S.S. Pande

Automatic recognition of features from freeform surface CAD models

This paper reports the design and implementation of a system for automatic recognition of features from freeform surface CAD models of sheet metal parts represented in STL format. The developed methodology has three major steps viz. STL model preprocessing, Region segmentation and automated Feature recognition. The input CAD model is preprocessed to get a healed and topology enriched STL model. A new hybrid region segmentation algorithm based on both edge- and region-based approaches has been developed to segment the preprocessed STL model into meaningful regions. Geometrical properties of facets, edges and vertices such as gauss and mean curvature at vertices, orientations of facet normals, shape structure of triangles, dihedral edge angle (angle between facets), etc. have been computed to identify and classify the regions. Feature on a freeform surface is defined as a set of connected meaningful regions having a particular geometry and topology which has some significance in design and manufacturing. Feature recognition rules have been formulated for recognizing a variety of protrusion and depression features such as holes, bends, darts, beads, louvres, dimples, dents, ridges/channels (blind and through) etc. occurring on automotive sheet metal panels. The developed system has been extensively tested with various industrial sheet metal parts and is found to be robust and consistent. The features data can be post processed and linked to various downstream CAD/CAM applications like automated process planning, sheet metal tool design, refinement of FEM meshes and product redesign.

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

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.

Investigations on reducing burr formation in drilling

Abstract This paper reports the experimental investigations on reducing burr formation while drilling through-holes in metals. Experiments have been planned based on response surface methodology (RSM) technique. Mathematical models correlating response parameters (burr sizes) to the process parameters, e.g. feed, hole size and workpiece hardness have been obtained. Optimal process conditions to minimize the sizes of burr at the entry and exit of holes have been identified. In addition an attachment has been designed and developed to provide continuous modification of feed during drilling. It is reported that the optimal process conditions and the use of attachment can cause significant reductions in the sizes of burr.

An approach to recognize interacting features from B-Rep CAD models of prismatic machined parts using a hybrid (graph and rule based) technique

This paper presents a new hybrid (graph+rule based) approach for recognizing the interacting features from B-Rep CAD models of prismatic machined parts. The developed algorithm considers variable topology features and handles both adjacent and volumetric feature interactions to provide a single interpretation for the latter. The input CAD part model in B-Rep format is preprocessed to create the adjacency graphs for faces and features of associated topological entities and compute their attributes. The developed FR system initially recognizes all varieties of the simple and stepped holes with flat and conical bottoms from the feature graphs. A new concept of Base Explicit Feature Graphs and No-base Explicit Feature Graphs has been proposed which essentially delineates between features having planar base face like pockets, blind slots, etc. and those without planar base faces like passages, 3D features, conical bottom features, etc. Based on the structure of the explicit feature graphs, geometric reasoning rules are formulated to recognize the interacting feature types. Extracted data has been post-processed to compute the feature attributes and their parent-child relationships which are written into a STEP like native feature file format. The FR system was extensively tested with several standard benchmark components and was found to be robust and consistent. The extracted feature file can be used for integration with various downstream applications like CAPP.

Intelligent system for extraction of product data from CADD models

Abstract This paper reports a system for automatic extraction of geometric and non-geometric part information from ‘engineering drawings’ created using computer-aided design and drafting (CADD) tools. A heuristic search is employed to interpret the characteristic attributes of dimension sets that denote linear, diametrical, radial and angular dimensions. Textual callouts are processed using natural language processing (NLP) techniques to interpret information related to part/feature function and related processes. The part information so recognized is represented using object oriented paradigm (OOP) suitable for linking to the downline CAD/CAM activities of the product cycle. The system, thus provides an effective alternative for design automation using CADD models.

Intelligent layout planning for rapid prototyping

Significant savings in cost and time can be achieved in rapid prototyping (RP) by manufacturing multiple parts in a single setup to achieve efficient machine volume utilization. This paper reports the design and implementation of a system for the optimal layout planning of 3D parts for a RP process. A genetic algorithm (GA) based search strategy has been used to arrive at a good packing layout for a chosen set of parts and RP process. A two stage approach has been proposed to initially short-list acceptable orientations for each part followed by the search for a layout plan which optimizes in terms of final product quality and build time. The GA uses a hybrid objective function comprising of the weighted measures like part build height, staircase effect, volume and area-of-contact of support structures. In essence it captures the key metrics of efficiency and goodness of packing for RP. The final layout plan is produced in the form of a composite part CAD model which can be directly exported to a RP machine for manufacturing. Design methodology of the system has been presented with some representative case studies.

Intelligent tool path correction for improving profile accuracy in CNC turning

This paper presents the development of an intelligent predictive tool for improving the accuracy of parts produced on CNC turning centers. Actual dimensions and shape (form) produced on components during machining differ from the nominal dimensions commanded in the CNC program. These dimensional and form errors on parts depend on a variety of factors such as tool/work deflections, machine tool accuracy, machining conditions etc. These errors, if predicted, can be used to correct the ideal CNC code and thus improve the part accuracy in CNC machining. In the present work artificial neural networks are used to capture the complex relationship between the errors on the component and the input process conditions. Significant improvement in part accuracy has been obtained using the corrected CNC code during machining.

Investigations on vibratory burnishing process

The present paper reports experimental investigations on vibratory ball burnishing process. Experiments were carried out to study the influence of various process parameters such as burnishing speed, feed, ball force, frequency and amplitude of vibration on the surface finish and microhardness of surface layers produced by vibratory burnishing process. Experiments were planned based on response surface methodology (RSM) technique. Two mathematical models correlating process parameters with response parameters (viz., surface roughness Ra and microhardness) were obtained. These models can be used in selecting optimum process parameters for obtaining desired controlled surface characteristics.

The role of deburring in manufacturing: A state-of-the-art survey

Abstract Even with the advancement of technology, problems of burr removal are still encountered by manufacturers and researchers. The proper control of burrs in manufacture and the selection of suitable deburring techniques can reduce manufacturing costs. Knowledge of burrs, their control in manufacture, methods of removal and the need for automation in deburring are thus important. Precision deburring calls for selection of proper techniques in feed-back also. A survey of these fields is reported in this paper.

WebROBOT: internet based robotic assembly planning system

WebROBOT is an Internet based assembly planning system for intelligent task level programming of assembly robots. Developed as the client-server architecture, WebROBOT typically enables the client to graphically synthesise and model the world, specify assembly tasks, enable automated task/motion planning, generate robot control program and transfer it to the remote robot site through server. Clients can communicate with the robot administrator through chat to set up and execute the robot program in the real assembly world. The design and implementation issues of WebROBOT are presented at length in this paper.