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Krakhmalev, Pavel, ProfessorORCID iD iconorcid.org/0000-0002-9441-2502
Publications (10 of 112) Show all publications
Subasic, M., Olsson, M., Dadbakhsh, S., Zhao, X., Krakhmalev, P. & Mansour, R. (2024). Fatigue strength improvement of additively manufactured 316L stainless steel with high porosity through preloading. International Journal of Fatigue, 180, Article ID 108077.
Open this publication in new window or tab >>Fatigue strength improvement of additively manufactured 316L stainless steel with high porosity through preloading
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2024 (English)In: International Journal of Fatigue, ISSN 0142-1123, E-ISSN 1879-3452, Vol. 180, article id 108077Article in journal (Refereed) Published
Abstract [en]

This work investigates the influence of a single tensile preload, applied prior to fatigue testing, on the fatigue strength of 316L stainless steel parts manufactured using laser-based powder bed fusion (PBF-LB) with a porosity of up to 4 %. The specimens were produced in both the horizontal and vertical build directions and were optionally preloaded to 85 % and 110 % of the yield strength before conducting the fatigue tests. The results indicate a clear tendency of improved fatigue life and fatigue limit with increasing overload in both cases. The fatigue limits increased by 25.8 % and 24.6 % for the horizontally and vertically built specimens, respectively. Extensive modelling and experiments confirmed that there was no significant alteration in the shape and size of the porosity before and after preloading. Therefore, the observed enhancement in fatigue performance was primarily attributed to the imposed local compressive residual stresses around the defects. 

Place, publisher, year, edition, pages
Elsevier, 2024
Keywords
Preload, Overload, Fatigue strength, 316L stainless steel, Porosity, Defects, PBF-LB
National Category
Metallurgy and Metallic Materials Building Technologies
Research subject
Materials Science
Identifiers
urn:nbn:se:kau:diva-98040 (URN)10.1016/j.ijfatigue.2023.108077 (DOI)2-s2.0-85181121906 (Scopus ID)
Funder
KTH Royal Institute of Technology
Available from: 2024-01-17 Created: 2024-01-17 Last updated: 2024-01-17Bibliographically approved
Ma, Y., Younis, K., Ahmed, B. S., Kassler, A., Krakhmalev, P., Thore, A. & Lindback, H. (2023). Automated and Systematic Digital Twins Testing for Industrial Processes. In: Proceedings - 2023 IEEE 16th International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2023: . Paper presented at 16th IEEE International Conference on Software Testing, Verification and Validation Workshops, Dublin,Ireland, April 16-20, 2023. (pp. 149-158). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Automated and Systematic Digital Twins Testing for Industrial Processes
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2023 (English)In: Proceedings - 2023 IEEE 16th International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2023, Institute of Electrical and Electronics Engineers (IEEE), 2023, p. 149-158Conference paper, Published paper (Refereed)
Abstract [en]

Digital twins (DT) of industrial processes have become increasingly important. They aim to digitally represent the physical world to help evaluate, optimize, and predict physical processes and behaviors. Therefore, DT is a vital tool to improve production automation through digitalization and becomes more sophisticated due to rapidly evolving simulation and modeling capabilities, integration of IoT sensors with DT, and high-capacity cloud/edge computing infrastructure. However, the fidelity and reliability of DT software are essential to represent the physical world. This paper shows an automated and systematic test architecture for DT that correlates DT states with real-time sensor data from a production line in the forging industry. Our evaluation shows that the architecture can significantly accelerate the automatic DT testing process and improve its reliability. A systematic online DT testing method can significantly detect the performance shift and continuously improve the DT’s fidelity. The snapshot creation methodology and testing agent architecture can be an inspiration and can be generally applicable to other industrial processes that use DT to generalize their automated testing. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Keywords
Automation, E-learning, Industry 4.0, Reinforcement learning, Software reliability, Industrial processs, Machine-learning, Modelling capabilities, Physical behaviors, Physical process, Physical world, Production automation, Reinforcement learnings, Simulation and modeling, Software testings, Software testing
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-96056 (URN)10.1109/ICSTW58534.2023.00037 (DOI)2-s2.0-85163093915 (Scopus ID)979-8-3503-3335-0 (ISBN)
Conference
16th IEEE International Conference on Software Testing, Verification and Validation Workshops, Dublin,Ireland, April 16-20, 2023.
Funder
Knowledge FoundationVinnova
Available from: 2023-07-07 Created: 2023-07-07 Last updated: 2023-08-07Bibliographically approved
Vilardell, A. M., Pelcastre, L., Dimitrios, N., Krakhmalev, P., Kato, M., Takata, N. & Kobashi, M. (2023). B2-structured Fe3Al alloy manufactured by laser powder bed fusion: Processing, microstructure and mechanical performance. Intermetallics (Barking), 156, Article ID 107849.
Open this publication in new window or tab >>B2-structured Fe3Al alloy manufactured by laser powder bed fusion: Processing, microstructure and mechanical performance
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2023 (English)In: Intermetallics (Barking), ISSN 0966-9795, E-ISSN 1879-0216, Vol. 156, article id 107849Article in journal (Refereed) Published
Abstract [en]

