Tata Consultancy Services Limited

Purushottham Gautham Basavarsu

Beena Rai

Sreedhar Sannareddy Reddy

Umesh Singh

Amit Gangadhar Salvi

Yogesh Tambe

Shankar Balajirao Kausley

Deepak Shyamsunder Jain

Prasenjit Das

Santosh Vasant Daware

Soumyadipta Maiti

Sapankumar Hiteshchandra Shah

Chetan Premkumar Malhotra

Dharmendr Kumar

Amit Bhowmik

Shally Gupta

Shashank Mishra

Trinath Gaduparthi

Bilal Muhammed

Avadhut Mohanrao Sardeshmukh

Sunil Sharma

Pramod Ramdas Zagade

Pallavi Bandi

Yagnik Pravinchandra Kalariya

Rinu Chako

Suryanaman Chaube

Himanshu Nirgudkar

Vishnudas Raveendran

Garima Jain

Srimannarayana Pusuluri

Dharmendra Kumar

This disclosure relates generally to recommending tool configurations in machining. The machining tool configuration selection involves the selection of several tool specification parameters concerning the material, geometry and composition of the machining tool. The state-of-the-art methods uses a rule and knowledge-based system to select tool configuration, however these methods do not recommend tool configurations which satisfy customer requirement. Embodiments of the present disclosure uses a hierarchical model which is trained to predict acceptable tool specification parameters for a given requirement by learning the patterns from past tool selection data. Further a probabilistic approach is used to predict the top set of recommendations of tool configurations with a probability score for each prediction. The disclosed method is used for recommending tool configurations in a cylindrical grinding wheel process.

Method and system for recommending tool configurations in machining

Conventional approaches of physical experiments for the effects of cure kinetics in composites materials may lack in capturing lower length scale effects at bulk level. The computational state of the art approaches has not focused on the issue of scale bridging between multiple length scales for manufacturing effects in composites. This limits its usability for specific materials or situations. Embodiments of the present disclosure provide systems and methods that implement a multiscale analysis for determining residual stress and deformation profiles in molded parts comprising composite material. More specifically, present disclosure implements the multiscale analysis wherein a thermal chemical analysis and thermal mechanical analysis are linked to achieve two-way coupling for curing effects at each node/point of molded parts having composite material to provide flexibility and versatility in terms of exploring multiple material combinations without major modification in the approach.

Simulation techniques for determining deformation profiles and residual stresses in molded parts having composite materials

The present disclosure addresses the technical problem of prediction of a preheat refractory temperature profile of a ladle furnace. Operational temperature of the ladle furnace, stability of sensors and placement make sensors not feasible. Computational Fluid Dynamics (CFD) simulations require large computation time and cannot be used for runtime applications in plants. The method and system of the present disclosure uses CFD modeling to carry out parametric study to generate data which is further processed to train an Artificial Neural Network (ANN) model that serves as a prediction model for predicting the preheat refractory temperature profile for at least a portion of the side refractory and at least a portion of the bottom refractory layer separately for which a new set of input data is obtained. The trained prediction model of the present disclosure provides a quick runtime prediction in plants.

Prediction of preheat refractory temperature profile of a ladle furnace

The disclosure generally relates to methods and systems for generating synthetic microstructure images of a material with desired features. Conventional techniques that make use of unsupervised deep generative models has no control on the generated microstructure images with specific, desired set of features. The present disclosure generates the synthetic microstructure images of the material with desired feature, by using a variational autoencoder defined with a style loss function. In the first step, the variational autoencoder is trained to learn latent representation of microstructure image of the material. In the second step, some of the dimensions of learned latent representation is interpreted as physically significant features. In the third and last step, the latent representation required for getting the desired features is appropriately sampled based on the interpretation to generate the synthetic microstructure images of the material with desired features.

