlled cavitation intensity to desired areas.

  PublisherDOI

• Improving Material Formability and Tribological Conditions through Dual-Pressure Tube Hydroforming

  Journal of Manufacturing and Materials Processing · 2023-07-02 · 4 citations

  articleOpen access1st authorCorresponding

  Dual-pressure tube hydroforming (THF) is a tube-forming process that involves applying fluid pressure to a tube’s inner and outer surfaces to achieve deformation. This study investigates the effect of dual-pressure loading paths on material formability and tribological conditions. Specifically, pear-shaped and triangular cross-sectional parts were formed using dual-pressure modes where fluid pressure on the inside of the tubular blank was alternated with pressure on the outside surface of the tubular blank, causing the tube to expand/stretch and contract. During expansion, the tube conformed to the die’s cavity, while during contraction, the contact area between the die and the workpiece reduced, leading to decreased friction stress at the tube–die interface. Additionally, the reversal of pressure loadings caused the tubular blank to buckle, altering the stress state and potentially increasing local shear stress, improving material formability. Dual-pressure THF has demonstrated that the pressure loading paths chosen can substantially influence material formability. Comparing the geometries of parts formed by dual-pressure THF and conventional THF shows a significant increase in the protrusion height of both the pear-shaped and triangular specimens using dual-pressure THF.

  PublisherOA PDFDOI

• Energy field assisted metal forming: Current status, challenges and prospects

  International Journal of Machine Tools and Manufacture · 2023 · 99 citations

  • Computer Science
  • Mechanical engineering
  • Manufacturing engineering
  PublisherOA PDFDOI

• Preform Design for Flash-Less Die Forging

  Lecture notes in mechanical engineering · 2023-08-22

  book-chapterSenior author
  PublisherDOI

• A Robust Bubble Growth Solution Scheme for Implementation in CFD Analysis of Multiphase Flows

  Computation · 2023-03-31

  articleOpen accessSenior authorCorresponding

  Although the full form of the Rayleigh–Plesset (RP) equation more accurately depicts the bubble behavior in a cavitating flow than its reduced form, it finds much less application than the latter in the computational fluid dynamic (CFD) simulation due to its high stiffness. The traditional variable time-step scheme for the full form RP equation is difficult to be integrated with the CFD program since it requires a tiny time step at the singularity point for convergence and this step size may be incompatible with time marching of conservation equations. This paper presents two stable and efficient numerical solution schemes based on the finite difference method and Euler method so that the full-form RP equation can be better accepted by the CFD program. By employing a truncation bubble radius to approximate the minimum bubble size in the collapse stage, the proposed schemes solve for the bubble radius and wall velocity in an explicit way. The proposed solution schemes are more robust for a wide range of ambient pressure profiles than the traditional schemes and avoid excessive refinement on the time step at the singularity point. Since the proposed solution scheme can calculate the effects of the second-order term, liquid viscosity, and surface tension on the bubble evolution, it provides a more accurate estimation of the wall velocity for the vaporization or condensation rate, which is widely used in the cavitation model in the CFD simulation. The legitimacy of the solution schemes is manifested by the agreement between the results from these schemes and established ones from the literature. The proposed solution schemes are more robust in face of a wide range of ambient pressure profiles.

  PublisherOA PDFDOI

• Mechanical Performance Evaluation of the Printed Circuit Heat Exchanger Core Experiments Under Tension and Pressure Loading

  Volume 2: Computer Technology and Bolted Joints; Design and Analysis · 2022-07-17 · 1 citations

  articleOpen access

  Abstract The printed circuit heat exchanger (PCHE) has small channels with high surface area, making them an efficient solution for next-generation nuclear plants (NGNPs). These PCHEs are fabricated through a diffusion bonding process. This fabrication step changes the microstructure of wrought metal plates. The current ASME design code does not support the PCHE design for NGNPs due to a lack of test data. Hence, there has been initiative towards elevated temperature mechanical property characterization of the diffusion bonded material. One of the most common channel shapes is a semicircular channel with sharp corners. These corners act as a stress riser at the diffusion bonding interface. Evaluating elevated temperature mechanical performance of diffusion bonded material in the presence of stress risers is an essential step towards the ASME code development of PCHE design. This study selected two specimen geometries: the first is a PCHE bar specimen for tensile loading with three rows and three columns of channels, and the second is a lab-scaled PCHE with six rows and eight columns of channels. A set of elevated temperature monotonic and cyclic tests were conducted on the PCHE bar specimen to evaluate the mechanical performance under axial tensile loadings to study the failure mechanism. The lab-scaled PCHE specimens were tested under overpressure loads at room temperature, and pressure creep and pressure creep-fatigue loadings to mimic the realistic loading conditions observed in typical NGNPs. The X-ray scans of channeled specimens show interesting observations. The test results and observations are presented in the paper.

  PublisherOA PDFDOI

• Tribological Characteristics of Dual-Pressure Tube Hydroforming

  Defect and diffusion forum/Diffusion and defect data, solid state data. Part A, Defect and diffusion forum · 2022-02-24 · 2 citations

  article1st authorCorresponding

  Dual-pressure tube hydroforming (THF) is a tube hydroforming process variant whereby deformation of the tubular specimen is achieved by exerting fluid pressure on both the inside and outside surfaces of the tube. Dual-pressure THF experiments are conducted to study tribological conditions when producing pear-shaped and triangular parts. The pressure-loading paths are designed to exert pressure in oscillatory pattern: I.e., the pressure on the inside was alternated with pressure on the outside causing the tube to expand and contract/buckle as deformation progressed. During tube contraction, the metal-to-metal contact area is substantially reduced. This leads to reduction in friction stress at the tube-die interface, thus increasing formability. Comparing the geometries of the formed parts produced by dual-pressure THF and conventional THF reveals that the former results in a substantial increase in the protrusion height of a pear-shaped specimen.

  PublisherDOI

• Tribology in Manufacturing Processes

  Trans Tech Publications Ltd. eBooks · 2022-02-24 · 7 citations

  bookOpen access1st authorCorresponding

  This volume contains the selected papers presented at the 9th International Conference on Tribology in Manufacturing Processes and Joining by Plastic Deformation (ICTMP2021). The conference was held in Chennai, India, November 24-26, 2021. It provided academia and industry professionals an international platform for representing and discussing recent research results on lubrication and treatment of technical surfaces, friction-based processes in materials processing, tools wear, and development of the tribological systems for the manufacturing processes.

  PublisherDOI

Recent grants

• Ultrasonic Assisted Microextrusion and Microtube Hydroforming

  NSF · $294k · 2009–2014

• CAREER: Meso and Macro Hydroforming of Complex Shapes - Mechanics and Control

  NSF · $406k · 2005–2011

Frequent coauthors

• James Lowrie

  North Carolina State University

  18 shared

• Taylan Altan

  The Ohio State University

  15 shared

• Tasnim Hassan

  Khulna University of Engineering and Technology

  11 shared

• Yang Chen

  7 shared

• J.H.L. Pang

  7 shared

• Manas Shirgaokar

  The Ohio State University

  5 shared

• Lucas D. Maciel

  4 shared

• Heramb Prakash Mahajan

  Idaho National Laboratory

  4 shared