Die Life Prediction of Connecting Rod SIFL-175

This work mainly focuses on the life prediction of hot forging dies of connecting rod SIFL-175. This prediction helps the forging industry in estimating the quantity of products forged before reworking or resinking and thereby can supply the forgings to the customer at reasonable lower price and this will also escalating the demand from the customer. The prediction of die life is vital to satisfy demands for lower cost and shorter production preparation times. The prediction helps the company to make an accurate production planning process and can take necessary steps and actions to utilize the maximum quantity of products before die failures

Modeling Of Metal Cutting Process Using Response Surface Methodology

The goal of modern industry is to manufacture low cost, high quality products in short time. Optimum machining parameters are of great concern in manufacturing environments, where economy of machining operation plays a key role in competitiveness in the market. A number of researchers have dealt with the optimization of machining parameters, considering only turning operations and graphical methods to determine the optimum speed, feed and depth of cut. In this work, work pieces machined by Computer Numerical Control machine centre are evaluated according to Response Surface Methodology with an objective function of obtaining good surface finish using single tool operations. Optimum machining parameters resulting from this work are intended for use by Numerical Control machines in order to improve machining efficiencies, improve quality, and reduce rework and scrap.

Multi Objective Optimization of Weld Parameters of Boiler Steel Using Fuzzy Based Desirability Function

The high pressure differential across the wall of pressure vessels is potentially dangerous and has caused many fatal accidents in the history of their development and operation. For this reason the structural integrity of weldments is critical to the performance of pressure vessels. In recent years much research has been conducted to the study of variations in welding parameters and consumables on the mechanical properties of pressure vessel steel weldments to optimize weld integrity and ensure pressure vessels are safe. The quality of weld is a very important working aspect for the manufacturing and construction industries. Because of high quality and reliability, Submerged Arc Welding (SAW) is one of the chief metal joining processes employed in industry. This paper addresses the application of desirability function approach combined with fuzzy logic analysis to optimize the multiple quality characteristics (bead reinforcement, bead width, bead penetration and dilution) of submerged arc welding process parameters of SA 516 Grade 70 steels(boiler steel). Experiments were conducted using Taguchi’s L27 orthogonal array with varying the weld parameters of welding current, arc voltage, welding speed and electrode stickout. By analyzing the response table and response graph of the fuzzy reasoning grade, optimal parameters were obtained. Solutions from this method can be useful for pressure vessel manufacturers and operators to search an optimal solution of welding condition

Investigations on Alkali Treated Natural Fiber Reinforced Polymer Composite by Finite Element Analysis

This work describes the development and characterization of a new set of polymer composites consisting of Palm leaf stalk fiber reinforcement, in polyester resin matrix. The composite slabs are made by normal hand layup process. Composites are made on pure resin using Palm leaf stalk fiber with and without alkali treatment. The newly developed composites are characterized with respect to their physical and mechanical properties. The mechanical properties like Impact strength, hardness value of the specimens were calculated by using Charpy Impact testing machine and standard hardness tester. The Palm leaf stalk fiber composites prepared without alkali treatment of the fiber showed better results. And also the finite element analysis of these Palm leaf stalk fiber composites was done using ANSYS. The analysis compares the impact strength with actual values

Neuro hybrid model to predict weld bead width in submerged arcwelding process

350-355This paper presents development of neuro hybrid model (NHM) to predict weld bead width in submerged arc welding.Experiments were designed using Taguchi’s principles and results were used to develop a multiple regression model. Data setgenerated from Multiple Regression Analysis (MRA) was utilized in ANN model, which was trained with backpropagation algorithm in MATLAB platform and used to develop NHM to predict quality of weld. NHM is flexible and accurate than existing models for a better online monitoring system

ANFIS for prediction of weld bead width in a submerged arc welding process

335-338This paper proposes an intelligent technique, Adaptive Neuro-Fuzzy Inference System (ANFIS), to predict the weld bead width in the submerged arc welding (SAW) process for a given set of welding parameters. Experiments are designed according to Taguchi’s principles and its results are used to develop a multiple regression model. Multiple sets of data from multiple regression analysis are utilized to train the intelligent network. The trained network is used to predict the quality of weld. The proposed ANFIS, developed using MATLAB functions, is flexible, accurate than existing models and it scopes for a better online monitoring system

Sensitivity analysis of submerged arc welding parameters for low alloy steel weldment

425-434It is very tedious to characterize the bead geometry of welding process due to complex existence of parameters that affect weld quality. Researchers have attempted to predict weld bead characteristics including bead size and shape, using various experimental parameters such as welding speed, welding current, arc voltage etc. However, the results of these studies tend to be limited to a specific process parameter range and to certain materials. In this article, the effects of welding process parameters on welding current, arc voltage, welding speed and electrode extension on weld bead geometries and percentage of dilution of submerged arc welding (SAW) on A516 grade 70 carbon steel is presented. Taguchi method which is a systematic optimization method for design and analysis of experiments is introduced to acquire optimum weld conditions. Multiple curvilinear regression models are developed to analyze the effects and interaction of each weld process parameter on the weld bead geometries and dilution. Sensitivity analysis is performed to study detailed characteristics of the weld bead. Results also reveal that the bead reinforcement is almost non-sensitive to the variations in welding current and welding speed