Prealloyed Fe3Al was successfully manufactured by laser powder bed fusion. The best set of process parameters led to parts with a relative density of 99.5 %, a surface roughness, Sa, of 31.5 ± 5.6 μm and a hardness of 319 ± 14 HV0.1. Its microstructure as well as its mechanical properties at room and high temperatures were analyzed. The results of the chemical composition showed minor variations in aluminum content oscillating between 21 and 28 at.% along the melt pool. Additionally, elongated grains were observed to grow parallel to the building direction, as well as the development of a weak 001 texture along the building direction. The mechanical properties were influenced by the temperature. Compression tests showed a loss in strength with the increase in temperature, from a yield strength of 621 ± 40 MPa at room temperature to 89 ± 20 MPa at 650 °C. Reciprocating sliding wear tests showed that fragmentation of the intermetallic at room temperature occurs, whereas plastic deformation dominated at higher temperatures. For all temperatures, tribochemical wear was also present due to the oxidation of wear debris. 

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Aluminum alloys, Binary alloys, Compression testing, Ductile fracture, Iron alloys, Surface roughness, Textures, Wear of materials, Laser powders, Laser process, Laser processing and cladding, Mechanical performance, Microstructure performance, Powder bed, Prealloyed, Process parameters, Processing performance, Relative density, Intermetallics
National Category
Manufacturing, Surface and Joining Technology Other Materials Engineering Tribology (Interacting Surfaces including Friction, Lubrication and Wear)
Research subject
Materials Engineering; Mechanical Engineering
Identifiers
urn:nbn:se:kau:diva-93854 (URN)10.1016/j.intermet.2023.107849 (DOI)000954396000001 ()2-s2.0-85148007172 (Scopus ID)
Available from: 2023-03-06 Created: 2023-03-06 Last updated: 2023-04-18Bibliographically approved
Vilardell, A. M., Cantillo Alzamora, V., Bauso, L. V., Madrid, C., Krakhmalev, P., Albu, M., . . . Garcia-Giralt, N. (2023). Effect of Heat Treatment on Osteoblast Performance and Bactericidal Behavior of Ti6Al4V(ELI)-3at.%Cu Fabricated by Laser Powder Bed Fusion. Journal of Functional Biomaterials, 14(2), Article ID 63.
Open this publication in new window or tab >>Effect of Heat Treatment on Osteoblast Performance and Bactericidal Behavior of Ti6Al4V(ELI)-3at.%Cu Fabricated by Laser Powder Bed Fusion
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2023 (English)In: Journal of Functional Biomaterials, ISSN 2079-4983, E-ISSN 2079-4983, Vol. 14, no 2, article id 63Article in journal (Refereed) Published
Abstract [en]

