---
_id: '10787'
abstract:
- lang: eng
  text: Cyber-physical production systems have emerged with the rise of Industry 4.0
    in different industrial fields. Especially the food sector, where inhomogeneous
    input products like beer/yeast suspensions with different qualities and properties
    have yet slowed down automation, has potential for this evolution. This contribution
    presents optimization methods for a dynamical cross-flow filtration plant which
    is driven by an advanced control concept in combination with data driven product
    monitoring via inline near infrared spectroscopy (NIR) in order to improve energy
    savings and filtration performance. Using a hierarchical control and optimization
    structure, the non stationary batch process is steered towards a high production
    rate with low energy consumption for a variety of different input products.
author:
- first_name: Jörn
  full_name: Tebbe, Jörn
  id: '79072'
  last_name: Tebbe
- first_name: Thomas
  full_name: Pawlik, Thomas
  id: '58915'
  last_name: Pawlik
- first_name: Marc
  full_name: Trilling-Haasler, Marc
  id: '81622'
  last_name: Trilling-Haasler
  orcid: 0000-0002-3685-6383
- first_name: Jannis
  full_name: Löbner, Jannis
  id: '74097'
  last_name: Löbner
- first_name: Markus
  full_name: Lange-Hegermann, Markus
  id: '71761'
  last_name: Lange-Hegermann
- first_name: Jan
  full_name: Schneider, Jan
  id: '13209'
  last_name: Schneider
  orcid: 0000-0001-6401-8873
citation:
  ama: Tebbe J, Pawlik T, Trilling-Haasler M, Löbner J, Lange-Hegermann M, Schneider
    J. <i>Holistic Optimization of a Dynamic Cross-Flow Filtration Process towards
    a Cyber-Physical System</i>. (Jasperneite J, Wisniewski L, Fung Man K, Institute
    of Electrical and Electronics Engineers , eds.). IEEE; 2023:1-7. doi:<a href="https://doi.org/10.1109/INDIN51400.2023.10217913">10.1109/INDIN51400.2023.10217913</a>
  apa: Tebbe, J., Pawlik, T., Trilling-Haasler, M., Löbner, J., Lange-Hegermann, M.,
    &#38; Schneider, J. (2023). Holistic optimization of a dynamic cross-flow filtration
    process towards a cyber-physical system. In J. Jasperneite, L. Wisniewski, K.
    Fung Man, &#38; Institute of Electrical and Electronics Engineers  (Eds.), <i>2023
    IEEE 21st International Conference on Industrial Informatics (INDIN)</i> (pp.
    1–7). IEEE. <a href="https://doi.org/10.1109/INDIN51400.2023.10217913">https://doi.org/10.1109/INDIN51400.2023.10217913</a>
  bjps: '<b>Tebbe J <i>et al.</i></b> (2023) <i>Holistic Optimization of a Dynamic
    Cross-Flow Filtration Process towards a Cyber-Physical System</i>, Jasperneite
    J et al. (eds). [Piscataway, NJ]: IEEE.'
  chicago: 'Tebbe, Jörn, Thomas Pawlik, Marc Trilling-Haasler, Jannis Löbner, Markus
    Lange-Hegermann, and Jan Schneider. <i>Holistic Optimization of a Dynamic Cross-Flow
    Filtration Process towards a Cyber-Physical System</i>. Edited by Jürgen Jasperneite,
    Lukasz Wisniewski, Kim Fung Man, and Institute of Electrical and Electronics Engineers
    . <i>2023 IEEE 21st International Conference on Industrial Informatics (INDIN)</i>.
    [Piscataway, NJ]: IEEE, 2023. <a href="https://doi.org/10.1109/INDIN51400.2023.10217913">https://doi.org/10.1109/INDIN51400.2023.10217913</a>.'
  chicago-de: 'Tebbe, Jörn, Thomas Pawlik, Marc Trilling-Haasler, Jannis Löbner, Markus
    Lange-Hegermann und Jan Schneider. 2023. <i>Holistic optimization of a dynamic
    cross-flow filtration process towards a cyber-physical system</i>. Hg. von Jürgen
    Jasperneite, Lukasz Wisniewski, Kim Fung Man, und Institute of Electrical and
    Electronics Engineers . <i>2023 IEEE 21st International Conference on Industrial
    Informatics (INDIN)</i>. [Piscataway, NJ]: IEEE. doi:<a href="https://doi.org/10.1109/INDIN51400.2023.10217913">10.1109/INDIN51400.2023.10217913</a>,
    .'
