Rahul Swarup Sharma

Modeling of manufacturing processes with ANNs for intelligent manufacturing

Modern manufacturing often caters to rapidly changing product specifications determined by the continuously increasing productivity, flexibility and quality demands. Metal forming and machining are two important manufacturing processes in present day manufacturing. Automatic selection of tools and accessories in these processes heavily relies on forming force/cutting force estimation. Complex relationships exist between process parameters and these forces. In the present work, the applicability and relative effectiveness of Artificial Neural Network based models has been investigated for rapid estimation of these, invoking the function approximation capabilities of the ANN models. The results obtained are found to correlate well with the finite element simulation data in cases of metal forming, and experimental data in cases of metal cutting. This work has considerable implications in selection of the tools and on-line monitoring of tool wear. The actual forming and cutting forces can be compared with predicted ones to signal the onset of tool wear, and thus prevent damage to the tool and work piece during the course of manufacturing.

Finite Element Simulation of Twist Extrusion on ECAPed Al6061 Specimen

Recently, the application of Severe Plastic Deformations (SPD) processes to produce Ultra‐Fine Grained materials with improved mechanical properties are gaining prominence. A number of researchers are working on Equal Channel Angular Pressing (ECAP) and Twist Extrusion (TE) independently. In this work an effort is made to study a hybrid TE process where the Al6061 specimen is subjected to ECAP and subsequently TE in the same die setup. Finite Element (FE) modeling of the above hybrid process is attempted in FORGE 2007 environment. The simulation results clearly depict the change in equivalent strain in the entire specimen on account of this process. A comparison is made between FE results of one pass obtained using ECAP, TE and hybrid TE. Also the variation of strain with change in friction conditions and channel angle are studied using current FE model. The hybrid TE opens up new possibilities for investigating and forming UFG materials.

Technology enabled learning of metal forming processes for engineering graduates using virtual simulation lab

Metal forming process is a widely used manufacturing process mainly for its economy, dimensional precision, and improved mechanical properties of the formed product. Real-time laboratory experiments are key to mechanical engineering course of metal forming. Lack of available capital in small engineering institutions constraints huge investments over machinery and necessary technologies. An interactive metal forming simulation lab is developed (i) having web-based global access to everyone (ii) with user friendly interface to move seamlessly and find topics (iii) enhancing learning by CAD simulations of metal forming processes (iv) to explore what-if situations experimenting over interactive simulation benches and (v) automatically scored e-quizzes for testing knowledge. This work helps amateur mechanical engineers and mechanical engineering undergraduate students of second, third, fourth, and fifth semesters, respectively, in learning and understanding of metal forming processes and concepts, allowing analysis and visualization of temperature changes, microstructure change, etc. This can be accessed through web address http://msvs-dei.vlabs.ac.in/ and contains over 200 modules. Each module is supported by adequate theory to reduce time and effort needed to understand different metal forming setups, various process parameters, materials, and equipments involved in the processes and provides interactive learning environment.

Evolution of Strain in Multipass Hybrid Equal Channel Angular Pressing Using 3D Finite Element Analysis

Severe Plastic Deformation (SPD) is well known process for producing nanostructured material from coarse material. Present paper is an effort to integrate the two well known SPD techniques Equal Channel Angular Pressing (ECAP) and Twist Extrusion (TE) to develop a new Hybrid ECAP (HECAP) technique that can produce nanostructured material more economically. In this technique, the specimen is subjected to both ECAP and TE in the same die setup. Finite Element (FE) modeling of metal forming processes has become an important tool for designing feasible production processes, because of its unique capability to describe the complex geometry and boundary conditions. FE Modeling of the above hybrid process is attempted in FORGE. The simulation results clearly depict the change in equivalent strain in the entire specimen on account of this process upto four passes. A comparison is made between FE results of simple ECAP and HECAP upto four passes. The study indicated that equivalent strain is much higher in case of HECAP in comparison to ECAP for same friction conditions. Also, the study is extended to analyse the effect of friction, channel angle and forging force on equivalent strain using current FE model. HECAP opens new possibilities for improving equivalent strain in same number of passes as compared to ECAP. This study is expected to contribute in forming UFG materials that are useful for automobile and aerospace industries.

