M. Sreenivasa Rao

Bio: M. Sreenivasa Rao is an academic researcher from Jawaharlal Nehru Technological University, Hyderabad. The author has contributed to research in topics: Network model & Surface roughness. The author has an hindex of 2, co-authored 2 publications receiving 167 citations.

Buffer stocks in kanban controlled (traditional) unsaturated multi-stage production system

Abstract: In recent years, most of the manufacturers are resorting to Kanban mechanism to control Work in Process inventories (WIP) in their production activity. Typical production system faces various sources of uncertainties, viz, external demand uncertainty, processing time variations and yield uncertainty etc and Kanban systems are not an exception to this. Often safety stocks are deployed to reduce the ill effects of uncertainties. In literature, there exist insights on the issues relevant to deployment of safety stocks in a Kanban controlled production system however under the assumption that external demand for the finished product is very large also known as saturated condition. In the present global competition, most products have a finite demand rate and stochastic in nature known as unsaturated condition. The present paper deals with Kanban controlled multi-stage unsaturated production (traditional) system with multiple sources of uncertainties in external demand and processing times. For the considered system, Continuous Time Markov Chain (CTMC) model is developed and expressions for steady state performance measures, viz, Probability that a customer demand is satisfied instantaneously on his arrival (known as Customer Service Level), average inventory and average throughput are derived. The expressions are subjected to numerical experimentation and to gain insights for evaluating the performance of the system. The analytical results are validated with the results obtained from stochastic discrete event simulation model at 95% Confidence Level (C.F). Further certain guidelines on the deployment of safety stocks in the considered system are presented.

Design and Analysis of Quad Copter Chassis Using Shape Optimization Technique

Abstract: Abstract: Unmanned aerial vehicles (UAVs) are replacing many traditional methods ranging from simple play toys to critical defense operations. UAVs or drones that are the little flying machines are used in space, defense, food delivery, pest sprays for agriculture, consumer goods delivery, land-surveillance and the list goes on. However, the physics behind it demands a lesser weight for drone to fly high as lesser the weight, lesser is the power required to operate. In all the components that make up a drone, is the frame that is most important structure which holds and bears everything at place. So the material used for the drone frame plays a very crucial role for it should have a lesser mass but sufficient strength. In this work, frame is modeled taking into consideration, its sturdiness and stress analysis is conducted using Autodesk Fusion 360 software and compared for different materials of frames from plastics to metals and the design is shape optimized to meet the objective

Optimization of the EDM parameters on machining Ti–6Al–4V with multiple quality characteristics

Abstract: In this paper, parameter optimization of the electrical discharge machining process to Ti–6Al–4V alloy considering multiple performance characteristics using the Taguchi method and grey relational analysis is reported. Performance characteristics including the electrode wear ratio, material removal rate and surface roughness are chosen to evaluate the machining effects. The process parameters selected in this study are discharge current, open voltage, pulse duration and duty factor. Experiments based on the appropriate orthogonal array are conducted first. The normalised experimental results of the performance characteristics are then introduced to calculate the coefficient and grades according to grey relational analysis. The optimised process parameters simultaneously leading to a lower electrode wear ratio, higher material removal rate and better surface roughness are then verified through a confirmation experiment. The validation experiments show an improved electrode wear ratio of 15%, material removal rate of 12% and surface roughness of 19% when the Taguchi method and grey relational analysis are used.

Optimization of modern machining processes using advanced optimization techniques: a review

Abstract: Thorough literature review of various modern machining processes is presented in this paper. The main focus is kept on the optimization aspects of various parameters of the modern machining processes and hence only such research works are included in this work in which the use of advanced optimization techniques were involved. The review period considered is from the year 2006 to 2012. Various modern machining processes considered in this work are electric discharge machining, abrasive jet machining, ultrasonic machining, electrochemical machining, laser beam machining, micro-machining, nano-finishing and various hybrid and modified versions of these processes. The review work on such a large scale was not attempted earlier by considering many processes at a time, and hence, this review work may become the ready information at one place and it may be very useful to the subsequent researchers to decide their direction of research.

Intelligent process modeling and optimization of die-sinking electric discharge machining

Abstract: This paper reports an intelligent approach for process modeling and optimization of electric discharge machining (EDM). Physics based process modeling using finite element method (FEM) has been integrated with the soft computing techniques like artificial neural networks (ANN) and genetic algorithm (GA) to improve prediction accuracy of the model with less dependency on the experimental data. A two-dimensional axi-symmetric numerical (FEM) model of single spark EDM process has been developed based on more realistic assumptions such as Gaussian distribution of heat flux, time and energy dependent spark radius, etc. to predict the shape of crater, material removal rate (MRR) and tool wear rate (TWR). The model is validated using the reported analytical and experimental results. A comprehensive ANN based process model is proposed to establish relation between input process conditions (current, discharge voltage, duty cycle and discharge duration) and the process responses (crater size, MRR and TWR) .The ANN model was trained, tested and tuned by using the data generated from the numerical (FEM) model. It was found to accurately predict EDM process responses for chosen process conditions. The developed ANN process model was used in conjunction with the evolutionary non-dominated sorting genetic algorithm II (NSGA-II) to select optimal process parameters for roughing and finishing operations of EDM. Experimental studies were carried out to verify the process performance for the optimum machining conditions suggested by our approach. The proposed integrated (FEM-ANN-GA) approach was found efficient and robust as the suggested optimum process parameters were found to give the expected optimum performance of the EDM process.

Fuzzy logic for modeling machining process: a review

Abstract: The application of artificial intelligence (AI) techniques in modeling of machining process has been investigated by many researchers. Fuzzy logic (FL) as a well-known AI technique is effectively used in modeling of machining processes such as to predict the surface roughness and to control the cutting force in various machining processes. This paper is started with the introduction to definition of FL and machining process, and their relation. This paper then presents five types of analysis conducted on FL techniques used in machining process. FL was considered for prediction, selection, monitoring, control and optimization of machining process. Literature showed that milling contributed the highest number of machining operation that was modeled using FL. In terms of machining performance, surface roughness was mostly studied with FL model. In terms of fuzzy components, center of gravity method was mostly used to perform defuzzification, and triangular was mostly considered to perform membership function. The reviews extend the analysis on the abilities, limitations and effectual modifications of FL in modeling based on the comments from previous works that conduct experiment using FL in the modeling and review by few authors. The analysis leads the author to conclude that FL is the most popular AI techniques used in modeling of machining process.