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The purpose of this paper is to study the isothermal and nonisothermal crystallisation kinetics of pure polypropylene (PP), 1 kGy pre‐irradiated PP and 1 kGy pre‐irradiated PP/syndiotactic 1,2‐polybutadiene (s‐1,2 PB) (90/10) blends by differential scanning calorimetry.

The Avrami equation, modified Avrami equation, Ozawa equation and the treatment by combining the Avrami and Ozawa equation were used to analyse the isothermal and nonisothermal crystallisation of various samples.

The s‐1,2 PB acted as a heterogeneous nucleation agent during the crystallisation of the PP/s‐1,2 PB blends and accelerated the crystallisation rate. The Avrami exponent n of the blends implied that the isothermal crystallisation kinetics of the blends followed a three‐dimensional growth via heterogeneous nucleation. The modified Avrami equation was limited to describe the nonisothermal crystallisation process of pure PP and 1 kGy pre‐irradiated PP, but it was successful for the blends. The treatment by combining the Avrami and Ozawa equation described appropriately the nonisothermal crystallisation process and obtained the kinetic parameter F(T) with specific physical meaning. The crystallisation activation energy for isothermal crystallisation and nonisothermal crystallisation of the blends was reduced due to the s‐1,2 PB acting as a heterogeneous nucleating agent during the crystallisation of the blends and accelerating the crystallisation rate.

The Avrami equation, modified Avrami equation, Ozawa equation and the treatment by combining the Avrami and Ozawa equation were compared for analysis of the isothermal and nonisothermal crystallisation of samples. The crystallisation activation energy for isothermal crystallisation and nonisothermal crystallisation was also calculated according to the Arrhenius and the Kissinger method.

The fundamental research on the crystallisation properties of PP/s‐1,2‐PB blends is essential to understand the mutual effects of two components on their crystallisation mechanisms, facilitating to improve the mechanical properties of the final materials.

The isothermal and nonisothermal crystallisation behaviours of PP/s‐1,2 PB blends, especially pre‐irradiated PP/s‐1,2 PB blends, have not been studied systematically yet, though PP/s‐1,2 PB blends were promising materials in terms of both PP toughening and the application of s‐1,2 PB thermal plastic elastomer.

This paper aims to disclose the evolution of pendulum hardness of two-component acrylic polyurethane coatings during the cure process and attempts to describe the quantitative relationship between pendulum hardness and curing time. These findings are helpful for the study of fast curing acrylic polyurethane coatings.

The pendulum hardness method was used to monitor the hardness of two-component acrylic polyurethane coatings during curing. The quantitative relationship between pendulum hardness and curing time can be obtained with Avrami equation.

The evolution of coating pendulum hardness can be divided into three stages. By using the Avrami equation that explained the influence of both the acid value and the curing temperature on the drying speed of hydroxyl acrylic resin, the evolution of coating pendulum hardness during curing can also be accurately described.

It should be noted that the physical meaning of the Avrami exponent, n, is not yet clear.

The results are of great significance for the development of fast-curing hydroxyl-functional acrylic resins, with the potential to improve the drying speed of the coatings used in automotive refinish.

It is novel to divide the pendulum hardness into three stages, and, for the first time, the Avrami equation is utilized to describe the evolution of coating pendulum hardness during curing.

This paper sets out to show the feasibility of the genetic algorithm inverse method for the determination of the parameters of crystallization kinetics laws in isothermal and non‐isothermal conditions, using multiple experiments.

The mathematical model for crystallization kinetics determination and the numerical methods of its resolution are introduced. Crystallization kinetic parameters determined by approximate physical analysis and the inverse genetic algorithm method are presented. Injection molding simulations taking into account crystallization are performed using the finite element method.

It is necessary to perform the optimization on two parameters, transformed volume fraction and number of spherulites to obtain correct results. It is possible to use results from different samples, in spite of the dispersion of some values.

Experimental data for isothermal and non‐isothermal conditions were used and obtained good results for the parameters of crystallization kinetics laws from which the evolutions of overall crystallization kinetics and crystalline microstructure were deduced. Nevertheless, the dispersion of the experimental data concerning the number of spherulites obtained with different samples is important. The evolution of the number of spherulites is required for the optimization to get correct results.

An important result of this work is that the genetic algorithm optimization can be applied to this problem where the experiments cannot be performed with a single sample and the experimental data for the number of spherulites have low precision. Even if only the crystallization kinetics was considered, the feasibility in molding simulation has been shown.

