Experimental and modeling studies of the evolution of plate-like δ phase precipitates in Inconel 625 superalloy additively manufactured by the laser powder bed fusion process are performed. The maximum Feret diameter and the number of particles per unit area are used as parameters describing the size and distribution of the δ phase precipitates. On the basis of microstructural analysis and quantitative image analysis, the effect of time and temperature on the development of δ phase precipitates is determined. The distinct differences in the intensity of precipitation, growth, and coarsening of the δ phase precipitates during annealing at temperatures of 700 and 800 °C up to 2000 h are shown. The experimental results are compared with computational data obtained by thermodynamic modeling. Using the experimentally determined parameters of the δ phase precipitates in different variants of annealing, a fuzzy logic-based phase distribution model is designed. Since the quantity of available data was too small to train a model with the machine learning approach, expert knowledge is used to design the rules, while numerical data are used for its validation. Designed rules, as well as reasoning methodology are described. The proposed model is validated by comparing it with the experimental results. It can be used to predict the size and number density of the δ phase precipitates in the additively manufactured Inconel 625, subjected to long-term annealing at temperatures of 700–800 °C. Due to limited experimental data, the quality of assurance is not perfect, but warrants preliminary research. Graphical abstract

The aim of our study was to verify if rule-based reasoning systems could be successfully applied for integration of different models and tools which are used for solving complex decision problems in technological design. Engineering problems are unique and differ from typical problems in the domain of management. To solve those problems, an iterative search of the most suitable solutions must be carried out. This puts a pressure on researchers to utilize optimization techniques in connection with simulation methods. Our research is focused on the usage of rule-based reasoning systems for solving simple decision problems, and their integration with highly-specialized tools. This research has been conducted at one of metal foundries, within the scope of supporting technological decisions. A dedicated platform was used in the research, which is tailored for such applications, i.e., Business and Technological Rules Management System REBIT. REBIT system has been developed at the Faculty of Management at the AGH University of Science and Technology in Cracow. In this paper, we prove that the rule-based approach could provide means to integrate all stages of technological design. This kind of approach has been successfully applied in the Business Rules Management Systems.

A comparison of various approaches to modeling of phase transformations during heat treatment of steels is the objective of the paper. The first approach is a conventional model based on the Avrami-type equation. In this approach, the Avrami coefficients are introduced as the modified Gauss distribution function of temperature. This model is identified using inverse analysis applied to the dilatometric tests. The model with optimized parameters is implemented into the finite element code, which simulates heat treatment of steel parts. The second approach is a coupled finite element-cellular automata (CAFE) multiscale model, which accounts explicitly for the phenomena occurring during transformations in the microscale. Simulations using both models were performed and the results were compared. Advantages of the CAFE model, as far as predictive capabilities and efficiency are concerned, are presented in the final part of the paper.

Modern design of technological processes employs various numerical tools. Artificial Intelligence is one of the most promising approaches. However, its application in industrial conditions is still limited. One of the important obstacles is a lack of large data sets, which are necessary for the most of AI approaches. In the paper, we present a modified Takagi–Sugeno method, one of Fuzzy Rule-Based Systems family, applied for prediction of forging dies wear. A technological process, as well as acquired data are briefly discussed. The modified Takagi-Sugeno approach is introduced. Its main advantage, acquiring knowledge from experts instead of datasets, is emphasized. Assumptions, model details, and prediction results are included.

The challenge on the contemporary market of consumer goods is a quick response to customer needs. It entails time restrictions, which a semi-finished products’ (including metal products) manufacturer must meet. This issue must be addressed during a design phase, which for the most of semi-finished products suppliers, takes part during a quotation preparation process. Our research is aimed at investigating possibility of application of Fuzzy Reasoning methods for shortening of a design process, being a part of this process. We present a study on application of simplified models for solving technological tasks, allowing obtaining expected properties of designed products. The core of our concept is replacing numerical models and classical metamodels with a rule-based reasoning. A quotation preparation process can be supported by solving a technological problem without numerical experiments. Our goal was to validate the thesis basing not only on the presentation of some potential solutions but also on the results of simulation studies. The problem is illustrated with an example of thermal treatment of aluminum alloys, aimed at evaluation of a summary fraction of precipitations as a function of time and technological parameters. We assumed that it is possible to use both unstructured and point numerical experiments for knowledge acquisition. Implementation of this concept required the use of hybrid knowledge acquisition methods that combine the results of point experiments with expert knowledge. A comparison of obtained results to the ones obtained with metamodels shows a similar efficiency of both approaches, while our method is less time and laborious.

The multiscale modelling of the behaviour of metal alloys during processing is often limited by the computing power required to run them. The Agile Multiscale Methodology was conceived to enhance the designing and controlling of complex multiscale models through an automatic run-time adaptation of its constitutive sub-models. This methodology is used to simulate the behaviour of an 6082 aluminium alloy during its thermomechanical treatment. The macroscopic deformation, the work-hardening and the state of precipitation are computed in different modules, allowing the coupling of several software solutions (DEFORM^TM2D and © MatCalc) through an external storage of the relevant data.