An artificial intelligence approach for the in-line evaluation of steels mechanical properties in rolling

Magro, T.; Ghiotti, Andrea; Bruschi, Stefania; Ferraiuolo, A.

doi:10.1016/j.procir.2021.05.054

Cited by 6 publications

(5 citation statements)

References 15 publications

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“…They utilized these measurements to predict the properties yield strength, width and thickness of the processed steel along the coil [27]. Magro et al investigated an approach to predict the yield stress after the tension levelling process and to adapt the initial process parameters according to an analytical model [26]. Grüber and Hirt proposed a semi-analytical model that calculates the levelling force and determines in an inverse approach the material data [25].…”

Section: Non-destructive Testing In Sheet-metal Forming (Rq1)mentioning

confidence: 99%

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Monitoring of fluctuating material properties for optimizing sheet-metal forming processes: a systematic literature review

ORTJOHANN

2023

Materials Research Proceedings

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Abstract. Material properties can vary both along a sheet-metal coil and from coil to coil despite tight tolerances influencing the process stability of sheet-metal processes and the part quality, which leads to rejects and machine downtime [1]. This significantly affect the economic efficiency of the process. Monitoring fluctuations in material characteristics at regular intervals along sheet-metal coils is enabled directly by non-destructive testing (NDT) before the process offering conclusions on the material properties. Another material monitoring technique arises from monitoring process conditions of the upstream processes, e.g., cold rolling, leading indirectly to insights on material properties. In this work, a systematic literature review (SLR) [2] is conducted to investigate recent approaches for material monitoring and for the utilization of resulting material data for optimizing different sheet-metal forming processes. Existing approaches for different sheet-metal forming processes are critically appraised. Based on the SLR research gaps are revealed and research opportunities, e.g., arising from a potential transfer of existing solutions between different forming processes and recent advances in data-driven methods, are identified.

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Section: Non-destructive Testing In Sheet-metal Forming (Rq1)mentioning

confidence: 99%

“…Hot rolling [8,[21][22][23] Cold rolling [24] Roller levelling [25][26][27] Stamping [28] RQ3 Stamping [29][30][31] Deep drawing [32][33][34][35][36][37][38][39][40] Bending [41][42][43] Table 2. Summary of the publication conducted in the SLR.…”

Section: Rq2mentioning

confidence: 99%

Monitoring of fluctuating material properties for optimizing sheet-metal forming processes: a systematic literature review

ORTJOHANN

2023

Materials Research Proceedings

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“…However, in recent years, there has also been significant research on the monitoring of sheet bending with rollers [11][12][13]. The monitoring of the sheet-bending process can also be improved with the application of artificial-intelligence techniques.…”

Section: Introductionmentioning

confidence: 99%

Application-Oriented Data Analytics in Large-Scale Metal Sheet Bending

Penalva,

Martín,

Ruiz

et al. 2023

Applied Sciences

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The sheet-metal-forming process is crucial in manufacturing various products, including pipes, cans, and containers. Despite its significance, controlling this complex process is challenging and may lead to defects and inefficiencies. This study introduces a novel approach to monitor the sheet-metal-forming process, specifically focusing on the rolling of cans in the oil-and-gas sector. The methodology employed in this work involves the application of temporal-signal-processing and artificial-intelligence (AI) techniques for monitoring and optimizing the manufacturing process. Temporal-signal-processing techniques, such as Markov transition fields (MTFs), are utilized to transform time series data into images, enabling the identification of patterns and anomalies. synamic time warping (DTW) aligns time series data, accommodating variations in speed or timing across different rolling processes. K-medoids clustering identifies representative points, characterizing distinct phases of the rolling process. The results not only demonstrate the effectiveness of this framework in monitoring the rolling process but also lay the foundation for the practical application of these methodologies. This allows operators to work with a simpler characterization source, facilitating a more straightforward interpretation of the manufacturing process.

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“…This branch of computer implementation is also known as computer-aided process planning (CAPP) and it includes Artificial Intelligence (AI), Integrated Computational Materials Engineering (ICME) as well as industrial twins (IT) and the occasional coupling of these methods. There are plentiful examples of research papers that are focused on the implementation of the foresaid methods in metal forming, welding and their defects (indicative work [2][3][4][5][6][7]) dating as far back as the late 20th century, foreshadowing what was to come. Circular economy is commonly known by its 4Rs (recovery, recycle, reduce, reuse) (Figure 1).…”

Section: Introductionmentioning

confidence: 99%

“…Proc. 2021, 5, 49 2 of 6 of the foresaid methods in metal forming, welding and their defects (indicative work [2][3][4][5][6][7]) dating as far back as the late 20th century, foreshadowing what was to come. Circular economy is commonly known by its 4Rs (recovery, recycle, reduce, reuse) (Figure 1).…”

Section: Introductionmentioning

confidence: 99%

Opportunities of AI and ICME in Metals Recycling, Production and Processing

Papaefthymiou

Bouzouni²,

Loukadakis

2021

International Conference on Raw Materials and Circular Economy

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The rapid penetration of Artificial Intelligence (AI) and all related developments of the fourth industrial revolution is paving the way for a more sophisticated production sequence that strives for higher quality, lower emissions and lower cost production. This work reviews and discusses these developments and correlates them with state-of-the-art changes in materials engineering. We highlight penetration paradigms of modern computation tools. These technologies sound very promising in terms of maximizing the production efficiency of modern industries and, thus, minimizing the required energy input, greenhouse gas emissions and leading the way to a more ecofriendly economy.

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An artificial intelligence approach for the in-line evaluation of steels mechanical properties in rolling

Cited by 6 publications

References 15 publications

Monitoring of fluctuating material properties for optimizing sheet-metal forming processes: a systematic literature review

Monitoring of fluctuating material properties for optimizing sheet-metal forming processes: a systematic literature review

Application-Oriented Data Analytics in Large-Scale Metal Sheet Bending

Opportunities of AI and ICME in Metals Recycling, Production and Processing

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