Methods and systems for generating synthetic microstructure images of material with desired features

This disclosure relates generally to a system and method to estimate an operational risk associated with one or more failures in at least one unit of a process plant. There is a continuous stream of operational data of several variables such as temperature, pressure, etc. Detections are defined in terms of acceptable/unacceptable ranges of parameters over a finite period and operating load of the unit. Often, these predefined parameters must be within a specified range based on operating condition of the process plant and when the measured parameters go beyond, a failure is detected. A risk priority number is estimated from number of occurrences of failure mode, average percentage change from dynamic limits with severity and degree of correlation with detectability from operational data and dynamic limits. Herein, operational risk associated with failure modes can be calculated and updated from time to time automatically from the stream of operational data.

System and method for calculating risk associated with failures in process plants

Currently solutions for early detection of failures in manufacturing utilize predefined threshold levels of the process variables associated with equipment in manufacturing unit/industry plants. The pre-defined threshold and levels thereof are compared with the real values obtained from the manufacturing unit to check behavior of process variables (also referred as 'process parameters') and thus are prone to error. The present disclosure provides systems and method for predicting early warning of operating mode of equipment operating in industry plants which is based on transforming conditions on process parameters into conditions on corresponding fuzzy indices based on their thresholds. The fuzzy indices (concordance index, discordance index) of individual conditions are combined into a composite fuzzy index (composite index or degree of credibility) that describes the failure scenario in the process parameter space. A fuzzy logic-based detection is useful for detecting a failure mode early and providing alerts to operators for necessary action.

Predicting early warnings of an operating mode of equipment in industry plants

This disclosure relates generally to method and system for designing the formulated products. Conventional techniques for designing the formulated products, meeting final functional properties, are limited. Further, understanding user requirements and active incorporation of the user requirements during design phase is quite challenging. The present disclosure herein provides method and system that solve the technical problem of extracting the functional requirement by establishing continuous conversation with the user. An optimal prediction function for each functional requirement is determined by using a plurality of prediction models. An optimization technique along with an objective function is employed to determine optimized solutions comprising list of ingredients, possible concentration level of each ingredient, the process parameters, and the operating parameters for obtaining the desired formulation based on the user requirement.

Method and system for designing formulated products

This disclosure relates to a model driven framework for realizing domain specific search system for unstructured text. The framework has two parts: information extraction system for defining various models such as a meta model, an instance model, an extraction model and mention model and a generic domain agnostic search system that works by interpreting the models specified via the information extraction system. The components of the search system are completely domain agnostic with no hard coded knowledge of the domain. The search system interprets the domain models specified in terms of the meta model to impart domain specificity to the search engine. In this sense, the framework is domain agnostic and it can be tailored for a new domain by just specifying domain related information in terms of the meta model. The model driven approach obviates need for re-coding the search system for any new domain of interest.

Methods and systems for performing a model driven domain specific search

Engineered structures include materials in certain arrangement and proportions to make a composite that provides desired properties to a structure. The mechanical and physical properties of the materials are measured through expensive and time consuming mechanical testing, and structural design is carried out using these properties thus warranting more time and cost spent on physical testing. Embodiments of the present disclosure provide multi-scale modeling and simulation techniques (MSMST) for design of composite materials with desired macro-scale properties wherein the (lower) MSMST are interconnected and each can pass on corresponding desired outputs to higher length-scales, which in turn evaluate macro-scale physical and mechanical properties/either to scale up the structure simulation, or to fine tune computational materials parameters thereby predicting behaviour of the structure based on determined properties of composite materials of the structure.

Systems and methods for determining properties of composite materials for predicting behaviour of structures

A system and method for obtaining topologically optimized structure in additive manufacturing. A finite element mesh of a standard shape geometry encompassing desired structure to be optimized with a design objective and orthotropic properties of material arising out of a selected material and selected additive manufacturing process to be used for manufacturing. An iterative topology optimization is carried out wherein the process includes simulation of loading requirements, design constraints and the boundary condition. The performance evaluation process uses a finite element analysis framework to obtain the factor of safety between two consecutive iterations based on the anisotropic properties of the material. The process will achieve a minimal factor of safety and a best suitable build orientation of the design.

System and method for design of additively manufactured products

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Pune, India

Purushottham Gautham Basavarsu