Lukasz Madej

Digital Material Representation as an efficient tool for strain inhomogeneities analysis at the micro scale level

The summary of recent research towards development of a tool for detailed microstructure modelling is presented within the paper. The main focus is put on micro scale behaviour, where advantages of digital material representation can be taken into account. Digital Material Representation allows modelling of microstructures along with features such as crystallographic orientation, grain boundaries or phase boundaries represented in an explicit manner. Incorporation of these digital microstructures into the numerical simulation methods provides the possibility to improve the quality of numerical results. The developed method can be used to design specifically dedicated microstructures, which meet very strict requirements. The clear motivation and importance of the work is presented in the first part of the paper followed by a short description of the developed approaches for creation of the digital microstructures. Two approaches are considered that provide an exact and statistical representation of the real microstructure. The main focus is put on the application of image processing and cellular automata techniques. Afterwards, obtained digital microstructures are used as input data for the finite element analysis of the micro scale compression test. Examples of applications during multiscale simulation are also presented in the paper.

Optimization of Cellular Automata Model for the Heating of Dual-Phase Steel by Genetic Algorithm and Genetic Programming

This study considers a common metallurgical problem associated with the phase transformation of steel during heating where austenite grain tends to grow in size with time and results in poor mechanical properties in the final stages. This investigation was performed using a Cellular Automata model for dual-phase steel developed in house. Data-driven metamodels for a biobjective optimization problem involving minimizing average austenite grain size along with the maximizing of time of heating were constructed using Evolutionary Neural Network (EvoNN) and Biobjective Genetic Programming (BioGP). The input variables selected for this task were (i) heating rate, (ii) pearlite percentage, (iii) nucleation density of austenite, and (iv) the finish temperature of austenite formation. The analyses of the results led to the fact that heating rate is the most influencing factor and it needs to be large during transformation to obtain a refined microstructure. The comparison of Pareto front between EvoNN and BioGP reveals a better performance of the latter. Limited experimental confirmation was also carried out.

System for design of the manufacturing process of connecting parts for automotive industry

The proposition of complex hybrid system, dedicated to modelling of life cycle of materials and optimization of their in use properties, is presented in the paper. The approach is based on the conventional optimization algorithms, FE simulations of industrial production process and knowledge base, containing both theoretical and practical data in form of rules, facts and equations. Simulation and optimization of the manufacturing of the connecting part used in automotive industry was selected for the purposes of this work. The particular emphasis is put on control of selected in use properties of products by proper design of technological parameters for consecutive stages of the production chain. The concept of the life cycle modelling used in the proposed system, as well as results obtained from simulations, are also presented in the paper.

Optimised recrystallisation model using multiobjective evolutionary and genetic algorithms and k-optimality approach

The meta-models are constructed for static recrystallisation of dual phase steels using evolutionary neural nets (EvoNN). Four mutually conflicting objectives—(i) overall kinetics, (ii) grain size, (iii) the amount of strain and (iv) the precipitate volume fraction—are optimised simultaneously using an emerging k-optimal approach incorporated in the EvoNN, using a predator–prey genetic algorithm. The first objective involved minimisation of error with respect to experimental observation. The grain size and the amount of strain were minimised, whereas the precipitate volume fraction was maximised. The aim is to control the recrystallisation process in order to achieve desired material properties of dual phase steel during the final stages of heat treatment.

Fracture modeling in dual-phase steel grades based on the random cellular automata finite element approach

The development of a parallel version of the fracture model dedicated for multi-phase materials based on a combination of the finite element model and random cellular automata approach is the overall goal of this study. Dual-phase (DP) steel, commonly used in the automotive industry, is selected as a case study for the present investigation. Firstly, various fracture modes that can occur during deformation in DP steel grade microstructures are presented from an experimental point of view. To consider explicitly microstructure features that play a significant role during initiation and subsequent failure propagation, the digital material representation concept is used. Then, details of the developed random cellular automata model, fully embedded within the finite element framework, are discussed. The cellular automata space definition, internal variables, state variables and transition rules replicating investigated fracture modes are presented in detail and discussed. The concept of data transfer and parallelization based on the Message Passing Interface methodology in such an innovative hybrid numerical model is also clearly presented. The final section of the paper is devoted to examples of obtained results highlighting model predictive capabilities.