Simulation of crystallization in injection molding is very important for a later prediction of the end‐use properties.

This paper aims to present a survey concerning the accuracy of thermoplastic polymeric parts fabricated by additive manufacturing (AM). Based on the scientific literature, the aim is to provide an updated map of trends and gaps in this relevant research field. Several technologies and investigation methods are examined, thus giving an overview and analysis of the growing body of research.

Permutations of keywords, which concern materials, technologies and the accuracy of thermoplastic polymeric parts fabricated by AM, are used for a systematic search in peer-review databases. The selected articles are screened and ranked to identify those that are more relevant. A bibliometric analysis is performed based on investigated materials and applied technologies of published papers. Finally, each paper is categorised and discussed by considering the implemented research methods.

The interest in the accuracy of additively manufactured thermoplastics is increasing. The principal sources of inaccuracies are those shrinkages occurring during part solidification. The analysis of the research methods shows a predominance of empirical approaches. Due to the experimental context, those achievements have consequently limited applicability. Analytical and numerical models, which generally require huge computational costs when applied to complex products, are also numerous and are investigated in detail. Several articles deal with artificial intelligence tools and are gaining more and more attention.

The cross-technology survey on the accuracy issue highlights the common critical aspects of thermoplastics transformed by AM. An updated map of the recent research literature is achieved. The analysis shows the advantages and limitations of different research methods in this field, providing an overview of research trends and gaps.

In this study, a three‐dimensional (3D) flow model is used to approximate the crystallinity gradients of slowly crystallizing polymers developed in the injection molding process. A generalized second order parallel splitting formula is constructed to achieve both the accuracy and efficiency of the computation. Calculated values of flow‐wise (flow‐thickness plane) and width‐wise (width‐thickness plane) crystallinity distributions are obtained and compared with experimental results. The structure‐oriented simulation method developed is not only capable of describing moldability parameters, but is also able to predict the characteristics of ultimate properties of the final products.

This study aims to focus on numerical simulation investigations of phase transformation during cooling of 55SiMnMo steel, which is commonly applied to improve mechanical properties.

A mathematical model based on the finite element method (FEM) and the phase transformation kinetics model has been proposed to predict microstructure changes during continuous cooling of 55SiMnMo steel. This model can be employed to analyze the variation of austenite, special upper bainite and lump-like composite structure with cooling time at different cooling rates.

According to the continuous cooling experiments, when the cooling rate is lower than 0.1°C/s, the special upper bainite is the only transformation product which decreases with increasing cooling rate; when the cooling rate is above 0.5°C/s, the transformation products include special upper bainite and lump-like composite structure. Meanwhile, the results of continuous cooling experiment verified the correctness of this finite element model.

This model has a great value for proper controlling of the cooling process which can improve the quality of hollow drill steel and increase the service life of the final product.

Some functional properties of engineering materials, i.e. physical, mechanical and thermal ones, depend directly on the microstructure, which is a result of processes occurring in the material during the forming and thermomechanical processing. The proper microstructure can be obtained in many cases by the phase transformation. This phenomenon is one of the most important processes during hot forming and heat treatment. The purpose of this paper is to develop a new comprehensive hybrid model for modeling diffusion phase transformations. A problem has been divided into several tasks and is carried out on several stages. The purpose of this stage is a development of the structure of a hybrid model, development of an algorithm used in the diffusion module and one-dimensional heat flow and diffusion modeling. Generally, the processes of phase transformations are studied well enough but there are not many tools for their complex simulations. The problems of phase transformation simulation are related to the proper consideration of diffusion, movement of phase boundaries and kinetics of transformation. The proposed new model at the final stage of development will take into account the varying grain growth rate, different shape of growing grains and will allow for proper modeling of heat flow and carbon diffusion during the transformation in many processes, where heating, annealing and cooling can be considered (e.g. homogenizing and normalizing).

One of the most suitable methods for modeling of microstructure evolution during the phase transformation is cellular automata (CA), while lattice Boltzmann method (LBM) suits for modeling of diffusion and heat flow. Then, the proposed new hybrid model is based on CA and LBM methods and uses high performing parallel computations.