Cu addition to alloys for biomedical applications has been of great interest to reduce bacterial growth. In situ-alloyed Ti6Al4V(ELI)-3at.%Cu was successfully manufactured by laser powder bed fusion (L-PBF). Even so, post-heat treatments are required to avoid distortions and/or achieve required/desired mechanical and fatigue properties. The present study is focused on the investigation of microstructural changes in L-PBF Ti6Al4V(ELI)-3at.%Cu after stress relieving and annealing treatments, as well as their influence on osteoblast and bactericidal behavior. After the stress relieving treatment, a homogenously distributed β phase and CuTi2 intermetallic precipitates were observed over the αʹ matrix. The annealing treatment led to the increase in amount and size of both types of precipitates, but also to phase redistribution along α lamellas. Although microstructural changes were not statistically significant, such increase in β and CuTi2 content resulted in an increase in osteoblast proliferation after 14 days of cell culture. A significant bactericidal behavior of L-PBF Ti6Al4V(ELI)-3at.%Cu by means of ion release was found after the annealing treatment, provably due to the easier release of Cu ions from β phase. Biofilm formation was inhibited in all on Cu-alloyed specimens with stress relieving but also annealing treatment. 

Place, publisher, year, edition, pages
MDPI, 2023
Keywords
aluminum, copper, titanium, vanadium, Article, bacterial growth, bacterial strain, bactericidal activity, bone mineralization, cell proliferation, cell viability, colony forming unit, controlled study, energy dispersive X ray spectroscopy, Escherichia coli, heat treatment, human, human cell, nonhuman, osteoblast, particle size, powder bed fusion, scanning electron microscopy, Staphylococcus aureus, X ray diffraction, bactericidal effect, laser powder bed fusion, microstructure, osteoblast activity, Ti–Cu alloys
National Category
Materials Engineering
Research subject
Materials Science
Identifiers
urn:nbn:se:kau:diva-93966 (URN)10.3390/jfb14020063 (DOI)000939144300001 ()2-s2.0-85148860618 (Scopus ID)
Available from: 2023-03-20 Created: 2023-03-20 Last updated: 2023-03-27Bibliographically approved
Hentschel, O., Krakhmalev, P., Fredriksson, G., Olsèn, J., Selte, A. & Schmidt, M. (2023). Influence of the in-situ heat treatment during manufacturing on the microstructure and properties of DED-LB/M manufactured maraging tool steel. Journal of Materials Processing Technology, 315, Article ID 117928.
Open this publication in new window or tab >>Influence of the in-situ heat treatment during manufacturing on the microstructure and properties of DED-LB/M manufactured maraging tool steel
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2023 (English)In: Journal of Materials Processing Technology, ISSN 0924-0136, E-ISSN 1873-4774, Vol. 315, article id 117928Article in journal (Refereed) Published
Abstract [en]

Due to high productivity, additive manufacturing (AM), and especially Directed Energy Deposition using laser and metallic powder (DED-LB/M) is attractive for manufacturing tools with integrated functionalities. This investigation was dedicated to DED-LB/M manufacturing of experimental maraging tool steel, characterization of the build microstructure with advanced electron microscopy and evaluation of hardness properties. High printability and low porosity of the final builds were observed, relative density was not lower than 99.5% for specimens manufactured with 600 W and 800 W, but microstructure and properties of the build had a gradient along the height. The characteristic hardness profile and microstructure, which were dependent on the manufacturing parameters, were observed. The top layers of manufactured maraging steel samples had a structure of martensite with precipitates presumably formed during solidification. The top layers were therefore softer to the depth of the austenitization isotherm. The higher hardness was measured in the inner regions which was a result of an in-situ heat treatment that the manufactured material was subjected to during layer-by-layer manufacturing. Thermal cycles during manufacturing resulted in precipitation hardening effect in the inner regions. Scanning and transmission electron microscopy confirmed the formation film-like and round particles in the as-build material, in top and inner regions. However, the quasicrystalline nano-sized R′-phase precipitates were observed only in the inner regions. The formation of the R′-phase precipitated during manufacturing as a result of the in-situ heat treatment was discussed as a reason for higher hardness (440 – 450 HV1) measured in the inner regions. 

