Recent advances and applications of machine learning in metal forming processes

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Recent Advances and Applications of Machine Learning in Metal Forming Processes

Machine learning (ML) technologies are emerging in Mechanical Engineering, driven by the increasing availability of datasets, coupled with the exponential growth in computer performance. In fact, there has been a growing interest in evaluating the capabilities of ML algorithms to approach topics related to metal forming processes, such as: Classification, detection and prediction of forming defects; Material parameters identification; Material modelling; Process classification and selection; Process design and optimization. The purpose of this Special Issue is to disseminate state-of-the-art ML applications in metal forming processes, covering 10 papers about the abovementioned and related topics

Book of abstracts of the 15th International Symposium of Croatian Metallurgical Society - SHMD \u272022, Materials and metallurgy

Book of abstracts of the 15th International Symposium of Croatian Metallurgical Society - SHMD \u272022, Materials and metallurgy, Zagreb, Croatia, March 22-23, 2022. Abstracts are organized in four sections: Materials - section A; Process metallurgy - Section B; Plastic processing - Section C and Metallurgy and related topics - Section D

Book of abstracts of the 15th International Symposium of Croatian Metallurgical Society - SHMD \u272022, Materials and metallurgy

Book of abstracts of the 15th International Symposium of Croatian Metallurgical Society - SHMD \u272022, Materials and metallurgy, Zagreb, Croatia, March 22-23, 2022. Abstracts are organized in four sections: Materials - section A; Process metallurgy - Section B; Plastic processing - Section C and Metallurgy and related topics - Section D

Application of Artificial Neural Networks in Cold Rolling Process

Rolling is one of the most complicated processes in metal forming. Knowing the exact amount of basic parameters, especially inter-stand tensions can be effective in controlling other parameters in this process. Inter-stand tensions affect rolling pressure, rolling force, forward and backward slips and neutral angle. Calculating this effect is an important step in continuous rolling design and control. Since inter-stand tensions cannot be calculated analytically, attempt is made to describes an approach based on artificial neural network (ANN) in order to identify the applied parameters in a cold tandem rolling mill. Due to the limited experimental data, in this subject a five stand tandem cold rolling mill is simulated through finite element method. The outputs of the FE simulation are applied in training the network and then, the network is employed for prediction of tensions in a tandem cold rolling mill. Here, after changing and checking the different designs of the network, the 11-42-4 structure by one hidden layer is selected as the best network. The verification factor of ANN results according to experimental data are over R=0.9586 for training and testing the data sets. The experimental results obtained from the five stands tandem cold rolling mill. This paper proposed new ANN for prediction of inter-stand tensions. Also, this ANN method shows a fuzzy control algorithm for investigating the effect of front and back tensions on reducing the thickness deviations of hot rolled steel strips. The average of the training and testing data sets is mentioned 0.9586. It means they have variable values which are discussed in details in section 4. According to Table 7, this proposed ANN model has the correlation coefficients of 0.9586, 0.9798, 0.9762 and 0.9742, respectively for training data sets and 0.9905, 0.9798, 0.9762 and 0.9803, respectively for the testing data sets. These obtained numbers indicate the acceptable accuracy of the ANN method in predicting the inter-stand tensions of the rolling tandem mill. This method provides a highly accurate solution with reduced computational time and is suitable for on-line control or optimization in tandem cold rolling mills. Due to the limited experimental data, for data extraction for the ANN simulation, a 2D tandem cold rolling process is simulated using ABAQUS 6.9 software. For designing a network for this rolling problem, various structures of neural networks are studied in MATLAB 7.8 software

Process Modeling in Pyrometallurgical Engineering

The Special Issue presents almost 40 papers on recent research in modeling of pyrometallurgical systems, including physical models, first-principles models, detailed CFD and DEM models as well as statistical models or models based on machine learning. The models cover the whole production chain from raw materials processing through the reduction and conversion unit processes to ladle treatment, casting, and rolling. The papers illustrate how models can be used for shedding light on complex and inaccessible processes characterized by high temperatures and hostile environment, in order to improve process performance, product quality, or yield and to reduce the requirements of virgin raw materials and to suppress harmful emissions