The first simulation results obtained for one-dimensional modeling confirm the correctness of interaction between LBM and CA in common numerical solution and the possibility of using these methods for modeling of phase transformations. The advantages of the LBM method can be used for the simulation of heat flow and diffusion during the transformation taking into account the results obtained from the simulations. LBM creates completely new possibilities for modeling of phase transformations in combination with CA.

The studies are focused on diffusion phase transformations in solid state in condition of low cooling rate (e.g. transformation of austenite into ferrite and pearlite) and during the heating and annealing (e.g. transformation of the ferrite-pearlite structure into austenite, the alignment of carbon concentration in austenite and growth of austenite grains) in carbon steels within a wide range of carbon content. The paper presents the comprehensive modeling system, which can operate with the technological processes with phase transformation during heating, annealing or cooling.

A brief review of the modeling of phase transformations and a description of the structure of a new CA and LBM hybrid model and its modules are presented in the paper. In the first stage of model implementation, the one-dimensional LBM model of diffusion and heat flow was developed. The examples of simulation results for several variants of modeling with different boundary conditions are shown.

Additive manufacturing (AM) is revolutionizing the manufacturing industry due to several advantages and capabilities, including use of rapid prototyping, fabrication of complex geometries, reduction of product development cycles and minimization of material waste. As metal AM becomes increasingly popular for aerospace and defense original equipment manufacturers (OEMs), a major barrier that remains is rapid qualification of components. Several potential defects (such as porosity, residual stress and microstructural inhomogeneity) occur during layer-by-layer processing. Current methods to qualify AM parts heavily rely on experimental testing, which is economically inefficient and technically insufficient to comprehensively evaluate components. Approaches for high fidelity qualification of AM parts are necessary.

This review summarizes the existing powder-based fusion computational models and their feasibility in AM processes through discrete aspects, including process and microstructure modeling.

Current progresses and challenges in high fidelity modeling of AM processes are presented.

Potential opportunities are discussed toward high-level assurance of AM component quality through a comprehensive computational tool.

The dynamic recrystallization (DRX) and flow stress of Nb‐bearing steels were investigated by means of isothermal single compression testing at temperatures of 850‐105° and at constant strain rate from 0.1 to 20s‐1 using a Gleeble 3800 thermo‐mechanical simulator in order to model the DRX processes and predict the flow stress during plate rolling. On the basis of the measured flow stress, a new model of DRX kinetics was proposed to calculate the volume fraction of dynamically recrystallized grains, which was a function of processing parameters such as deformation temperature, strain, strain rate, the initial austenite grain size and Nb content. The effect of deformation conditions was quantified by the Zener‐Hollomon parameter, in which the activation energy of deformation was expressed as a power function of Nb content. The critical strain was determined by using the method proposed by Jonas and co‐workers. It is shown that the ratio of the critical strain to the peak strain decreases with increasing Nb content, from which an empirical equation was developed. In addition, the influence of Nb content and deformation conditions on the steady state grain size was determined by fitting the experimental results to a linear relationship. Finally, the flow stress of Nb bearing steels was accurately predicted using a one‐internal‐variable evolution equation by taking Nb content as a parameter and including the influence of DRX. The comparison between the experimental and theoretical results confirmed that the modeling had a good accuracy to predict flow stresses during hot deformation.

Reliable modeling of induction hardening requires a multi-physical approach, which makes it time-consuming. In designing an induction hardening system, combining such model with an optimization technique allows managing a high number of design variables. However, this could lead to a tremendous overall computational cost. This paper aims to reduce the computational time of an optimal design problem by making use of multi-fidelity modeling and parallel computing.

In the multi-fidelity framework, the “high-fidelity” model couples the electromagnetic, thermal and metallurgical fields. It predicts the phase transformations during both the heating and cooling stages. The “low-fidelity” model is instead limited to the heating step. Its inaccuracy is counterbalanced by its cheapness, which makes it suitable for exploring the design space in optimization. Then, the use of co-Kriging allows merging information from different fidelity models and predicting good design candidates. Field evaluations of both models occur in parallel.

In the design of an induction heating system, the synergy between the “high-fidelity” and “low-fidelity” model, together with use of surrogates and parallel computing could reduce up to one order of magnitude the overall computational cost.

On one hand, multi-physical modeling of induction hardening implies a better understanding of the process, resulting in further potential process improvements. On the other hand, the optimization technique could be applied to many other computationally intensive real-life problems.

This paper highlights how parallel multi-fidelity optimization could be used in designing an induction hardening system.