  din1505-2-1: '<span style="font-variant:small-caps;">Tebbe, Jörn</span> ; <span
    style="font-variant:small-caps;">Pawlik, Thomas</span> ; <span style="font-variant:small-caps;">Trilling-Haasler,
    Marc</span> ; <span style="font-variant:small-caps;">Löbner, Jannis</span> ; <span
    style="font-variant:small-caps;">Lange-Hegermann, Markus</span> ; <span style="font-variant:small-caps;">Schneider,
    Jan</span> ; <span style="font-variant:small-caps;">Jasperneite, J.</span> ; <span
    style="font-variant:small-caps;">Wisniewski, L.</span> ; <span style="font-variant:small-caps;">Fung
    Man, K.</span> ; <span style="font-variant:small-caps;">Institute of Electrical
    and Electronics Engineers </span> (Hrsg.): <i>Holistic optimization of a dynamic
    cross-flow filtration process towards a cyber-physical system</i>. [Piscataway,
    NJ] : IEEE, 2023'
  havard: J. Tebbe, T. Pawlik, M. Trilling-Haasler, J. Löbner, M. Lange-Hegermann,
    J. Schneider, Holistic optimization of a dynamic cross-flow filtration process
    towards a cyber-physical system, IEEE, [Piscataway, NJ], 2023.
  ieee: 'J. Tebbe, T. Pawlik, M. Trilling-Haasler, J. Löbner, M. Lange-Hegermann,
    and J. Schneider, <i>Holistic optimization of a dynamic cross-flow filtration
    process towards a cyber-physical system</i>. [Piscataway, NJ]: IEEE, 2023, pp.
    1–7. doi: <a href="https://doi.org/10.1109/INDIN51400.2023.10217913">10.1109/INDIN51400.2023.10217913</a>.'
  mla: Tebbe, Jörn, et al. “Holistic Optimization of a Dynamic Cross-Flow Filtration
    Process towards a Cyber-Physical System.” <i>2023 IEEE 21st International Conference
    on Industrial Informatics (INDIN)</i>, edited by Jürgen Jasperneite et al., IEEE,
    2023, pp. 1–7, <a href="https://doi.org/10.1109/INDIN51400.2023.10217913">https://doi.org/10.1109/INDIN51400.2023.10217913</a>.
  short: J. Tebbe, T. Pawlik, M. Trilling-Haasler, J. Löbner, M. Lange-Hegermann,
    J. Schneider, Holistic Optimization of a Dynamic Cross-Flow Filtration Process
    towards a Cyber-Physical System, IEEE, [Piscataway, NJ], 2023.
  ufg: '<b>Tebbe, Jörn u. a.</b>: Holistic optimization of a dynamic cross-flow filtration
    process towards a cyber-physical system, hg. von Jasperneite, Jürgen u. a., [Piscataway,
    NJ] 2023.'
  van: 'Tebbe J, Pawlik T, Trilling-Haasler M, Löbner J, Lange-Hegermann M, Schneider
    J. Holistic optimization of a dynamic cross-flow filtration process towards a
    cyber-physical system. Jasperneite J, Wisniewski L, Fung Man K, Institute of Electrical
    and Electronics Engineers , editors. 2023 IEEE 21st International Conference on
    Industrial Informatics (INDIN). [Piscataway, NJ]: IEEE; 2023.'