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Additive manufacturing, Directed Energy Deposition (DED-LB/M), Hardness, Maraging steel, Precipitates, Additives, Age hardening, Deposition, High resolution transmission electron microscopy, Microstructure, Scanning electron microscopy, Tool steel, Tools, Directed energy, Energy depositions, High hardness, Inner region, Maraging, Microstructure and properties, R phase, Situ heat treatments, Top layers, 3D printing
National Category
Manufacturing, Surface and Joining Technology Metallurgy and Metallic Materials
Research subject
Mechanical Engineering; Materials Science
Identifiers
urn:nbn:se:kau:diva-94694 (URN)10.1016/j.jmatprotec.2023.117928 (DOI)001041769300001 ()2-s2.0-85150472874 (Scopus ID)
Available from: 2023-05-15 Created: 2023-05-15 Last updated: 2023-08-24Bibliographically approved
Javadzadeh Kalahroudi, F., Sadek, M., Krakhmalev, P., Berglund, T., Bergström, J. & Grehk, M. (2023). On the microstructure and high cycle fatigue of near-net shape PM-HIPed Inconel 625. Materials Science & Engineering: A, 886, Article ID 145671.
Open this publication in new window or tab >>On the microstructure and high cycle fatigue of near-net shape PM-HIPed Inconel 625
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2023 (English)In: Materials Science & Engineering: A, ISSN 0921-5093, E-ISSN 1873-4936, Vol. 886, article id 145671Article in journal (Refereed) Published
Abstract [en]

This paper investigated the microstructure and fatigue behavior of PM-HIPed Inconel 625. The microstructure was composed of γ phase and (Mo, Nb) carbonitrides located mostly on prior particle boundaries. Despite the presence of these carbonitrides, the samples showed good tensile properties with high elongation. Two different surface conditions, pickled and machined, were used for high cycle fatigue testing under a four-point bending test. The results indicated that pickled samples had 6% lower fatigue strength (at 106 cycles) with three times higher standard deviation compared to the machined ones. Fatigue failure mechanisms were found to be dependent on surface conditions and showed different failure modes due to non-metallic oxide inclusions and surface defects in samples with machined and pickled surfaces, respectively. The effect of type, size, and location of defects, multiplicity of crack initiations, as well as surface roughness were analyzed and discussed.

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Fatigue properties, Inconel 625, Mechanical properties, Microstructure, Surface roughness, Carbon nitride, Failure (mechanical), Fatigue testing, High-cycle fatigue, Niobium compounds, Surface defects, Fatigue behaviour, Four-point bending test, High cycle fatigue, High cycle fatigue testing, High elongation, Near net shape, Prior particle boundaries, Surface conditions
National Category
Manufacturing, Surface and Joining Technology
Research subject
Materials Engineering; Materials Science
Identifiers
urn:nbn:se:kau:diva-97121 (URN)10.1016/j.msea.2023.145671 (DOI)001080121200001 ()2-s2.0-85171337833 (Scopus ID)
Available from: 2023-10-20 Created: 2023-10-20 Last updated: 2024-01-29Bibliographically approved
Kuzminova, Y. O., Firsov, D. G., Shibalova, A. A., Kudryavtsev, E. A., Krakhmalev, P., Klimova-Korsmik, O. G., . . . Evlashin, S. A. (2023). Structural and mechanical properties of the additive manufactured CrFeCoNi(Al,Ti) high-entropy alloys produced using powder blends. Materialia, 32, Article ID 101957.
Open this publication in new window or tab >>Structural and mechanical properties of the additive manufactured CrFeCoNi(Al,Ti) high-entropy alloys produced using powder blends
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2023 (English)In: Materialia, E-ISSN 2589-1529, Vol. 32, article id 101957Article in journal (Refereed) Published
Abstract [en]

High-entropy Alloys (HEAs) are considered prospective materials demonstrating the new approach of alloy design creating new compositions for harsh conditions. However, searching for alloy chemical composition providing the best material properties is a costly process. Additive manufacturing (AM) can be an effective technique for adjusting the alloy composition by using several initial materials. The powder bed fusion (PBF) AM process allows the printing of solid parts using powder blends. In the present study, the CrFeCoNi(Al,Ti) HEAs were printed by the PBF technique using the blends of three powders. The structural and phase investigations revealed the chemical inhomogeneity in the materials that led to the new phase formations affecting the mechanical characteristics. The high-temperature annealing at 1200 °C can be considered a post-treatment process for the printed alloys as a homogenization process while the annealing at a lower temperature of 800 °C initiates the decomposition of the initially formed f.c.c. phase. 

