Reasearch Scholars

Prof. A.S. Maheshwari

Research Work:

Topic : Optimization of Burnishing Parameters to Improve Surface Integrity of Aero Metals

Aerospace industry has traditionally always looking for introduction of new production technologies and materials systems. The key driving forces for newer production technologies and material development are strength to weight ratio of materials, performance and reliability improvement. Component performance is determined by the mechanical properties of material such as strength, damage tolerance, and stiffness along with physical and chemical properties, such as corrosion resistance at ambient temperature. Roller and ball burnishing have been used for years in the aerospace and automotive industry. Most of the burnishing tools are made for processing inside diameters of holes or outside diameters of shafts. High-speed steel or carbide ball or rollers are put into contact with the surface with a slight interference and then rolled over the surface. The resulting plastic deformation leaves residual compressive stresses with an added benefit of smoothing out the surface of the component. A mirror finish can be achieved in some cases. Cold working increases the surface hardness of the material. This can improve the wear resistance of the surface. Burnishing can be very cost effective and, in some cases, can be used to finish a component instead of a secondary operation like grinding, honing or lapping.Comparison between the results on Al 6351 and Titanium alloy is done. In both case depth of penetration and number of passes are the significant factors to improve surface finish of the workpiece. In case of the surface hardness number of passes, plays an important role. It is important to note that other burnishing parameters must be kept under control in order to achieve improvement in surface hardness and surface finish.
Research center: BapuraoDeshmukh College of Engineering, Wardha.
Affiliated to RashtrasantTukadojiMaharaj Nagpur University, Nagpur.
Current Status:
a. Completed course work.
b. Completed Literature Review.
c. Mathematical model was developed.
d. Publications are in process

Prof. (Dr.) M. D. Pasarkar

Research Work:

Topic :
Generation of Shadow less surfaces and deformation of CAD surface models with haptic Interaction based on shape control functions.

CAD models especially surface models are basically not easy to design and edit with 2- D based interfaces due to their three dimensional nature. Achieving greater control over the shape largely depends on number of control points. Designers deform the surface to achieve the accuracy by manipulating the control points in real time environment by pushing, pulling, adding or removing them. The exactness of the shape depends upon number of control points. Many available techniques are numerically not that much efficient for actually achieving the required shape in early product development stage. Deformation of surface model by manipulating the control points in real time environment to achieve the accuracy is thus a need.
An attempt is made to provide a market ready solution for manipulation and generation of complex CAD surfaces, which can be utilized by any automobile industry at low cost and thereby resulting in saving manpower, time and cost.
A novel tool ‘CUSUMA’ (Curve Surface Manipulation) for a computer-assisted design of various vehicles and method for generation of complex surfaces for various industrial applications like aerodynamic shape design in airplanes, ships, cars, scooters, mopeds is developed and tested for various applications to strengthen all available CAD softwares. It can be used for creating intricate surfaces by changing position of control points easily. Before actually modeling, the designer can select any number of control points on the canvas and complete the shape of the desired object.
The proposed Profile Curve Management Technique (PCMT) helps designer in adding, removing and manipulating any number of control points to achieve desired accuracy of shape in early product design stage. The designer can select any number of control points before actually drawing it and finalize the shape and size of the desired object in early product design stage.
Research Center: B.N. College of Engineering, Pusad, affiliated to Sant Gadge Baba Amravati University, Amravati.


Prof. (Dr.) G. G. Waghmare

Research work:

      “Single and Multiobjective Design Optimization Using Teaching-Learning-Based Optimization Algorithm”


