Jawaharlal Nehru Technological University Kakinada
Jawaharlal Nehru Technological University Kakinada
Kakinada, Andhra Pradesh, INDIA - 533003
www.jntuk.edu.in
Faculty Development Program on Cutting Edge Technology
(FDP4CET-19)
Embedded Files
Advanced Materials and Manufacturing
Advanced Materials and Manufacturing
23rd - 27th June 2025
Venue: Vignan's Lara Institute of Tech & Science (A), Vadlamudi, Guntur
Organized by
Organized by
Faculty Development Centre, JNTUK
Faculty Development Centre, JNTUK
Jawaharlal Nehru Technological University Kakinada (JNTUK) is established in the year 2008 vide ACT NO. 30 OF 2008 by the State of ANDHRA PRADESH. The University grew out of the College of Engineering Vizagapatnam founded by the Government of the composite Madras State in the year 1946. Spread over a sprawling campus of 100 acres in the port city of Kakinada, the college became a constituent of JNTU, Hyderabad in 1972. Subject to the trifurcation of the JNTU Hyderabad, it was notified as JNTUK Kakinada by the act of legislature in 2008.
The jurisdiction of JNTU Kakinada extends over the districts of East Godavari, Kakinada, Konaseema, West Godavari, Eluru, NTR, Krishna, Guntur, Palnadu, Bapatla and Prakasam. The University has 159 affiliated colleges under the jurisdiction of these 11 districts. The University serves approximately 2.3 lakh students across more than 50 bachelor programs, over 120 master programs, and various Ph.D. programs.
In Association with
In Association with
University College of Engineering Kakinada (A)
Department of Mechanical Engineering
UCEK (A), JNTUK, Kakinada.
Vignan's Lara Institute of Tech & Science (A)
Department of Mechanical Engineering
VLITS(A), Vadlamudi, Guntur
University College of Engineering Kakinada (Autonomous), J N T U Kakinada was originally ‘The College of Engineering, Vizagpatnam’ at the time of its establishment in the year 1946 by Government of the Composite Madras State. It is now a sprawling campus of 110 acres, green with mango trees in the fast developing Port city of Kakinada. Kakinada has a rich political literacy and cultural heritage passed on through generations. This college became a constituent College of the Jawaharlal Nehru Technological University, Hyderabad w.e.f 02-10-1972. The College has become autonomous in the year 2001. On 20th August, 2008 it became a constituent College for the newly established JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY KAKINADA.
About Department
Department of Mechanical Engineering is established from the inception (1946) of the institute to meet the requirements of the mechanical industry and the society /discipline after the consultation with various stakeholders. The department started with an initial intake of 40 students in UG Program in ME and the intake is enhanced to 50 in the year 1976. In 1972, the department started a PG Program in “Machine Design (MD)” with an initial intake of 12 and the intake is enhanced to 18 students in the year 2001 again 18 to 25 in the year 2008. The department also started another PG Program in “Computer Aided Design and Computer Aided Manufacturing (CAD/CAM)” in the year 2001 with an initial intake of 18 and the intake is enhanced to 25 students in the year 2008.
Vignan's Lara Institute of Technology & Science, Autonomous, was established in 2007 with the objective of delivering quality technical education that meets international standards. Located in Vadlamudi, within the Guntur District, the institute is approved by the All India Council for Technical Education, New Delhi, and is affiliated with Jawaharlal Nehru Technological University, Kakinada. The institute boasts accreditation from the NBA for five undergraduate programs (CSE, IT, ECE, EEE, and Mechanical) and has achieved an NAAC A+ grade. Additionally, the UGC has granted the institute autonomous status starting from the 2023-24 academic year.
Set against the backdrop of the green and tranquil rural landscape of Vadlamudi, the college is conveniently located on the Guntur-Tenali highway, approximately 14 km from Guntur and 11 km from Tenali. The campus serves as a serene retreat, encompassing extensive grounds adorned with lush greenery, making it an ideal choice for aspiring engineering students. Furthermore, the college prides itself on its well-qualified and experienced faculty, comprising Ph.D. holders and M.Tech graduates, supported by a dedicated technical staff.
About Department
The department, established in 2010, aims to provide high-quality engineering education for undergraduate and postgraduate students. With a highly qualified faculty and modern laboratory facilities, it focuses on research in composites and alternative fuels, supporting key industries like automotive, aerospace, and marine. The faculty and staff work to ensure students develop teamwork and leadership skills, gain practical experience, and participate in internships. The department's core values foster personal development, preparing students to compete in a global environment.
About the Training Program
About the Training Program
The Faculty Development Program (FDP) on Advanced Materials and Manufacturing aims to empower faculty with modern tools and techniques integrating material science, manufacturing, and data-driven technologies. The program will cover the fundamentals of Machine Learning (ML), Artificial Neural Networks (ANNs), and their applications in manufacturing processes. Participants will explore Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for applications like defect detection, process monitoring, and predictive maintenance. Various optimization algorithms for solving maximization and minimization problems will be introduced. The FDP will also focus on prediction models for estimating outputs based on experimental data and research input. Python programming basics for implementing optimization and prediction techniques will be taught through hands-on sessions. Process parameter optimization in Fused Deposition Modeling (FDM) will be highlighted to improve part quality and efficiency. Advanced topics like modeling and analysis of composites and their mechanical behavior will also be included. Deep learning applications in machining process optimization and performance prediction will be explored. The program is designed to bridge the gap between experimental research and intelligent algorithms, helping participants enhance their teaching, research, and industrial collaboration capabilities.