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
3D printing, Additives, Entropy, High-entropy alloys, Homogenization method, Microstructure, Titanium alloys, Alloy compositions, Alloy designs, Chemical compositions, Condition, High entropy alloys, New approaches, Powder bed, Powder blends, Prospectives, Structural and mechanical properties, Scanning electron microscopy
National Category
Metallurgy and Metallic Materials Composite Science and Engineering Manufacturing, Surface and Joining Technology
Research subject
Materials Science
Identifiers
urn:nbn:se:kau:diva-97569 (URN)10.1016/j.mtla.2023.101957 (DOI)001113114000001 ()2-s2.0-85176453973 (Scopus ID)
Available from: 2023-11-29 Created: 2023-11-29 Last updated: 2023-12-21Bibliographically approved
Panahi, N., Åsberg, M., Oikonomou, C. & Krakhmalev, P. (2022). Effect of preheating temperature on the porosity and microstructure of martensitic hot work tool steel manufactured with L-PBF. Paper presented at 12th CIRP Conference on Photonic Technologies, LANE 2022, 4 September 2022 through 8 September 2022. Procedia CIRP, 111, 166-170
Open this publication in new window or tab >>Effect of preheating temperature on the porosity and microstructure of martensitic hot work tool steel manufactured with L-PBF
2022 (English)In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 111, p. 166-170Article in journal (Refereed) Published
Abstract [en]

Additive manufacturing (AM) brings the possibility to produce tools with a complex design of the cooling channels to achieve desired properties in the working piece. However, AM of tool steels can be challenging due to the residual stresses and crack formation which may be mitigated by optimizing process parameters. In this study, process parameters development in the laser powder bed fusion (L-PBF) of a martensitic hot work tool steel was under investigation. Having power, scanning speed and hatch distance as variables, a design of experiment was performed to map a process window to produce dense components. In addition, the same sets of process parameters were applied at different preheating temperatures to study and discuss the effect of three preheating temperatures on the porosity, microstructure and hardness of the manufactured components. The characterization of the parts was done using optical and scanning electron microscopy, x-ray diffraction and micro-hardness test. The results showed that the martensitic hot work tool steel can be manufactured with L-PBF with high densities above 99.95%. © 2022 The Authors. Published by Elsevier B.V.

Place, publisher, year, edition, pages
Elsevier, 2022
Keywords
additive manufacturing, hot work, laser powder bed fusion, microstructure, porosity, preheating temperature, process parameters, tool steel, 3D printers, Additives, Design of experiments, Martensitic stainless steel, Microhardness, Preheating, Scanning electron microscopy, Tools, Complex designs, Hot-work, Hot-work tool steel, Laser powders, Martensitics, Powder bed, Steel cans
National Category
Materials Engineering
Research subject
Physics
Identifiers
urn:nbn:se:kau:diva-92583 (URN)10.1016/j.procir.2022.08.142 (DOI)2-s2.0-85141895015 (Scopus ID)
Conference
12th CIRP Conference on Photonic Technologies, LANE 2022, 4 September 2022 through 8 September 2022
Available from: 2022-11-30 Created: 2022-11-30 Last updated: 2023-08-17Bibliographically approved
Zhirnov, I., Panahi, N., Åsberg, M. & Krakhmalev, P. (2022). Process quality assessment with imaging and acoustic monitoring during Laser Powder Bed Fusion. Paper presented at 12th CIRP Conference on Photonic Technologies, LANE 2022, 4 September 2022 through 8 September 2022. Procedia CIRP, 111, 363-367
Open this publication in new window or tab >>Process quality assessment with imaging and acoustic monitoring during Laser Powder Bed Fusion
2022 (English)In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 111, p. 363-367Article in journal (Refereed) Published
Abstract [en]