Optimization problems are of high importance both for the industrial world as well as for the scientific world. Design optimization consists of certain goals (objective functions), a search space (feasible solutions) and a search process (optimization method). Real life engineering designs often have more than one conflicting objective functions thus requiring a multiobjective approach. In real design problems the number of design variables can be very large and their influence on the objective function to be optimized can be very complicated with a nonlinear character. The objective function may have many local optima whereas the designer is interested in the global optimum. Such problems cannot be handled by classical methods that only compute local optima. So there remains a need for efficient and effective optimization methods for mechanical design problems.    Continuous research is being conducted in this field and nature-inspired heuristic optimization methods are proving to be better than deterministic methods and thus are widely used. The genetic algorithm (GA), ant colony optimization (ACO) algorithm, particle swarm optimization (PSO) algorithm; differential evolution (DE) algorithm, artificial bee colony (ABC) algorithm, shuffled frog leaping (SFL) algorithm, harmony search (HS) algorithm, etc. have been applied to many engineering optimization problems and proven effective for solving some specific kinds of problems. However, the parameters setting of these algorithms are a serious problem which influences the performance of the optimization problem. The GA requires the crossover probability, mutation probability, and selection operator; ACO algorithm requires exponent parameters, pheromone evaporation rate and the reward factor; PSO algorithm requires learning factors, the variation of inertia weight and the maximum value of velocity; DE algorithm requires crossover probability and differential weight; ABC algorithm requires number of bees (scout, onlooker and employed) and the limit; SFL algorithm requires number of memeplexes and iteration per memeplexes; HS algorithm requires harmony memory consideration rate, pitch adjusting rate, and number of improvisations. Finding the optimum values of the specific parameters of these algorithms is an optimization problem itself. Proper tuning of the algorithm-specific parameters is very crucial factor which affect the performance of the above mentioned algorithms. The improper tuning of algorithm-specific parameters either increases the computational efforts or yields the local optimum solution. In addition to the tuning of algorithm-specific parameters the common control parameters like population size, number of generations (and elite size considered) need to be tuned which further enhances the effort. Keeping the above points in view, Rao et al. (2011) proposed an optimization algorithm known as “Teaching-Leaning-Based Optimization (TLBO)” algorithm which doesn’t require algorithm-specific parameters thus making the implementation of TLBO algorithm simpler. The TLBO algorithm requires only the common control parameters like population size and number of generations for its working. If elitism is considered then the elite size becomes another common control parameter of the algorithm. TLBO is population based algorithm which simulates the teaching-learning process of the class room. While the elitist TLBO is a modified version of the TLBO algorithm in which during every generation the worst solution are replaced by elite solution.

In present research work, the performance of a TLBO and ETLBO algorithms are evaluated against the other optimization algorithms over a set of complex composite functions and multiobjective unconstrained and constrained benchmark functions and design benchmark problems. These benchmark functions and problems are taken from the research literature. The performance of the TLBO and ETLBO algorithms are further evaluated for the design optimization of (i) robot manipulator (ii) robot gripper (iii) four bar linkage (iv) gear train (v) plate fin heat sink (vi) smooth flat plate solar air heater and (vii) shell and tube condenser. The computational results obtained have shown that the TLBO and ETLBO algorithms are better or competitive to other optimization algorithms considered by the previous researchers in all the above cases. Therefore, it may be stated that the TLBO and ETLBO algorithms are potential algorithms and have great potential for solving the design optimization problems of engineering.

 Research Center: S. V. National Institute of Technology, Surat, Gujarat (India)



Prof.(Dr.) Lalit K. Toke

  1. Research Topic

Study of Green Supply Chain Management in Manufacturing Industries.

The central purpose of this study is to establish the measures and approaches for implementing GSCM practices in manufacturing industries in response to critical success factors. The analytic hierarchy process (AHP), which is applied to determine the relative importance of different approached, is extremely crucial, since the result can be used by mangers for implementing and adopting their own GSCM practices. This study is limited to some sectors of Indian manufacturing industries only; nevertheless it can be extended to meet the need of the special categories of the industries after identification of the key critical success factor. The results demonstrate that greening the supply chain, significantly lead to better competitiveness and economic performance of the company, gains in terms of less or minimal environmentally waste, reduced costs, compliance with regulation, reduced pollution, improved resource utilization and enhancement in economic performance. The developed model is simple to understand and will definitely help to manufacturing industries in their journey of GSCM.

  1. Research Centre – S.G.S. Institute of Technology and Science, Indore.

University – Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal.