Key highlights of the FDP include:
Fundamentals of Machine Learning (ML) and Artificial Neural Networks (ANNs)
Applications of CNNs and RNNs in manufacturing and process optimization
Overview of optimization algorithms for engineering design problems
Prediction techniques using experimental and research data
Python programming for implementing ML, optimization, and prediction models
Process parameter optimization in Fused Deposition Modeling (FDM)
Modeling and analysis of composite materials for mechanical applications
Application of deep learning in machining process optimization
Techniques for data refinement to improve model accuracy
Expert lectures, hands-on sessions, and interactive discussions with domain specialists
Course Learning Objectives
Course Learning Objectives
CLO-1: To introduce the fundamentals of Machine Learning, Artificial Neural Networks, CNNs, and RNNs, and their practical applications in manufacturing processes.
CLO-2: To understand and apply various optimization and prediction algorithms for solving engineering problems using experimental data.
CLO-3: To perform output prediction from research input data and develop Python-based solutions for optimization and data-driven modeling.
CLO-4: To optimize process parameters in FDM 3D printing and understand the modeling and analysis of blow-molded components.
CLO-5: To explore deep learning methods for machining process optimization and refine experimental data for integration into DL models.
Course Outcomes
Course Outcomes
CO 1: Demonstrate an in-depth understanding of Machine Learning, Artificial Neural Networks, Convolutional and Recurrent Neural Networks with relevance to manufacturing systems.
CO 2: Select and apply appropriate optimization and prediction algorithms for solving maximization and minimization problems based on experimental data.
CO 3: Perform analytical computations for output estimation from research inputs and implement basic optimization and prediction routines using Python.
CO 4: Optimize process parameters in FDM technology and conduct modeling and performance evaluation of blow-molded polymer components.
CO 5: Apply deep learning techniques to machining process optimization and refine experimental datasets for enhanced predictive modeling accuracy.
Topic 1: Understanding the Basics of Machine Learning
Topic 2: Artificial Neural Networks and Their Applications in Manufacturing
Topic 3: Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) and Their Applications
Topic 4: Optimization Algorithm Types for Maximization and Minimization Problems
Topic 5: Prediction Algorithm Types and Value Estimation Based on Experimental Data
Topic 6: Output Calculations for Given Input Research Data
Topic 7: Python Programming Fundamentals for Optimization and Prediction
Topic 8: Process Parameter Optimization in Fused Deposition Modeling (FDM) Technology
Topic 9: Modelling and Analysis of Blow Moulded Components and Composites
Topic 10: Deep Learning in the Optimization of the Machining Process
Topic 11: Prediction and Refinement of Experimental Data for Use in Deep Learning Algorithms
Topic 12: Hands-on Practice Sessions for Practical Implementation
Distinguished Experts
Distinguished Experts
Expert 1: Dr. J. Anuradha, Professor, VIT Vellore
Expert 2: Dr. A. Gopala Krishna, Professor, JNTUK
Expert 3: Dr. N. Nalini, Sr. Asst. Professor, Vellore
Expert 4: Dr. D. Nagaraju, Professor, VIT Vellore
Expert 5: Dr. N. B. Prakash Tiruveedula, Asst. Professor, VFSTR
Expert 6: Dr. K. Durga Rao, Asst. Professor, VFSTR
Patrons
Patrons
Chief Patron
Dr. C.S.R.K. Prasad
Hon'ble Vice-Chancellor, JNTUK
Dr. Lavu Rathaiah
Chairman, Vignan’s Group of Institutions
Patron
Dr. K.V. Ramana
Rector, JNTUK
Sri Lavu Srikrishnadevarayalu, Vice-Chairman, Vignan’s Group of Institutions
Co-Patron
Dr. V. Ravindranath
Registrar, JNTUK
Advisory Committee
Advisory Committee
Chief Chairperson
Dr. P. Subba Rao, Director, FDC, JNTUK
Chairperson
Dr. K. Phaneendra Kumar, Principal, VLITS (A)
Co- Chairperson - Board of Studies, M.E.D, UCEK(A), JNTUK
Prof. A. Gopala Krishna , JNTUK, Kakinada.
Co- Chairperson - Board of Studies, M.E.D, VLITS
Dr. P. Bhaskara Rao, HoD, Dept. of ME, VLITS (A)
Organising committee
All faculty of M.E.D, UCEK(A), JNTUK
Dr. G. Nageswara Rao, Professor
Dr. P. B. G. S. N. Murthy, Professor
Dr. B. Jagan Mohan Rao, Professor
Dr. M. Mohammed Asif, Assoc. Professor
Dr. Balijepalli Rama Krishna, Assoc. Professor
Dr. Y. Siva Sankara Rao, Assoc. Professor
Mr. E. Ramu, Asst. Professor
Mr. N.M.K. Sarath Kumar, Asst. Professor
Mr. Manohar Velaga, Asst. Professor
Mr. B.K. Pavan Kumar, Asst. Professor
Ms. V. Mercy, Asst. Professor
Mr. R. Sairam, Asst. Professor
Coordinators
Coordinators
Coordinator - UCEK (A)
Dr. M. Madusudhan Prasad, Assistant Professor
Email: msprasad@jntucek.ac.in; Mob: 9966915354
Registration Fee
For JNTUK affiliated Participants : NO REGISTRATION FEE
For non-JNTUK affiliated Participants : ₹ 500/- for PG students
₹1,000/- for Research Scholars
₹2,500/- for Teaching Faculty
₹5,000/- for Industry Personal
Page updated
Report abuse