Shunmugam M S

Flat pattern development of sheet metal components is a prerequisite for sheet metal fabrication and it also facilitates development of process plans. Commercial softwares for flat pattern development are too expensive and they often require a 3-D model of the component. In fact, most of the component drawings exist as orthographic projections in the majority of industries. A system that can automatically generate a flat pattern using the orthographic projections as the input can be very useful. Such a system is proposed in this paper and it first extracts the features automatically from the orthographic projections and then generates the 3-D wireframe model of the central plane of the component. Using a variant of attributed adjacency graph, the data from the 3-D wireframe model are used for automatic flat pattern development incorporating bending allowances. © 2002 Elsevier Science Ltd. All rights reserved.

High-aspect-ratio high-quality microholes are required in turbine blades to improve cooling performance. These cooling holes are drilled by pulsed laser and hence dimensional as well as geometrical tolerances like circularity and cylindricity are important. The measurement of geometrical features of the microholes is a very challenging task without destroying the components. In the present work, the microholes are produced on Ti6Al4V alloy by ultra-short pulse laser. The geometrical features of microholes are then captured using a non-destructive technique, namely computed tomography. CT-scanned 3D data is directly used for geometrical analysis using open-source software, GOM Inspect. Since algorithms used in the GOM Inspect are proprietary in nature, the extracted coordinate data are also analyzed using the computational methods developed by the authors based on least squares technique. The dimension, circularity, and cylindricity of microholes are compared with the results obtained from GOM Inspect software and a close match is found.

A fillet curve is provided at the root of the spur gear tooth, as stresses are high in this portion, The fillet curve may be a trochoid or an arc of suitable size as specified by designer. The fillet stress is influenced by the fillet geometry as well as the number of teeth, modules, and the pressure angle of the gear. Because the relationship is nonlinear and complex, an artificial neural network and a backpropagation algorithm, are used in the present work to predict the fillet stresses. Training data are obtained from finite element simulations that are greatly reduced using Taguchi's design of experiments. Each simulation takes around 30 min. The 4-5-1 network and a sigmoid activation function are chosen. TRAINLM function is used for training the network with a learning rate parameter of 0.01 and a momentum constant of 0.8. The neural network is able to predict the fillet stresses in 0.03 s with reasonable accuracy for spur gears having 25-125 teeth, a 1-5 mm module, a 0.05-0.45 mm fillet radius, and a 15°-25° pressure angle. Copyright © 2008 Cambridge University Press.

Mass production of holes having good surface finish and geometrical accuracy necessitates the need for precision in assembly. This cannot be satisfied by the conventional twist drills and reaming is carried out as a secondary operation. The reamers are developed for enlarging and finishing of holes accurately and stock removal is less. A survey of medium size industries revealed 40% of machining time is devoted to hole-making and as high as 70% for prismatic component. The nature of surface layer produced has an influence on mechanical properties and service performance of components. The condition of the manufactured surface will be explained in terms of surface integrity. Surface integrity is taken to represent the enhanced condition of a surface produced in machining and other surface generating operations. The nature of surface produced by reaming will be highlighted. The results of surface topography and surface and sub surface metallurgical studies will be elaborated upon and discussed.

Wire-cut EDM is a highly complex process, which is characterized by non-linear behavior. Due to very large number of input parameters, a Neural Network Approach is followed in this paper to model this process. A large number of experiments have been carried out to train the network. The model is also tested using additional experimental data. It is found that neural network models of wire-cut EDM process provide results with required accuracy. The important input parameters have been identified and reported in this paper.

Manufactured features generally deviate in size, form and relations with respect to other features from the features desired by the designer. Designers specify certain limits for these deviations that depend on functional requirements. The specification of different form errors is dealt with by the International Organization for Standardization (ISO). ISO also specifies that an ideal geometric feature must be established from the actual measurements such that the maximum deviation between it and the actual feature concerned is the least possible value. In practice, the least squares (LS) method is sometimes used for establishing the ideal feature, as this method is based on sound mathematical principles. However, the least squares procedure does not yield a minimum value. Therefore some attempts have been made to arrive at a form error based on the minimum deviation (MD) principles. A stray peak or valley on the actual feature introduces considerable variation in the results obtained by the minimum deviation method. This paper suggests a new approach based on the minimum average deviation (MAD). In this method, the ideal feature is established by using a search technique for different geometric features such as straight lines, circles, planes, cylinders and spheres. Using simple numerical examples, the values obtained by this approach are compared with the values obtained by the least squares and minimum deviation methods. © 1987.

Intelligent control of any manufacturing process requires an appropriate modeling of the process. Wire-cut EDM, being a highly complex process with non-linear behavior, is modeled by fuzzy logic approach in this paper. The knowledge base for the fuzzy model has been developed using the experimental results. Membership functions are defined for each parameter and a total of 10 input and 2 output parameters are considered to build the model. The results of the fuzzy model are compared with experimental values and a statistical analysis is presented.

Gear shaping cutters are provided with top-relief and rake angles to facilitate effective cutting. Due to this, the profile representing the cutting edge (effective profile) is altered. This paper outlines a method of obtaining the effective profile and its deviations from the theoretical involute form required. This paper also deals with a method that modifies the pressure angle, while maintaining zero deviation at the reference circle, so that the effective profile closer to the theoretical form is obtained after grinding the rake face. The Golden section optimization method is used to get the optimum value of the modified pressure angle. The effect of regrinding on the profile error of the cutting edge is also brought out. The method suggested in this paper will be useful to the designers and manufacturers of gear shaping cutters. © 1988, All rights reserved.

An automated planning system extracts data from design models and processes it efficiently for transfer to manufacturing activity. Researchers have used face adjacency graphs and volume decomposition approaches which make the feature recognition complex and give rise to multiple interpretations. The present work recognizes the features in prismatic parts considering Attributed Adjacency Matrix (AAM) for the faces of delta volume that lie on rawstock faces. Conceptually, intermediate shape of the workpiece is treated as rawstock for the next stage and tool approach direction is used to recognize minimum, yet practically feasible, set of feature interpretations. Edge-features like fillets/undercuts and rounded/chamfer edges are also recognized using a new concept of Attributed Connectivity Matrix (ACM). In the first module, STEP AP-203 format of a model is taken as the geometric data input. Datum information is extracted from Geometric Dimension and Tolerance (GD&T) data. The second module uses features and datum information to arrive at setup planning and operation sequencing on the basis of different criteria and priority rules. Copyright © 2013.