(University of Technology M.P.)
Prof. A.D.Lokhande

1. Research work

Manufacturing industry has extremely devoted the resources and energy in the earth and polluted the environment. The 4-R rule is the important appraise to obtain the goal. 4-R rule consists of Reduce, Reuse, Recycle and Remanufacture. Remanufacturing is a process of disassembly of products. The components are cleaned, repaired and replaced then reassembled it. This research instrument used to evaluate performance measure factors for establishing remanufacturing industry which supported by various manufacturing industries. Critical success factors and performance measure factors were considered as beneficial to success remanufacturing industry. Indian organizations have mostly engaged in manufacturing and different services. The developed model is simple to understand and will definitely help to remanufacturing industry. The performance measure factors were giving the awareness for successful achievement of establishing remanufacturing industry. The concepts of critical success factors and performance measurement factors were supporting growth and accomplishment for remanufacturing industry. The research is focused on remanufacturing industry in India. Such a study has enormous potential to contribute and improve the needs of remanufacturing in Indian organization.

Research Center

Yeshwantrao Chavan College of Engineering, Nagpur affiliated to Rastrasant Tukadoji Maharaj University of Nagpur,

Current status:

Thesis submitted.

Prof. Niteen L. Bhirud

Research work :

1. Topic:

Heat Transfer Analysis and Optimization of Process Parameters for Milling Operations. Using cutting fluids is a traditional approach for reducing the temperature and friction at the cutting zone. They are considered as hazardous substances for the workers’ health and environment. Extending governmental and environmental regulations have limited the usage and increased the costs associated with cutting fluids.The best approach to reduce the usage of cutting fluids is dry cutting. However, it fails to produce desired tool life and surface finish in some cases due to the excessive generation of heat at the cutting zone. In order to realize the dry machining, improved cutting tool materials and further studies on the cutting parameters is inevitable.The proposed research is about optimization of the process parameters in order to get minimum temperature at machining zone with better surface finish and prediction of the temperature at cutting zone.

2. Research center: Bapurao Deshmukh College of Engineering, Wardha. Affiliated to Rashtrasant

Tukadoji Maharaj Nagpur University, Nagpur.

3. Current Status:

a. Completed course work.

b. Completed Literature Review.

c. Experimentation is in progress.


Prof. A. S. Dube

Research work :

1. Topic:

Modeling and Performance evaluation of Green Supply Chain  practices for Indian industry

In 2010 the world’s greenhouse gas emissions was the highest ever in history. The implications this will have is still unknown, but research done leaves no doubt that the climate changes we are facing today is a consequence of the increased amounts of gases that circulates in our atmosphere due to increased human activity following the industrialization.

Green supply chain management is defined as “the process of using environmentally friendly input and transforming these inputs into outputs that can be reclaimed and re-used at the end of their life cycle thus, creating a sustainable supply chain. Sustainable development means “Development that meets the needs of the present without compromising the ability of future generations” Sustainability covers three aspects: economic, environmental and social responsibility.Outcome of GSCM implementation are as below:

  • Dell saves over $20mn annually as a result of supply chain and packaging improvements. In fact, this market leader achieved its goal of becoming carbon neutral by 2008.
  • Texas Instruments saves $8mn each year by reducing its transit packaging budget for its semiconductor business through source reduction, recycling, and use of reusable packaging systems4 (20% annual savings).
  • Pepsi-Cola saved $44mn by switching from corrugated to reusable plastic shipping containers for one liter and 20-ounce bottles, conserving 196mn pounds of corrugated material
  • Dow Corning saved $2.3mn by using reconditioned steel drums in 1995 and conserved 7.8mn pounds of steel.
  • Main object of this research is to develop framework for GSCM practices and its effect on operation performance, financial performance and customer satisfaction in Indian Industry.Also find out the drivers and barriers for GSCM practices in Indian Industry.This research can be validated   by using multiple case study approach.

2. Research center: Bapurao Deshmukh College of Engineering, Wardha. Affiliated to Rashtrasant Tukadoji Maharaj Nagpur University, Nagpur.

3. Ph.D. Thesis submitted.