conference:
  end_date: 2023-07-20
  location: Lemgo
  name: 21st International Conference on Industrial Informatics ; INDIN 2023
  start_date: 2023-07-17
corporate_editor:
- 'Institute of Electrical and Electronics Engineers '
date_created: 2023-11-21T08:04:41Z
date_updated: 2025-06-26T07:48:22Z
department:
- _id: DEP4018
- _id: DEP1308
- _id: DEP4028
doi: 10.1109/INDIN51400.2023.10217913
editor:
- first_name: Jürgen
  full_name: Jasperneite, Jürgen
  id: '1899'
  last_name: Jasperneite
- first_name: Lukasz
  full_name: Wisniewski, Lukasz
  id: '1710'
  last_name: Wisniewski
- first_name: Kim
  full_name: Fung Man, Kim
  last_name: Fung Man
keyword:
- Spectroscopy
- Production systems
- Filtration
- Velocity control
- Optimization methods
- Cyber-physical systems
- Nonhomogeneous media
language:
- iso: eng
page: 1-7
place: '[Piscataway, NJ]'
publication: 2023 IEEE 21st International Conference on Industrial Informatics (INDIN)
publication_identifier:
  eisbn:
  - 978-1-6654-9313-0
  isbn:
  - '978-1-6654-9314-7 '
  issn:
  - 1935-4576
publication_status: published
publisher: IEEE
status: public
title: Holistic optimization of a dynamic cross-flow filtration process towards a
  cyber-physical system
type: conference_editor_article
user_id: '83781'
year: '2023'
...
---
_id: '12806'
abstract:
- lang: eng
  text: Cyber-Physical Systems (CPS) play an essential role in today’s production
    processes, leveraging Artificial Intelligence (AI) to enhance operations such
    as optimization, anomaly detection, and predictive maintenance. This article reviews
    a cognitive architecture for Artificial Intelligence, which has been developed
    to establish a standard framework for integrating AI solutions into existing production
    processes. Given that machines in these processes continuously generate large
    streams of data, Online Machine Learning (OML) is identified as a crucial extension
    to the existing architecture. To substantiate this claim, real-world experiments
    using a slitting machine are conducted, to compare the performance of OML to traditional
    Batch Machine Learning. The assessment of contemporary OML algorithms using a
    real production system is a fundamental innovation in this research. The evaluations
    clearly indicate that OML adds significant value to CPS, and it is strongly recommended
    as an extension of related architectures, such as the cognitive architecture for
    AI discussed in this article. Additionally, surrogate-model-based optimization
    is employed, to determine the optimal hyperparameter settings for the corresponding
    OML algorithms, aiming to achieve peak performance in their respective tasks.
article_number: '11506'
author:
- first_name: Alexander
  full_name: Hinterleitner, Alexander
  last_name: Hinterleitner
- first_name: Richard
  full_name: Schulz, Richard
  last_name: Schulz
- first_name: Lukas
  full_name: Hans, Lukas
  last_name: Hans
- first_name: Aleksandr
  full_name: Subbotin, Aleksandr
  last_name: Subbotin
- first_name: Nils
  full_name: Barthel, Nils
  last_name: Barthel
- first_name: Noah
  full_name: Pütz, Noah
  last_name: Pütz
- first_name: Martin
  full_name: Rosellen, Martin
  last_name: Rosellen
- first_name: Thomas
  full_name: Bartz-Beielstein, Thomas
  last_name: Bartz-Beielstein
- first_name: Christoph
  full_name: Geng, Christoph
  id: '61408'
  last_name: Geng
- first_name: Phillip
  full_name: Priss, Phillip
  last_name: Priss
citation:
  ama: 'Hinterleitner A, Schulz R, Hans L, et al. Online Machine Learning and Surrogate-Model-Based
    Optimization for Improved Production Processes Using a Cognitive Architecture.
    <i>  Applied Sciences : open access journal</i>. 2023;13(20). doi:<a href="https://doi.org/10.3390/app132011506">10.3390/app132011506</a>'
  apa: 'Hinterleitner, A., Schulz, R., Hans, L., Subbotin, A., Barthel, N., Pütz,
    N., Rosellen, M., Bartz-Beielstein, T., Geng, C., &#38; Priss, P. (2023). Online
    Machine Learning and Surrogate-Model-Based Optimization for Improved Production
    Processes Using a Cognitive Architecture. <i>  Applied Sciences : Open Access
    Journal</i>, <i>13</i>(20), Article 11506. <a href="https://doi.org/10.3390/app132011506">https://doi.org/10.3390/app132011506</a>'
  bjps: '<b>Hinterleitner A <i>et al.</i></b> (2023) Online Machine Learning and Surrogate-Model-Based
    Optimization for Improved Production Processes Using a Cognitive Architecture.