Mechanical Properties of Al6061 Processed by Equal Channel Angular Pressing

The severe plastic deformation (SPD) is an effective approach for producing bulk nanostructured materials. The Equal Channel Angular Pressing (ECAP) is the most efficient SPD solution for achieving ultra-fined grained (UFG) material as billet undergoes severe and large deformation. The process parameters of ECAP (Channel Angle, angle of curvature, friction, number of passes, etc) influences major impact on the properties. In present work, the ECAP process is performed by pressing a specimen through a die consisting of two intersecting channels meeting at an angle φ and outer corner meeting at an angle ψ. Experiments with a circular specimen of Al6061 were conducted to investigate the changes in mechanical properties upto 2 passes. 3-D finite element simulations were also performed using metal forming software FORGE to study the evolution of strain in the specimen during the ECAP process. Simulation results were investigated by comparing them with experimental measured data in terms of load variations. The present work clearly shows that ECAP caused accentuated increase in Al6061 hardness and tensile strength during multi-pass processing. This study is beneficial in developing high quality, high strength products in manufacturing industry on account of its ability to change microstructure of materials.

Optimization of hot extrusion using single objective neuro stochastic search technique

This paper presents a new single-objective neuro-stochastic search technique (SONSST) for the economic load estimation problem in hot extrusion which is often used to produce long straight metal products of constant cross-sections such as bars, solid and hollow sections, tubes, wires and strips from materials that cannot be formed by cold extrusion. The shape of the dies and the temperature developed during extrusion and the velocity of the dies significantly influence forging force at which the process is to be carried out. In order to understand the complex relationship between the material and process variables, a few finite element models are developed and simulated in the FORGE2 environment. These finite element simulations are used to train a neural network (NN) model. Later the same model is incorporated along with a genetic algorithm (GA) and simulated annealing (SA) to form SONSST. It incorporates a genetic crossover operator BLX-/spl alpha/ and a problem specific mutation operator incorporating a local search heuristic: to provide it a better search capability. Extensive simulations have been carried out considering various aspects and the results are validated with those of the existing finite element method in the literature. These results indicate that the new SONSST heuristic converges to better solutions rapidly. SONSST is a truly single-objective technique as it provides the values of various process parameters for optimizing single objective (extrusion load), in a single run and thus assists in achieving energy and material saving, quality improvement and in the development of sound extruded parts.

Study of Multi Pass Equal Channel Angular Pressing Using 3D Finite Element Analysis

Equal Channel Angular Pressing (ECAP) has emerged as most prominent Severe Plastic Deformation (SPD) technique used to produce an ultrafine grained (UFG) structure in metals in order to improve their mechanical and physical properties. In this work Finite Element modeling of ECAP is attempted in FORGE 2007 environment. Four passes of the ECAP process of 10mm square shaped AL 6061 billet were carried out for routes A, BA and C for different channel angles and values of coefficient of friction to investigate their influence on the billet. The models were developed assuming a range of friction conditions at the billet‐die contact region considering eight distinct friction coefficient (μ) values of 0.0, 0.10, 0.15, 0.20, 0.25, 0.30, 0.35 and 0.40, respectively. The simulations are carried out using three distinct situations of die channel angles (Φ), 90°, 105°, and 120° respectively. Route ‘BA’ emerged as a better method among the three routes studied and 90° channel angle appeared to be optimal in terms of p...

AGILE MODELING AND OPTIMIZATION OF END MILLING

The rising demand for precision and quality in manufacturing necessitates that vast amounts of manufacturing knowledge be incorporated in manufacturing systems. Surface finish in end milling depends upon a number of variables such as cutting speed, feed rate, spindle speed, radial depth of cut, etc. The relative effect of these variables on surface roughness and machining time is quite considerable. A complex relationship exists between these process parameters and hence there is a need to develop models which can capture this complex interrelationship and enable fast computation of the average surface roughness and machining time based on process parameters. Neuro Fuzzy (NF) modeling has gained prominence recently on account of its fast reaction times, improved ease of operation and flexibility to respond to change in process parameters. In the present work, initially a Neuro Fuzzy Model is trained with experimental results of end milling. Subsequently, a generic approach is developed for optimization of end milling where the applicability and effectiveness of Neuro Fuzzy Model for function approximation is used to rapidly estimate average surface roughness and machining time in an integrated framework of Hybrid Stochastic Search Technique (HSST) to form a Neuro Fuzzy Hybrid Stochastic Search Technique (NFHSST). The results indicate that the NFHSST heuristic converges to better solutions rapidly as it provides the values of various process parameters for optimizing the objectives in a single run. Thus, NFHSST assists in the improvement of quality by developing multiple sound parts in an agile manner.