Data mining for fault diagnosis in steel making process under industry 4.0

The concept of Industry 4.0 (I4.0) refers to the intelligent networking of machines and processes in the industry, which is enabled by cyber-physical systems (CPS) - a technology that utilises embedded networked systems to achieve intelligent control. CPS enable full traceability of production processes as well as comprehensive data assignments in real-time. Through real-time communication and coordination between "manufacturing things", production systems, in the form of Cyber-Physical Production Systems (CPPS), can make intelligent decisions. Meanwhile, with the advent of I4.0, it is possible to collect heterogeneous manufacturing data across various facets for fault diagnosis by using the industrial internet of things (IIoT) techniques. Under this data-rich environment, the ability to diagnose and predict production failures provides manufacturing companies with a strategic advantage by reducing the number of unplanned production outages. This advantage is particularly desired for steel-making industries. As a consecutive and compact manufacturing process, process downtime is a major concern for steel-making companies since most of the operations should be conducted within a certain temperature range. In addition, steel-making consists of complex processes that involve physical, chemical, and mechanical elements, emphasising the necessity for data-driven approaches to handle high-dimensionality problems. For a modern steel-making plant, various measurement devices are deployed throughout this manufacturing process with the advancement of I4.0 technologies, which facilitate data acquisition and storage. However, even though data-driven approaches are showing merits and being widely applied in the manufacturing context, how to build a deep learning model for fault prediction in the steel-making process considering multiple contributing facets and its temporal characteristic has not been investigated. Additionally, apart from the multitudinous data, it is also worthwhile to study how to represent and utilise the vast and scattered distributed domain knowledge along the steel-making process for fault modelling. Moreover, state-of-the-art does not iv Abstract address how such accumulated domain knowledge and its semantics can be harnessed to facilitate the fusion of multi-sourced data in steel manufacturing. In this case, the purpose of this thesis is to pave the way for fault diagnosis in steel-making processes using data mining under I4.0. This research is structured according to four themes. Firstly, different from the conventional data-driven research that only focuses on modelling based on numerical production data, a framework for data mining for fault diagnosis in steel-making based on multi-sourced data and knowledge is proposed. There are five layers designed in this framework, which are multi-sourced data and knowledge acquisition, data and knowledge processing, KG construction and graphical data transformation, KG-aided modelling for fault diagnosis and decision support for steel manufacturing. Secondly, another of the purposes of this thesis is to propose a predictive, data-driven approach to model severe faults in the steel-making process, where the faults are usually with multi-faceted causes. Specifically, strip breakage in cold rolling is selected as the modelling target since it is a typical production failure with serious consequences and multitudinous factors contributing to it. In actual steel-making practice, if such a failure can be modelled on a micro-level with an adequately predicted window, a planned stop action can be taken in advance instead of a passive fast stop which will often result in severe damage to equipment. In this case, a multifaceted modelling approach with a sliding window strategy is proposed. First, historical multivariate time-series data of a cold rolling process were extracted in a run-to-failure manner, and a sliding window strategy was adopted for data annotation. Second, breakage-centric features were identified from physics-based approaches, empirical knowledge and data-driven features. Finally, these features were used as inputs for strip breakage modelling using a Recurrent Neural Network (RNN). Experimental results have demonstrated the merits of the proposed approach. Thirdly, among the heterogeneous data surrounding multi-faceted concepts in steelmaking, a significant amount of data consists of rich semantic information, such as technical documents and production logs generated through the process. Also, there Abstract v exists vast domain knowledge regarding the production failures in steel-making, which has a long history. In this context, proper semantic technologies are desired for the utilisation of semantic data and domain knowledge in steel-making. In recent studies, a Knowledge Graph (KG) displays a powerful expressive ability and a high degree of modelling flexibility, making it a promising semantic network. However, building a reliable KG is usually time-consuming and labour-intensive, and it is common that KG needs to be refined or completed before using in industrial scenarios. In this case, a fault-centric KG construction approach is proposed based on a hierarchy structure refinement and relation completion. Firstly, ontology design based on hierarchy structure refinement is conducted to improve reliability. Then, the missing relations between each couple of entities were inferred based on existing knowledge in KG, with the aim of increasing the number of edges that complete and refine KG. Lastly, KG is constructed by importing data into the ontology. An illustrative case study on strip breakage is conducted for validation. Finally, multi-faceted modelling is often conducted based on multi-sourced data covering indispensable aspects, and information fusion is typically applied to cope with the high dimensionality and data heterogeneity. Besides the ability for knowledge management and sharing, KG can aggregate the relationships of features from multiple aspects by semantic associations, which can be exploited to facilitate the information fusion for multi-faceted modelling with the consideration of intra-facets relationships. In this case, process data is transformed into a stack of temporal graphs under the faultcentric KG backbone. Then, a Graph Convolutional Networks (GCN) model is applied to extract temporal and attribute correlation features from the graphs, with a Temporal Convolution Network (TCN) to conduct conceptual modelling using these features. Experimental results derived using the proposed approach, and GCN-TCN reveal the impacts of the proposed KG-aided fusion approach. This thesis aims to research data mining in steel-making processes based on multisourced data and scattered distributed domain knowledge, which provides a feasibility study for achieving Industry 4.0 in steel-making, specifically in support of improving quality and reducing costs due to production failures

Experimental and numerical investigation of a method for strengthening cold-formed steel profiles in bending

Perforated cold-formed steel (CFS) beams subjected to different bending scenarios should be able to deal with different buckling modes. There is almost no simple way to address this significant concern. This paper investigates the bending capacity and flexural behavior of a novel-designed system using bolt and nut reinforcing system through both experimental and numerical approaches. For the experiential program, a total of eighteen specimens of three types were manufactured: a non-reinforced section, and two sections reinforced along the upright length at 200 mm and 300 mm pitches. Then, monotonic loading was applied to both the minor and major axes of the specimens. The finite element models were also generated and proved the accuracy of the test results. Using the proposed reinforcing system the flexural capacity of the upright sections was improved around either the major axis or minor axis. The 200 mm reinforcement type provided the best performance of the three types. The proposed reinforcing pattern enhanced flexural behavior and constrained irregular buckling and deformation. Thus, the proposed reinforcements can be a very useful and cost-effective method for strengthening all open CFS sections under flexural loading, considering the trade-off between flexural performance and the cost of using the method