    <i>  Applied Sciences : open access journal</i> <b>13</b>.'
  chicago: 'Hinterleitner, Alexander, Richard Schulz, Lukas Hans, Aleksandr Subbotin,
    Nils Barthel, Noah Pütz, Martin Rosellen, Thomas Bartz-Beielstein, Christoph Geng,
    and Phillip Priss. “Online Machine Learning and Surrogate-Model-Based Optimization
    for Improved Production Processes Using a Cognitive Architecture.” <i>  Applied
    Sciences : Open Access Journal</i> 13, no. 20 (2023). <a href="https://doi.org/10.3390/app132011506">https://doi.org/10.3390/app132011506</a>.'
  chicago-de: 'Hinterleitner, Alexander, Richard Schulz, Lukas Hans, Aleksandr Subbotin,
    Nils Barthel, Noah Pütz, Martin Rosellen, Thomas Bartz-Beielstein, Christoph Geng
    und Phillip Priss. 2023. Online Machine Learning and Surrogate-Model-Based Optimization
    for Improved Production Processes Using a Cognitive Architecture. <i>  Applied
    Sciences : open access journal</i> 13, Nr. 20. doi:<a href="https://doi.org/10.3390/app132011506">10.3390/app132011506</a>,
    .'
  din1505-2-1: '<span style="font-variant:small-caps;"><span style="font-variant:small-caps;">Hinterleitner,
    Alexander</span> ; <span style="font-variant:small-caps;">Schulz, Richard</span>
    ; <span style="font-variant:small-caps;">Hans, Lukas</span> ; <span style="font-variant:small-caps;">Subbotin,
    Aleksandr</span> ; <span style="font-variant:small-caps;">Barthel, Nils</span>
    ; <span style="font-variant:small-caps;">Pütz, Noah</span> ; <span style="font-variant:small-caps;">Rosellen,
    Martin</span> ; <span style="font-variant:small-caps;">Bartz-Beielstein, Thomas</span>
    ; u. a.</span>: Online Machine Learning and Surrogate-Model-Based Optimization
    for Improved Production Processes Using a Cognitive Architecture. In: <i>  Applied
    Sciences : open access journal</i> Bd. 13. Basel, MDPI AG (2023), Nr. 20'
  havard: 'A. Hinterleitner, R. Schulz, L. Hans, A. Subbotin, N. Barthel, N. Pütz,
    M. Rosellen, T. Bartz-Beielstein, C. Geng, P. Priss, Online Machine Learning and
    Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive
    Architecture,   Applied Sciences : Open Access Journal. 13 (2023).'
  ieee: 'A. Hinterleitner <i>et al.</i>, “Online Machine Learning and Surrogate-Model-Based
    Optimization for Improved Production Processes Using a Cognitive Architecture,”
    <i>  Applied Sciences : open access journal</i>, vol. 13, no. 20, Art. no. 11506,
    2023, doi: <a href="https://doi.org/10.3390/app132011506">10.3390/app132011506</a>.'
  mla: 'Hinterleitner, Alexander, et al. “Online Machine Learning and Surrogate-Model-Based
    Optimization for Improved Production Processes Using a Cognitive Architecture.”
    <i>  Applied Sciences : Open Access Journal</i>, vol. 13, no. 20, 11506, 2023,
    <a href="https://doi.org/10.3390/app132011506">https://doi.org/10.3390/app132011506</a>.'
  short: 'A. Hinterleitner, R. Schulz, L. Hans, A. Subbotin, N. Barthel, N. Pütz,
    M. Rosellen, T. Bartz-Beielstein, C. Geng, P. Priss,   Applied Sciences : Open
    Access Journal 13 (2023).'
  ufg: '<b>Hinterleitner, Alexander u. a.</b>: Online Machine Learning and Surrogate-Model-Based
    Optimization for Improved Production Processes Using a Cognitive Architecture,
    in: <i>  Applied Sciences : open access journal</i> 13 (2023), H. 20.'
  van: 'Hinterleitner A, Schulz R, Hans L, Subbotin A, Barthel N, Pütz N, et al. Online
    Machine Learning and Surrogate-Model-Based Optimization for Improved Production
    Processes Using a Cognitive Architecture.   Applied Sciences : open access journal.
