Ankit Dilipkumar Oza – Manufacturing – Best Researcher Award

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Ankit Dilipkumar Oza - Manufacturing - Best Researcher Award

Parul University - India

AUTHOR PROFILE

SCOPUS

🛠️ EXPERTISE IN MICRO-MACHINING AND SPARK MACHINING

Ankit Dilipkumar Oza holds a Ph.D. in micro-machining, specializing in spark-assisted electrochemical discharge machining (TW-ECDM) processes. His extensive experience in micro-machining non-conducting materials, particularly Quartz, has made him a key figure in this advanced manufacturing domain. His expertise also includes operating sophisticated analytical instruments such as SEM, EDX, XRD, and TEM, which are crucial for analyzing microstructures and machining precision.

⚙️ INNOVATIVE TW-ECDM TECHNIQUES AND SETUP DEVELOPMENT

During his Ph.D. research, Ankit focused on improving the Traveling Wire-Electrochemical Discharge Machining (TW-ECDM) process to minimize the challenges in machining Quartz, a non-conducting material. His research led to the development of two experimental setups and a Micro-ECDM setup integrated into an existing EDM system, marking significant progress in the field of non-conducting material machining. His innovative approaches in coating wires, optimizing electrolytes, and employing Magnetohydrodynamic (MHD) convection have advanced machining capabilities.

📐 DESIGN AND MODELING EXPERTISE

Ankit possesses a deep understanding of designing and modeling experimental setups for advanced machining processes. His knowledge extends to the design of experiments and statistical analysis, ensuring precision and efficiency in complex machining operations. His contributions have been pivotal in improving machining performance, especially in reducing electrolyte wastage and enhancing electrolysis in the TW-ECDM process.

🔬 RESEARCH IN QUARTZ MICRO-MACHINING

The core of Ankit’s Ph.D. research revolves around overcoming the difficulties in machining Quartz materials. By using innovative techniques, his work has significantly improved the performance of machining processes for non-conducting materials, setting new standards in the field. His research contributes to the advancement of micro-machining technology, making it more efficient and sustainable.

🏗️ POSTGRADUATE PROJECT IN GRAVITY DIE CASTING

In addition to his work in micro-machining, Ankit developed a novel hybrid gravity die casting process during his postgraduate studies. Using PRO-CAST, a FEM-based simulation software, he optimized metal flow, temperature variations, and solidification, resulting in a more efficient and defect-reduced process. His work contributed to reducing common casting issues like shrinkage porosities and cold shuts, improving the overall quality of gravity die casting.

🚀 EXPERIENCE IN INDUSTRIAL RESEARCH AND DEVELOPMENT

Ankit's industrial project at Mehtex Engineering Pvt. Ltd. in Ahmedabad exemplifies his ability to bridge academic research with practical applications. His work on optimizing gravity die casting processes showcases his skills in applying theoretical knowledge to real-world industrial challenges, bringing innovation to manufacturing techniques.

🔧 FAST-LEARNING AND ADAPTIVE RESEARCHER

Known for his enthusiasm, adaptability, and quick learning, Ankit has proven his capability to manage challenging research work in complex and highly demanding environments. His contributions to the field of micro-machining are marked by his innovative thinking and ability to drive progress in both academic and industrial settings.

NOTABLE PUBLICATION

Title: Integrating intelligent machine vision techniques to advance precision manufacturing: a comprehensive survey in the context of mechatronics and beyond
Authors: D.R. Patel, A.D. Oza, M. Kumar
Journal: International Journal on Interactive Design and Manufacturing
Year: 2024

Title: Development and characterization of eco-friendly extruded green composites using PLA/wood dust fillers
Authors: H.H. Parikh, S. Chokshi, V. Chaudhary, A.D. Oza, C. Prakash
Journal: Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology
Year: 2024

Title: Divacancy defects on the dynamic behaviour of single layer graphene
Authors: M. Manisha, A. Patel, A. Oza, A. Goyal
Journal: AIP Conference Proceedings
Year: 2024

Title: Photoluminescence and antibacterial performance of sol-gel synthesized ZnO nanoparticles
Authors: M.S. Rathore, H. Verma, S.B. Akhani, K. Kaur, A. Oza
Journal: Materials Advances
Year: 2024

Title: A Review of Optimization Methods in Laser and Abrasive Jet Manufacturing Methods
Authors: A. Goyal, N. Gautam, A.D. Oza, R. Choudhary, R.K. Phanden
Journal: Lecture Notes in Mechanical Engineering
Year: 2024

Zisheng Wang – Industrial Big Data – Best Researcher Award

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Zisheng Wang - Industrial Big Data - Best Researcher Award

Tsinghua University - China

AUTHOR PROFILE

GOOGLE SCHOLAR

ORCID

CURRENT ROLE AT TSINGHUA UNIVERSITY 🎓

As of December 2023, Zisheng Wang has been contributing to the field of industrial engineering as a Research Assistant at Tsinghua University in Beijing. His role focuses on advancing research in intelligent maintenance systems, particularly for high-end CNC machine tools, furthering his impact in the academic and industrial sectors.