Hybrid System Supporting Flexible Design of Flat Rolling Production Processes in Collaborative Environment

The paper is devoted to advanced production processes design, based on numerical simulations of material behaviour under complex loading conditions. The computer system proposed in this work facilitates creation of the sophisticated flat rolling facilities composed of different subesequent stages e.g. heating, roughing and finishing mills, cooling, cutting, descaling. Each stage is treated as a separated module with its own features and methods that implement its functionality. However, the most demanding part of proposed system lies in reliable simulation of connection between these separated modules. To deal with this the highly fexible numerical solutions are required. Creation of this approach is the main goal of the work and is described in details including examples obtained results. Disscusion on accurate material models taking into account dynamic recrystallization or grain growth as well as on application of the optimization procedures inorder to obtain desired final properties is also presented in the paper.

Numerical and Experimental Investigation of the Innovatory Incremental-Forming Process Dedicated to the Aerospace Industry

The main goal of this work is development of the incremental-forming (IF) process for manufacturing integral elements applicable to the aerospace industry. A description of the proposed incremental-forming concept based on division of large die into a series of small anvils pressed into the material by a moving roll is presented within this article. A unique laboratory device has been developed to investigate the effects of process parameters on the material flow and the press loads. Additionally, a developed numerical model of this process with specific boundary conditions is also presented and validated to prove its predictive capabilities. However, main attention is placed on development of the process window. Thus, detailed investigation of the process parameters that can influence material behavior during plastic deformation, namely, roll size and roll frequency, is presented. Proper understanding of the material flow to improve the IF process, as well as press prototype, and to increase its technological readiness is the goal of this article. Results in the form of, e.g., strain distribution or recorded forging loads are presented and discussed.

Knowledge Based Optimization of the Manufacturing Processes Supported by Numerical Simulations of Production Chain

The system dedicated to optimization of the manufacturing processes, used in metal forming branches, is presented in the paper. The proposed approach is based on the conventional optimization methods supported by Good Practice Guides (GPG), which represent rich engineering knowledge and are usually applied in industrial practice. Each step of optimization algorithm includes numerical simulations of analyzed manufacturing process, while the goal function is calculated regarding the ‘in use’ material properties allowing to minimize the number of expensive, time consuming industrial tests. It also leads to the higher efficiency of the production chain under consideration. These features result in the system which is flexible enough to face the challenges of the market and rapid development of customers requirements. Moreover, this combination guarantees that the whole presented solution is innovative and unique in the field of manufacturing support systems. Application of the developed software to optimize the flat rolling process with respect to uniformity of final material properties was selected as an example. The obtained results regarding temperature distribution in the slab are presented in the paper. Possibilities of further improvement of the system are finally drawn.

Complex Modelling Platform based on Digital Material Representation

Proposition of innovative software platform dedicated to modelling of metallurgical processes is presented in the paper. Developed approach is based on the idea of material representation in the form of digital data sets describing various material properties in different length scales. The platform is equipped with additional software modules dedicated to support data gathering, microstructure image analysis, mesh generation and performance of multiscale simulations. The latter module, based on Cellular Automata — Finite Element (CAFE) method, contains two algorithms related to modelling of microstructural phenomena occuring in material during deformation under varying conditions i.e. micro shear and shear bands analysis and recrystallization modelling. The complex approach described in this paper allows not only knowledge based prediction of detailed material properties after thermomechanical metallurgical processes but it also gives possibility to modell entire life cycle of considered material. Thus, it facilitates the investigation of properties of final products and their development by strong quality improvement. Moreover, the platform allows to limit costs of manufacturing by reduction of many expensive industrial trials and their replacement by pure virtual research. Some of the results obtained from application of selected software modules are presented in the paper.

Multi scale modeling of phenomena caused by changes of the deformation path in materials forming

Application of the CAFE model, which combines Cellular Automata with Finite Element Method, to prediction of material flow in processes characterized by change in the deformation path, is the objective of the paper. The general idea of the CAFE model and its application to modelling initiation and propagation of micro shear bands and shear bands is presented. Transition rules developed for new application of the model, which is simulation of processes characterized by the changes in deformation path, are described next. Primary results obtained for simulation of the compression‐tension process are presented and compared with the experimental data available in the literature.