    2023;13(20).'
date_created: 2025-04-16T07:27:52Z
date_updated: 2025-06-26T07:50:56Z
department:
- _id: DEP5023
doi: 10.3390/app132011506
external_id:
  isi:
  - '001096019200001'
intvolume: '        13'
isi: '1'
issue: '20'
keyword:
- machine learning
- online algorithms
- cyber-physical production systems
- surrogate-based optimization
language:
- iso: eng
place: Basel
publication: '  Applied Sciences : open access journal'
publication_identifier:
  issn:
  - 2076-3417
publication_status: published
publisher: MDPI AG
status: public
title: Online Machine Learning and Surrogate-Model-Based Optimization for Improved
  Production Processes Using a Cognitive Architecture
type: scientific_journal_article
user_id: '83781'
volume: 13
year: '2023'
...
---
_id: '12790'
abstract:
- lang: eng
  text: n the last years, Additive Manufacturing, thanks to its capability of continuous
    improvements in performance and cost-efficiency, was able to partly replace and
    redefine well-established manufacturing processes. This research is based on the
    idea to achieve great cost and operational benefits especially in the field of
    tool making for injection molding by combining traditional and additive manufacturing
    in one process chain. Special attention is given to the surface quality in terms
    of surface roughness and its optimization directly in the Selective Laser Melting
    process. This article presents the possibility for a remelting process of the
    SLM parts as a way to optimize the surfaces of the produced parts. The influence
    of laser remelting on the surface roughness of the parts is analyzed while varying
    machine parameters like laser power and scan settings. Laser remelting with optimized
    parameter settings considerably improves the surface quality of SLM parts and
    is a great starting point for further post-processing techniques, which require
    a low initial value of surface roughness.
author:
- first_name: Filippo
  full_name: Simoni, Filippo
  last_name: Simoni
- first_name: Andrea
  full_name: Huxol, Andrea
  id: '43559'
  last_name: Huxol
- first_name: Franz-Josef
  full_name: Villmer, Franz-Josef
  id: '14290'
  last_name: Villmer
citation:
  ama: Simoni F, Huxol A, Villmer FJ. Improving surface quality in selective laser
    melting based tool making. <i>Journal of Intelligent Manufacturing</i>. 2021;32(7):1927-1938.
    doi:<a href="https://doi.org/10.1007/s10845-021-01744-9">10.1007/s10845-021-01744-9</a>
  apa: Simoni, F., Huxol, A., &#38; Villmer, F.-J. (2021). Improving surface quality
    in selective laser melting based tool making. <i>Journal of Intelligent Manufacturing</i>,
    <i>32</i>(7), 1927–1938. <a href="https://doi.org/10.1007/s10845-021-01744-9">https://doi.org/10.1007/s10845-021-01744-9</a>
  bjps: <b>Simoni F, Huxol A and Villmer F-J</b> (2021) Improving Surface Quality
    in Selective Laser Melting Based Tool Making. <i>Journal of Intelligent Manufacturing</i>
    <b>32</b>, 1927–1938.
  chicago: 'Simoni, Filippo, Andrea Huxol, and Franz-Josef Villmer. “Improving Surface
    Quality in Selective Laser Melting Based Tool Making.” <i>Journal of Intelligent
    Manufacturing</i> 32, no. 7 (2021): 1927–38. <a href="https://doi.org/10.1007/s10845-021-01744-9">https://doi.org/10.1007/s10845-021-01744-9</a>.'
  chicago-de: 'Simoni, Filippo, Andrea Huxol und Franz-Josef Villmer. 2021. Improving
    surface quality in selective laser melting based tool making. <i>Journal of Intelligent
    Manufacturing</i> 32, Nr. 7: 1927–1938. doi:<a href="https://doi.org/10.1007/s10845-021-01744-9">10.1007/s10845-021-01744-9</a>,
    .'
  din1505-2-1: '<span style="font-variant:small-caps;">Simoni, Filippo</span> ; <span
    style="font-variant:small-caps;">Huxol, Andrea</span> ; <span style="font-variant:small-caps;">Villmer,
    Franz-Josef</span>: Improving surface quality in selective laser melting based
    tool making. In: <i>Journal of Intelligent Manufacturing</i> Bd. 32. Dordrecht
    [u.a.], Springer Science and Business Media (2021), Nr. 7, S. 1927–1938'
  havard: F. Simoni, A. Huxol, F.-J. Villmer, Improving surface quality in selective
    laser melting based tool making, Journal of Intelligent Manufacturing. 32 (2021)
    1927–1938.