Acoustic monitoring of laser powder bed fusion (LPBF) has shown a high sensitivity to stochastic defects, e.g., cracks, pores and lack of fusion (LOF), and melting instability. The advantage of this method is the possibility to filter raw data and extract acoustic signal features for the data analysis, thus minimizing data and computing time. In this research during the build of components from hot work tool steel powder, acoustic signals and powder bed images were acquired for post-process data analysis and search for correlations with LOF. Different densities caused by LOF were obtained by changing the shielding gas velocity. In the analysis, selected combinations of features with the relationship between the build phases and the final properties such as density and surface roughness, were investigated. For the current dataset prediction of the optimal state showed an accuracy of 98%. This investigation suggests the applicability of the smart data-centric machine learning method to predict the relationship of process parameters, monitoring signals, and material properties. © 2022 The Authors. Published by Elsevier B.V.

Place, publisher, year, edition, pages
Elsevier, 2022
Keywords
acoustic features, data-centric, laser powder bed fusion, machine learning, material properties, monitoring, smart data, spattering, tool steel, Acoustic waves, Data acquisition, Data handling, Deep learning, Information analysis, Signal processing, Stochastic systems, Surface roughness, Acoustic monitoring, Acoustic signals, Data centric, Laser powders, Machine-learning, Powder bed, SMART datum
National Category
Materials Engineering
Research subject
Mechanical Engineering; Materials Engineering
Identifiers
urn:nbn:se:kau:diva-92582 (URN)10.1016/j.procir.2022.08.167 (DOI)2-s2.0-85141895463 (Scopus ID)
Conference
12th CIRP Conference on Photonic Technologies, LANE 2022, 4 September 2022 through 8 September 2022
Available from: 2022-11-30 Created: 2022-11-30 Last updated: 2022-12-07Bibliographically approved
Ma, Y., Kassler, A., Ahmed, B. S., Krakhmalev, P., Thore, A., Toyser, A. & Lindbäck, H. (2022). Using Deep Reinforcement Learning for Zero Defect Smart Forging. In: Ng A.H.C., Syberfeldt A., Hogberg D., Holm M. (Ed.), Advances in Transdisciplinary Engineering: . Paper presented at 10th Swedish Production Symposium, SPS 2022 (pp. 701-712). IOS Press, 21
Open this publication in new window or tab >>Using Deep Reinforcement Learning for Zero Defect Smart Forging
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2022 (English)In: Advances in Transdisciplinary Engineering / [ed] Ng A.H.C., Syberfeldt A., Hogberg D., Holm M., IOS Press, 2022, Vol. 21, p. 701-712Conference paper, Published paper (Refereed)
Abstract [en]

Defects during production may lead to material waste, which is a significant challenge for many companies as it reduces revenue and negatively impacts sustainability and the environment. An essential reason for material waste is a low degree of automation, especially in industries that currently have a low degree of digitalization, such as steel forging. Those industries typically rely on heavy and old machinery such as large induction ovens that are mostly controlled manually or using well-known recipes created by experts. However, standard recipes may fail when unforeseen events happen, such as an unplanned stop in production, which may lead to overheating and thus material degradation during the forging process. In this paper, we develop a digital twin-based optimization strategy for the heating process for a forging line to automate the development of an optimal control policy that adjusts the power for the heating coils in an induction oven based on temperature data observed from pyrometers. We design a digital twin-based deep reinforcement learning (DTRL) framework and train two different deep reinforcement learning (DRL) models for the heating phase using a digital twin of the forging line. The twin is based on a simulator that contains a heating transfer and movement model, which is used as an environment for the DRL training. Our evaluation shows that both models significantly reduce the temperature unevenness and can help to automate the traditional heating process.

Place, publisher, year, edition, pages
IOS Press, 2022
Keywords
smart forge, digital twin, reinforcement learning, process control, proximal policy optimization
National Category
Computer and Information Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-89788 (URN)10.3233/atde220189 (DOI)2-s2.0-85132842805 (Scopus ID)978-1-64368-268-6 (ISBN)978-1-64368-269-3 (ISBN)
Conference
10th Swedish Production Symposium, SPS 2022
Available from: 2022-05-16 Created: 2022-05-16 Last updated: 2023-01-12Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-9441-2502

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