FEM-based prediction of workpiece transient temperature distribution and deformations during milling

In high-speed dry milling of thin-walled parts, the cutter-workpiece temperature rises asymptotically with cutting speed, causing excessive cutter tooth wear and workpiece thermal expansion, which in turn reduces the cutter life and produces dimensional and geometrical variabilities in the machined part. Therefore, a basic understanding of the thermal aspect of machining and the effecting parameters is essential for achieving better part quality with improved productivity. This paper presents a transient milling simulation model to assist manufacturing engineers in gaining in-depth understanding of the thermomechanical aspects of machining and their influence on resulted part quality. Based on the finite-element method approach, the model can predict transient temperature distributions and resulted elastic-plastic deformations induced during the milling of 2.5D prismatic parts comprising features like slots, steps, pockets, etc. The advantages of the proposed model over previous works are that it (1) performs feature-based machining simulation considering transient thermomechanical loading conditions; (2) allows modeling the effects of coolant on convective heat transfer rate; and (3) considers the nonlinear behavior of the workpiece due to its changing geometry, inelastic material properties, and flexible fixture-workpiece contacts. The prediction accuracy of the model was validated with experimental results obtained during the course of the research work. A good agreement between the numerical and experimental results was found for different test cases with varying part geometries and machining conditions

Mill-cut: a neural network system for the prediction of thermo-mechanical loads induced in end-milling operations

This paper presents the design and implementation issues of a generalized system called mill-cut, developed for the prediction of cutting forces and temperature in end-milling operations. Based on an ANN approach, mill-cut predicts all the three components of cutting forces and average shear plane temperature for a given set of machining parameters broadly categorized into three groups viz. (i) cutting tool geometrical parameters (ii) cutting parameters and (iii) workpiece material properties. In the present work, for representing overall machining condition, 15 machining parameters having major impact on the cutting forces and cutting temperature were chosen. The feed-forward back-propagated ANN architecture has been incorporated, which was initially trained with analytical data before incorporating it as part of an integrated system. Results obtained from the proposed model show good agreement with the experimental/numerical (FEM based) results available in the literature

Finite element method based machining simulation environment for analyzing part errors induced during milling of thin-walled components

The rigid body motion of the workpieces and their elastic-plastic deformations induced during high speed milling of thin-walled parts are the main root causes of part geometrical and dimensional variabilities; these are governed mainly from the choice of process plan parameters such as fixture layout design, operation sequence, selected tool path strategies and the values of cutting variables. Therefore, it becomes necessary to judge the validity of a given process plan before going into actual machining. This paper presents an overview of a comprehensive finite element method (FEM) based milling process plan verification model and associated tools, which by considering the effects of fixturing, operation sequence, tool path and cutting parameters simulates the milling process in a transient 3D virtual environment and predicts the part thin wall deflections and elastic-plastic deformations during machining. The advantages of the proposed model over previous works are: (i) Performs a computationally efficient transient thermo-mechanical coupled field milling simulation of complex prismatic parts comprising any combination of machining features like steps, slots, pockets, nested features, etc., using a feature based milling simulation approach; (ii) Predicts the workpiece non-linear behavior during machining due to its changing geometry, inelastic material properties and fixture-workpiece flexible contacts; (iii) Allows the modelling of the effects of initial residual stresses (residing inside the raw stock) on part deformations; (iv) Incorporates an integrated analytical machining load (cutting force components and average shear plane temperature) model; and (v) Provides a seamless interface to import an automatic programming tool file (APT file) generated by CAM packages like CATIA V5. The prediction accuracy of the model was validated experimentally and the obtained numerical and experimental results were found in good agreement. (C) 2007 Elsevier Ltd. All rights reserved

An intelligent system for predicting HPDC process variables in interactive environment