  ieee: 'F. Simoni, A. Huxol, and F.-J. Villmer, “Improving surface quality in selective
    laser melting based tool making,” <i>Journal of Intelligent Manufacturing</i>,
    vol. 32, no. 7, pp. 1927–1938, 2021, doi: <a href="https://doi.org/10.1007/s10845-021-01744-9">10.1007/s10845-021-01744-9</a>.'
  mla: Simoni, Filippo, et al. “Improving Surface Quality in Selective Laser Melting
    Based Tool Making.” <i>Journal of Intelligent Manufacturing</i>, vol. 32, no.
    7, 2021, pp. 1927–38, <a href="https://doi.org/10.1007/s10845-021-01744-9">https://doi.org/10.1007/s10845-021-01744-9</a>.
  short: F. Simoni, A. Huxol, F.-J. Villmer, Journal of Intelligent Manufacturing
    32 (2021) 1927–1938.
  ufg: '<b>Simoni, Filippo/Huxol, Andrea/Villmer, Franz-Josef</b>: Improving surface
    quality in selective laser melting based tool making, in: <i>Journal of Intelligent
    Manufacturing</i> 32 (2021), H. 7,  S. 1927–1938.'
  van: Simoni F, Huxol A, Villmer FJ. Improving surface quality in selective laser
    melting based tool making. Journal of Intelligent Manufacturing. 2021;32(7):1927–38.
date_created: 2025-04-15T09:17:22Z
date_updated: 2025-06-26T13:41:37Z
department:
- _id: DEP7000
doi: 10.1007/s10845-021-01744-9
external_id:
  isi:
  - '000636136100001'
intvolume: '        32'
isi: '1'
issue: '7'
keyword:
- Direct rapid tooling
- Toolmaking
- Additive manufacturing process chain
- Process control
- Production systems
- Selective laser melting
- Surface roughness
- Laser surface remelting
language:
- iso: eng
page: 1927-1938
place: Dordrecht [u.a.]
publication: Journal of Intelligent Manufacturing
publication_identifier:
  eissn:
  - 1572-8145
  issn:
  - 0956-5515
publication_status: published
publisher: Springer Science and Business Media
status: public
title: Improving surface quality in selective laser melting based tool making
type: scientific_journal_article
user_id: '83781'
volume: 32
year: '2021'
...
---
_id: '12792'
abstract:
- lang: eng
  text: Additive Manufacturing has arisen as a ground-breaking set of technologies
    that, thanks to their capability of continuous improvements in performance and
    cost-efficiency, was able in the last years to replace well-established manufacturing
    processes. Proficiency in the fabrication of highly complex parts forced this
    astonishing development. This research is based on the idea that through the integration
    of additive and conventional manufacturing technologies it is possible to achieve
    great cost and operational benefits especially in the field of tool making for
    injection molding. Such an integrated manufacturing solution could overcome the
    limitations of independent additive, subtractive, and post-processing procedures
    by strengthening their potentialities. The present study highlights the opportunities
    of a synergy between the above-mentioned manufacturing technologies for the optimized
    fabrication of injection molds. An additive manufacturing process chain is presented,
    and special attention is given to the surface quality and its optimization directly
    in the Selective Laser Melting process. The potentialities of the Laser Surface
    Re-melting technique are analyzed, and the process optimization leads to a reduction
    of 45% of the average roughness directly in the SLM process. (C) 2019, IFAC (International
    Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
author:
- first_name: Filippo
  full_name: Simoni, Filippo
  last_name: Simoni
- first_name: Andrea
  full_name: Huxol, Andrea
  id: '43559'
  last_name: Huxol
- first_name: Franz-Josef
  full_name: Villmer, Franz-Josef
  id: '14290'
  last_name: Villmer
citation:
  ama: Simoni F, Huxol A, Villmer FJ. <i>Approach Towards Surface Improvement in Additively
    Manufactured Tools</i>. Vol 52. Elsevier BV; 2019:254-259. doi:<a href="https://doi.org/10.1016/j.ifacol.2019.10.032">10.1016/j.ifacol.2019.10.032</a>
  apa: Simoni, F., Huxol, A., &#38; Villmer, F.-J. (2019). Approach Towards Surface
    Improvement in Additively Manufactured Tools. In <i>13th International-Federation-of-Automatic-Control
    (IFAC) Workshop on Intelligent Manufacturing Systems (IMS)</i> (Vol. 52, pp. 254–259).
