Processing–Structure–Property Relationships in Metals

Montanari, Roberto; Varone, Alessandra

doi:10.3390/met9080907

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…To improve industrial manufacturing processes, a better understanding of the process-structure-property relation is required [22,23]. Computational simulation models allow to gain insights of the process-structures-properties relations [24].…”

Section: Simulation Of Manufacturing Systemsmentioning

confidence: 99%

Model-based analysis, control and dosing of electroplating electrolytes

Leiden

Kölle

Thiede

et al. 2020

Int J Adv Manuf Technol

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Controlling and dosing electrolytes is a key challenge in the operation of electroplating process chains. Electrolyte components are continuously degraded and dragged out during the production process. This process is influenced by a variety of internal and external factors such as process parameters, the electrolyte itself, anodes, the substrates and the production environment. The exact analytical measurement of the electrolyte composition requires extensive analytical equipment and typically cannot be completely realized within an industrial plating company. Therefore, this paper presents a model-based approach, integrated in a cyber-physical production system, for controlling and dosing electrolytes. A mathematical resource flow model is the basis for a dynamic agent-based simulation. This model uses available data from the manufacturing execution system and enterprise resource planning system to model the current composition of the electrolyte. The approach is successfully validated for two different electrolyte substances at an industrial acid zinc–nickel barrel plating process chain for automotive parts.

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Section: Simulation Of Manufacturing Systemsmentioning

confidence: 99%

Model-based analysis, control and dosing of electroplating electrolytes

Leiden

Kölle

Thiede

et al. 2020

Int J Adv Manuf Technol

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show abstract

“…The direct correlation between a material's microstructure and properties necessitates precise and rapid microstructural characterization to elucidate the process-structureproperty relationship [1,2]. This is particularly evident in steels, where microstructural variations induced by changes in composition and processing can significantly alter mechanical properties.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Labeling Method for Material Microstructure Image Segmentation

Wang,

Su,

et al. 2023

Processes

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In the existing deep learning modeling process for material microstructure image segmentation, the manual pixel labeling process is time-consuming and laborious. In order to achieve fast and high-accuracy modeling, this work proposes a convenient deep learning labeling method and a workflow for generating a synthetic image data set. Firstly, a series of label templates was prepared by referring to the distribution of the material microstructure. Then, the typical textures of different microstructures were box-selected in the images to be segmented to form texture templates. The manual pixel labeling was simplified to the box-selection of the typical microstructure texture. Finally, a synthetic data set can be generated using the label and texture templates for further deep learning model training. Two image cases containing multiple types of microstructures were used to verify the labeling method and workflow. The results show that the pixel segmentation accuracy of the deep learning model for the test images reaches 95.92% and 95.40%, respectively. The modeling workflow can be completed within 20 min, and the labeling time that requires manual participation is within 10 min, significantly reducing the modeling time compared to traditional methods where the labeling process may take several hours.

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Influence of the different variants of the surface treatment on the durability of forging dies made of Unimax steel

Hawryluk

Dobras

Kaszuba

et al. 2020

Int J Adv Manuf Technol

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The study performs an analysis as well as makes a comparison of the durability of forging tools used in the die forging process made of high-strength steel Unimax. For tool steel WCL (1.2343 according to DIN), which has been applied so far, the obtained average durability has been at the level of about 6000 forgings. Additionally, in order to increase the durability of the Unimax material, two surface treatment variants were applied: in the form of ion nitriding (for nitrides A) and gas nitriding together with a PVD-Alvin coating, which were compared with the results for an insert without surface treatment. For each variant, three tools were produced, in order to obtain repeatable and verified results. In the first place, an analysis of the working conditions of the tools was performed through thorough observations of the industrial forging process, particularly the tribological conditions, including the manner of lubrication as well as the temperature distributions, by means of, among others, thermovisual examinations. Additionally, numerical modeling of the process was carried out with the purpose of a more accurate analysis of the tool work in contact. Next, a detailed analysis of the exploitation of the worn tools was performed, including a macroscopic and geometrical analysis through 3D scanning, microscopic optical, and SEM tests as well as microhardness measurements. The obtained results demonstrated that only the application of the new material, Unimax, itself caused a durability increase by 2.5 times with regard to the WCL steel used so far. In turn, with the application of additional surface engineering techniques, Unimax tools characterized in better operational properties (high thermal and abrasive wear resistance at elevated temperatures), which made it possible to forge over four times more forgings, i.e., 26,000 items, after nitriding with a PVD-Alvin coating had been applied to the tool.

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Processing–Structure–Property Relationships in Metals

Abstract: The increasing demand for advanced materials in construction, transportation, communications, medicine, energy production, as well as in several other fields, is the driving force for investigating the processing–structure–property relationships [...]

Cited by 3 publications

References 16 publications

Model-based analysis, control and dosing of electroplating electrolytes

Model-based analysis, control and dosing of electroplating electrolytes

A Deep Learning Labeling Method for Material Microstructure Image Segmentation

Influence of the different variants of the surface treatment on the durability of forging dies made of Unimax steel

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