The selection of optimal parameters in high pressure die casting process (HPDC) has been long recognized as a complex nonlinear problem due to the involvement of a large number of interconnected process variables, each influencing the flow behavior of molten metal inside the die cavity and thus part quality and productivity. In the present work a physical model called Neural Network based Casting Process model (NN-CastPro) has been developed for real time estimation of optimal HPDC process parameters. By submitting a set of four process parameters (having major impact on productivity and part quality) namely, (i) inlet melt temperature, (ii) mold initial temperature, (iii) inlet first phase velocity and (iv) inlet second phase velocity, as input to the NN-CastPro, values for filling time, solidification time and porosity can be obtained simultaneously. The proposed artificial neural network (ANN) model was trained using data generated by ProCast (an FEM-based flow simulation software). The obtained prediction accuracy and enhanced functional capabilities of NN-CastPro show its improved performance over other models available in the literature. (C) 2007 Elsevier B.V. All rights reserved

Optimal selection of cutting parameters in multi-tool milling operations using a genetic algorithm

In the milling of large monolithic structural components for aircraft, 70-80% of the total cut volume is removed using high-speed roughing operations. In order to achieve the economic objective (i.e. optimal part quality in minimal machining time) of this process, it is necessary to determine the optimal cutting conditions while respecting the multiple constraints (functional and technological) imposed by the machine, the tool and the part geometry. This work presents a physical model called GA-MPO (genetic algorithm based milling parameter optimisation system) for the prediction of the optimal cutting parameters (namely, axial depth of cut (a(p)), radial immersion (a(e)), feed rate (f(t)) and spindle speed (n)) in the multi-tool milling of prismatic parts. By submitting a preliminary milling process plan (i.e. CL data file) generated by CAM (computer-aided manufacturing) software, the developed system provides an optimal combination of process parameters (for each machining feature), respecting the machine-tool-part functional/technological constraints. The obtained prediction accuracy and enhanced functional capabilities of the developed system demonstrate its improved performance over other models available in the literature

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Not AvailableKarnal bunt disease caused by the fungus Tilletia indica Mitra is a serious concern due to strict quarantines affecting international trade of wheat. We announce here the first draft assembly of two monosporidial lines, PSWKBGH-1 and -2, of this fungus,having approximate sizes of 37.46 and 37.21 Mbp, respectivelyNot Availabl

Genome and transcriptome based comparative analysis of Tilletia indica to decipher the causal genes for pathogenicity of Karnal bunt in wheat

Abstract Tilletia indica Mitra causes Karnal bunt (KB) in wheat by pathogenic dikaryophase. The present study is the first to provide the draft genomes of the dikaryon (PSWKBGD-3) and its two monosporidial lines (PSWKBGH-1 and 2) using Illumina and PacBio reads, their annotation and the comparative analyses among the three genomes by extracting polymorphic SSR markers. The trancriptome from infected wheat grains of the susceptible wheat cultivar WL711 at 24 h, 48h, and 7d after inoculation of PSWKBGH-1, 2 and PSWKBGD-3 were also isolated. Further, two transcriptome analyses were performed utilizing T. indica transcriptome to extract dikaryon genes responsible for pathogenesis, and wheat transcriptome to extract wheat genes affected by dikaryon involved in plant-pathogen interaction during progression of KB in wheat. A total of 54, 529, and 87 genes at 24hai, 48hai, and 7dai, respectively were upregulated in dikaryon stage while 21, 35, and 134 genes of T. indica at 24hai, 48hai, and 7dai, respectively, were activated only in dikaryon stage. While, a total of 23, 17, and 52 wheat genes at 24hai, 48hai, and 7dai, respectively were upregulated due to the presence of dikaryon stage only. The results obtained during this study have been compiled in a web resource called TiGeR ( http://backlin.cabgrid.res.in/tiger/ ), which is the first genomic resource for T. indica cataloguing genes, genomic and polymorphic SSRs of the three T. indica lines, wheat and T. indica DEGs as well as wheat genes affected by T. indica dikaryon along with the pathogenecity related proteins of T. indica dikaryon during incidence of KB at different time points. The present study would be helpful to understand the role of dikaryon in plant-pathogen interaction during progression of KB, which would be helpful to manage KB in wheat, and to develop KB-resistant wheat varieties