DOCTORATE FROM HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY 🎓

Zisheng earned his Doctorate in Engineering from the School of Mechanical Science and Engineering at Huazhong University of Science and Technology in Wuhan. From September 2018 to September 2023, he conducted groundbreaking research that laid the foundation for his current work in digital twin systems and fault diagnosis methods.

BACHELOR'S DEGREE FROM NORTHEASTERN UNIVERSITY 🎓

Before his doctoral studies, Zisheng completed his Bachelor's degree in Engineering at the School of Mechanical Engineering and Automation at Northeastern University in Shenyang. His undergraduate education, from October 2014 to June 2018, provided a solid grounding in mechanical engineering principles and automation technologies, which he continues to build upon in his research career.

INNOVATIVE FAULT DIAGNOSIS METHODS FOR CNC MACHINES 🛠️

Zisheng's research is distinguished by the development of a variety of CNC machine tool fault diagnosis methods. These methods address the challenges posed by multi-source sensors, compound faults, and semi-supervised conditions, systematically enhancing state monitoring and maintenance practices. His work aims to revolutionize the maintenance strategies for high-end CNC machine tools, ensuring higher efficiency and reliability in industrial applications.

LEADERSHIP IN CROSS-DOMAIN FAULT IDENTIFICATION 🔍

A key aspect of Zisheng's research is cross-domain fault identification, which is crucial for maintaining the performance and longevity of complex equipment. His methods integrate deep reinforcement learning and time-frequency transformation to effectively identify and address faults across different operational domains, showcasing his expertise in advanced diagnostic technologies.

COMMITMENT TO ADVANCING INDUSTRIAL ENGINEERING 🏭

Through his current role at Tsinghua University and his extensive academic background, Zisheng Wang continues to push the boundaries of industrial engineering. His dedication to developing intelligent maintenance systems for high-end CNC machine tools highlights his commitment to innovation and excellence in the field.

A VISIONARY IN MACHINE TOOL MAINTENANCE 🌟

Zisheng Wang's work exemplifies the fusion of advanced theoretical frameworks with practical engineering applications. His contributions to digital twin systems and intelligent maintenance strategies are paving the way for more resilient and efficient industrial machinery, positioning him as a visionary in the realm of machine tool maintenance and industrial engineering.

NOTABLE PUBLICATION

Multi-source information fusion deep self-attention reinforcement learning framework for multi-label compound fault recognition 2023 (14)

An autonomous recognition framework based on reinforced adversarial open set algorithm for compound fault of mechanical equipment 2024

Measuring compound defect of bearing by wavelet gradient integrated spiking neural network 2023 (1)

Alternative multi-label imitation learning framework monitoring tool wear and bearing fault under different working conditions 2022 (12)

Multi-label fault recognition framework using deep reinforcement learning and curriculum learning mechanism 2022 (11)

Jiliang Luo – Intelligent manufacturing systems – Best Researcher Award

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Jiliang Luo - Intelligent manufacturing systems - Best Researcher Award

College of Information Science and Engineering - China

AUTHOR PROFILE

Scopus

EARLY ACADEMIC PURSUITS

Jiliang Luo embarked on his academic journey with a strong foundation in power systems, culminating in a Ph.D. in Control Science and Engineering from Zhejiang University. His multidisciplinary background provided him with a solid framework for his future contributions to the field of intelligent manufacturing systems.

PROFESSIONAL ENDEAVORS

Throughout his career, Jiliang Luo has held various esteemed positions, including Dean of the School of Information Science and Engineering at Huaqiao University and Director of the Fujian Engineering Research Center for Motor Control and System Optimization and Scheduling. He has also chaired prestigious conferences and served in leadership roles in professional societies, reflecting his dedication to advancing the field.

CONTRIBUTIONS AND RESEARCH FOCUS

Jiliang's research interests span a wide range of topics within the realm of intelligent manufacturing systems, including Digital Twin, Robotics, Integrated Scheduling and Control, Dynamic Systems of Discrete Events, and Petri Net Control Theory. His innovative work in these areas has contributed significantly to the development of advanced control methodologies for complex manufacturing processes.

IMPACT AND INFLUENCE

Jiliang Luo's research has garnered international recognition, as evidenced by numerous grants, honors, and awards. His groundbreaking contributions to the field have not only advanced academic knowledge but also had a profound impact on industry practices, driving innovation and efficiency in manufacturing systems worldwide.

ACADEMIC CITES

Jiliang's publications in top-tier journals and his role as an invited reviewer underscore his influence in the academic community. His research findings have been widely cited, reflecting their significance and relevance in shaping the discourse surrounding intelligent manufacturing systems and related disciplines.

LEGACY AND FUTURE CONTRIBUTIONS

As Jiliang continues his academic journey, he remains committed to pushing the boundaries of knowledge in intelligent manufacturing systems. Through his mentorship of students, prolific research output, and leadership in academic and professional organizations, he seeks to leave a lasting legacy of innovation and excellence in the field.

NOTABLE PUBLICATION

Petri-net-based deep reinforcement learning for real-time scheduling of automated manufacturing systems.  2024

Optimal Opacity-Enforcing Supervisory Control of Discrete Event Systems on Choosing Cost 2024

Active disturbance rejection control for non-minimum phase systems under plant reconstruction 2023 (3)

On disturbance rejection proportional–integral–differential control with model-free adaptation 2023 (1)