Integrated Computational Materials Engineering (ICME) for Steel Industry

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1 Integrated Computational Materials Engineering (ICME) for Steel Industry Dr G Balachandran Head ( R&D) Kalyani Carpenter Special Steels Ltd., Pune Indo-US Workshop on ICME for Integrated Realization of Engineered Materials and Products December 18-21, 2013, Sponsored by Indo-US Science and Technology Forum & Tata Consultancy Services

2 KALYANI CARPENTER SPECIAL STEELS Ltd. Part of KALYANI GROUP Companies Bharat Forge Ltd is the Flag Ship Company Kalyani Steels, Hospet Kalyani Carpenter Special Steels Ltd., Pune Joint Venture with Carpenter Technology Corporation Manufacturing facilities at Pune and Ranjangaon Total Employees 800 Current capacity 240,000 t/ annum Production in the year : Sales 174,587 ton

3 Production Facilities at KCSSL Macro Models : Stoichiometry; Thermodynamics Transport Phenomenon ( Heat, Mass & Momentum)

4 The Steel Industry has to cater to wide variety of market segments - General Engineering industry - Automotive industry - Oil & Gas Industry - Chemical and Petro chemical industry - Mining industry - Tool & Die steel industry - Power generation industry [ Thermal, Wind, Hydro ] - Defence industry - Nuclear Industry - Aerospace industry - Biomedical industry - Construction Grades required are widely varying Needs of Steel for Industry Same grade with different properties for different industries may be required Sometimes customer specific alloy modifications are implemented 4 Industry needs to grasp and react to customer requirement quickly, confidently and with minimum development time

5 Belief in Virtual Tools : Simulation of Ingot Casting at KCSSL Microstructure Simulation predicts fluid flow, temperature profile, solid front movement, central porosity KCSSL is able to optimise mould, hot & casting condition using virtual experimentation 5

6 Belief in Virtual Tools: Hot Rolling Deformation Model at KCSSL Nalawade et al. Int. J. of Mech. Sciences 77 (2013) p Predominant Compressive Stress Micro-structure after 8 th Pass 6 With deformation degree - temperature. strain, stress, grain size, rolling load & torque could be predicted & matched with experiments

7 Belief in Virtual Tools: Microstructure Prediction in Hot Rolling Int. J. of Mech. Sciences 77 (2013) p % Ferrite 16% Ferrite 16% Ferrite 20% Ferrite 18% Ferrite Percentage of phases & Grain size 7 Load and Torque match at most point Phase fraction and grain size match with experimental results

8 Belief in Virtual Tools: Hot Rolling of 316 SS Compared with HSLA Rolling Temp= 1235 o C Cross section (mm) Start Finish HSLA 320x x SS 325x x SS results in, - more passes for same strain penetration - high load/pass [>60%] - higher adiabatic rise in temp - higher surface temp loss 8

9 Is the Virtual Tools always successful? Simulations are used only as a guidance in many instance Industry is guided more by not attempting a prediction that gave poor results, although, accuracy may be lacking in a successful model Predictions can have differences as high as 20 to 30% due to - configuring of problem inaccurately - quality of input data [ data to be refined with experimentation correct data ] - basic validity of the generalized physical models in the in-built software - limitations in computational techniques or software - experimental errors - lack of skill [ Mechanical Vs Metallurgical Engineers ] 9

10 Product Development in Industry Raw Material Cost saving Process Route optimization Bench Marks Yield Improvement Structure property correlations Inventory Reduction Rejection Management Clean Environment and Minimising Effluents Role of ICME in Existing Steel Grades Optimization of processing for a given raw material mix Eliminate unwanted processing, saves raw material, energy, & labour Performance of each unit process assessed. Enable effective capacity utilisation, optimum productivity Fixes targets on energy, labour & productivity Process optimization improves yield (e.g.) Mold designs, deformation scheduling, fish tail reduction, loose structure elimination Enhances accuracy in getting desired properties Locates alternate application for aging material Faster solution to solving internal & customer rejections Choice of process conditions that minimizes pollution and effluents 10

11 Product Development in Industry Choice of composition to meet customer property requirements Decision on processing route Equipment Choice Cost effectiveness Yield Delivery Role of ICME in New Development Grades Faster and accurate choice of composition based on robust alloy design principles to meet properties. Choice of process route & parameters [melting, casting, forging/rolling, heat treatment ] within available infrastructure Enables choice of equipment for processes Ex. AOD/VAD - ESR/ VAR. Enables cost effectiveness of products by optimizing raw material and energy in unit processes Enables yield improvement in the unit processes Fewer & faster development trials ensure faster delivery 11

12 Product Development in Industry Failure Mode & Effect Analysis Industries face internal failures & customer rejections [ especially in new products ] - Quick solutions are needed - Industries combat failures on every day basis Robust modeling or virtual tools are not available to foresee failures. Although theoretical information is available, they are not predictive. Non-metallic Inclusions type, shape, distribution as a function of processing - influence on properties [ undesirable & desirable nature] - Statistical nature of inclusion & fatigue [ Murakami Extreme Value Statistics] Robust damage mechanism based on rules & theories - Porosity formation in casting: Niyama or Yamanaka Criteria - Cracks in deformation : Latham Cockcroft criteria, C-Z Criteria Other Failure modes not yet in the realm of simulation - gas porosity, inter-dendritic shrinkage, porosity distribution - Hydrogen flaking, void crushing in deformation - extent of banding, fracture prediction, FATT 12 Industries will welcome in a big way for such virtual tools on failure

13 Quality Product Cost effective product Timely delivery Enhanced performance & Alerts Ease of subsequent fabrication [Closed die forging, machinability, weldablity etc] Customer is the Focal Point ICME for Customer Needs Choice of alloy design & optimal processing route enables meeting Customer desired properties. Customer expectation & draft of correct specification possible. Every unit processes is optmized - cost effective product - alternate cost effective material assessment.. Hasten process route selection ICME for Customer Delights Additional desirable properties foreseen Customer can cash that in his components Warning against detrimental properties can be an alert. Foreseeing performance of the material at customer end Component life in service conditions. This reduces failure of product at all stages Yield & Quality improvement Optimizes yield & quality improvement at customer end. Reduced rejection 13 Reduced rejections & reliability at customer end, retains customer and enhanced business

14 Conclusion ICME can play a great role for industry. ICME needs to be - universal based on robust and experimentally verified physical models and empirical formulations - at every stage failure modes & effect analysis are to be evaluated - constantly upgraded in every unit at various levels Institutions: Academic, Research & Industry - data mining in industrial data can give excellent models for prediction - user friendly - cost effective - A body to validate & standardize 14

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