Research Vision: Democratization of manufacturing through digital innovation and intelligence.

Research goal: Study fundamental principles of manufacturing science to develop innovative solutions for advanced manufacturing challenges.

Manufacturing is a hierarchical process, and issues are often addressed by focusing on and optimizing adjacent upstream steps. This strategy is commonly known as the ‘low-hanging fruit’ approach, as it offers immediate but often ad-hoc solutions. While useful for quick fixes, such methods frequently result in narrow, localized, and machine-specific optimizations that fail to tackle deeper inefficiencies in the manufacturing process. My research shifts the focus from these surface-level interventions to a more fundamental level of investigation through the integration of design-materials-manufacturing science and digitization. This approach involves understanding and utilizing intrinsic material properties, data-driven design quantification, process analytics, and energy interactions occurring at the micro, or even atomic, levels. These interactions are then aggregated to influence the macro-scale manufacturing process outcomes as innovative advanced manufacturing solutions. Following my research philosophy, I have been granted (or in-process of securing) three patents on three distinct and novel manufacturing process.

This methodological shift toward fundamental, generalized solutions will push the boundaries of manufacturing science and innovation by enabling a more robust, adaptive, and efficient manufacturing ecosystem. By leveraging newly generated knowledge, my research lab aims to democratize advanced manufacturing processes and systems, thereby revolutionizing human-machine synergies in manufacturing engineering. The outcome of this research will provide an innovation platform for capturing advanced manufacturing ingenuity across critical sectors of the U.S. economy, including healthcare, aerospace, automotive, and construction industries. The scholarly objectives of this program include pursuing scientific questions in manufacturing science, securing major grant funding, and fostering collaboration through multidisciplinary teams.

Bio-manufacturing and Tissue Engineering: (Khoda, B. et. al. 2024 Biofabrication); (Khoda, B. et. al. 2024 Bioprinting); (Khoda, B. et. al. 2020 JMSE); (Khoda, B. et. al. 2021 JMP); (Khoda, B. et. al. 2019 JMP);
The goal of this research is to develop a high-throughput cell cultivation process using microreactor spheroids, which provide a 3D matrix to enhance cell growth, protein production, and biomolecule removal at higher cell densities and throughput compared to traditional suspension culture. The project aims to investigate the physics of hydrodynamic instability for the rapid and sustainable formation of micro-droplets on fiber surfaces dipped in cell-laden bioink. Additionally, it will examine how the 3D structure, mechanical properties, and chemistry of the bioink facilitate cell growth and tissue manufacturing for tissue engineering and bio-molecular farming. This project will advance the fundamental understanding of factors regulating biomacromolecule (e.g., recombinant protein) manufacturing processes and will contribute to scaling up production rates with an improved current Good Manufacturing Practice (cGMP) process.

Generative Design with Deep-Learning for Manufacturable Topology: (Khoda, B. et. al. IDETC 2023) (Khoda, B. et. al. 2021 3DPAM); (Khoda, B. et. al. 2021 Sci. Report); (Khoda, B. et. al. 2020 JMSE); (Khoda, B et. al. 2018 RPJ); (Khoda, B et. al. 2017 JMP);
A novel additive metal structure manufacturing process is developed with a continuous rod. The primary objective is to address the unexplored potential in the Ashby chart, particularly focusing on enhancing modulus while reducing density. To achieve solutions to this complex problem, we will employ deep learning techniques for generative design, incorporating Graph Neural Networks (GNN) to harness the power of artificial intelligence (AI).

Targeted Retention by Adhesion and Prevention (TRAP):(Khoda, B. et. al. 2024 and 2022 Sci. Report); (Khoda, B. et. al. 2023 Appl. Mechanics); (Khoda, B. et. al. 2023 Prog. Coating); (Khoda, B. et. al. 2022 Appl. Mechanics); (Khoda, B. et. al. 2021 JMSE); (Khoda, B. et. al. 2021 JMNM);
The objective of the research is to investigate the physical mechanism behind the selective entrapment of inertial particles from a non-colloidal mixture, a manufacturing process first reported by my lab. The orderly transfer and retention of granules and particles by entrapment mechanism is a unique phenomenon, which is driven by the higher-order multi-physics problem of particle-substrate collision, hydrodynamics of the particles, viscous polymer layer formation and interfacial adhesion science. My lab will work to generate a semi-supervised computational model of TRAP using meta-data with hybrid CNN-GNN (convolution and graph neural network) framework. The research goal is to establish the selective particle entrapment process as an advanced manufacturing technology which can enable the manufacturing of next-generation materials and devices, including energy storage, tubular structures, synthetic blood vessels, tissue scaffolds, flexible electronics, and filtration devices.

PFAS Removal with Functionalized and Patterned Porous Structure: (Khoda, B. et. al. MSEC 2024);
Per- and polyfluoroalkyl substances (PFAS) are commonly found in agricultural water and PFAS can be released into the irrigation water and food processing cycles, thus causing adverse effects on human health and the environment. Current technologies for the removal and treatment of PFAS either have insufficient selectivity and efficiency in degrading/removing PFAS or resource expensive. To fill the gaps, the primary objective of this project is to seamlessly integrate AI-based computational design, advanced manufacturing techniques and Metal-Organic Frameworks (MOF) nano-material technology to develop a tailored PFAS filtration platform which will be targeted for selective PFAS removal in a high flow-rate environment and accessible to small, disconnected communities. This proposed platform offers adjustability at macro, micro, and nano scales, presenting potential advances in materials and manufacturing processes.

Resource Efficiency in Additive Technology:Khoda, B. et. al. 2020 JMSE; Khoda, B et. al. 2018 RPJ; Khoda, B et. al. 2017 JMP; Khoda, B et. al. 2017 RCIM
The goal of this research is to create process behaviors analytics for solid and cellular porous 3D-printed objects. One of the major constraints of additive manufacturing processes is that they consume a significant amount of resources (i.e., time, energy and material, support structure and cost) to fabricate parts, which is often tied to the part and process attributes. The objective is to establish a relationship among design, geometry, process variables, material distribution, and AM capabilities while establishing resource consumption mechanisms. This research is built upon balancing the hierarchical AM eco-system with primary emphasis on the pre-processing stage followed by downstream optimization.

Porous infill design and 3D printing: Khoda, B. et. al. 2021 JMSE; Khoda, B. et. al. 2018 RPJ; Khoda, B. et. al. 2023 ASME Eng.
A new fabrication pattern for honeycomb infill is proposed for additive manufacturing applications. The proposed pattern will uniformly distribute the material and can accommodate controllable variational honeycomb infill while maintaining continuity with relative ease. The infill structures are fabricated with both uniform and variational patterns, which are then compared with the traditional tool-path pattern with compression testing. The results show that the proposed design demonstrates uniform densification under compression and performs better while absorbing more energy. Studying novel pattern and their impact on mechanical properties will help understand the design-performance relationship of the 3D printed parts.