    Elsevier BV. <a href="https://doi.org/10.1016/j.ifacol.2019.10.032">https://doi.org/10.1016/j.ifacol.2019.10.032</a>
  bjps: '<b>Simoni F, Huxol A and Villmer F-J</b> (2019) <i>Approach Towards Surface
    Improvement in Additively Manufactured Tools</i>. Amsterdam [u.a.]: Elsevier BV.'
  chicago: 'Simoni, Filippo, Andrea Huxol, and Franz-Josef Villmer. <i>Approach Towards
    Surface Improvement in Additively Manufactured Tools</i>. <i>13th International-Federation-of-Automatic-Control
    (IFAC) Workshop on Intelligent Manufacturing Systems (IMS)</i>. Vol. 52. IFAC-PapersOnLine.
    Amsterdam [u.a.]: Elsevier BV, 2019. <a href="https://doi.org/10.1016/j.ifacol.2019.10.032">https://doi.org/10.1016/j.ifacol.2019.10.032</a>.'
  chicago-de: 'Simoni, Filippo, Andrea Huxol und Franz-Josef Villmer. 2019. <i>Approach
    Towards Surface Improvement in Additively Manufactured Tools</i>. <i>13th International-Federation-of-Automatic-Control
    (IFAC) Workshop on Intelligent Manufacturing Systems (IMS)</i>. Bd. 52. IFAC-PapersOnLine.
    Amsterdam [u.a.]: Elsevier BV. doi:<a href="https://doi.org/10.1016/j.ifacol.2019.10.032">10.1016/j.ifacol.2019.10.032</a>,
    .'
  din1505-2-1: '<span style="font-variant:small-caps;">Simoni, Filippo</span> ; <span
    style="font-variant:small-caps;">Huxol, Andrea</span> ; <span style="font-variant:small-caps;">Villmer,
    Franz-Josef</span>: <i>Approach Towards Surface Improvement in Additively Manufactured
    Tools</i>, <i>IFAC-PapersOnLine</i>. Bd. 52. Amsterdam [u.a.] : Elsevier BV, 2019'
  havard: F. Simoni, A. Huxol, F.-J. Villmer, Approach Towards Surface Improvement
    in Additively Manufactured Tools, Elsevier BV, Amsterdam [u.a.], 2019.
  ieee: 'F. Simoni, A. Huxol, and F.-J. Villmer, <i>Approach Towards Surface Improvement
    in Additively Manufactured Tools</i>, vol. 52. Amsterdam [u.a.]: Elsevier BV,
    2019, pp. 254–259. doi: <a href="https://doi.org/10.1016/j.ifacol.2019.10.032">10.1016/j.ifacol.2019.10.032</a>.'
  mla: Simoni, Filippo, et al. “Approach Towards Surface Improvement in Additively
    Manufactured Tools.” <i>13th International-Federation-of-Automatic-Control (IFAC)
    Workshop on Intelligent Manufacturing Systems (IMS)</i>, vol. 52, Elsevier BV,
    2019, pp. 254–59, <a href="https://doi.org/10.1016/j.ifacol.2019.10.032">https://doi.org/10.1016/j.ifacol.2019.10.032</a>.
  short: F. Simoni, A. Huxol, F.-J. Villmer, Approach Towards Surface Improvement
    in Additively Manufactured Tools, Elsevier BV, Amsterdam [u.a.], 2019.
  ufg: '<b>Simoni, Filippo/Huxol, Andrea/Villmer, Franz-Josef</b>: Approach Towards
    Surface Improvement in Additively Manufactured Tools, Bd. 52, Amsterdam [u.a.]
    2019 (IFAC-PapersOnLine).'
  van: 'Simoni F, Huxol A, Villmer FJ. Approach Towards Surface Improvement in Additively
    Manufactured Tools. 13th International-Federation-of-Automatic-Control (IFAC)
    Workshop on Intelligent Manufacturing Systems (IMS). Amsterdam [u.a.]: Elsevier
    BV; 2019. (IFAC-PapersOnLine; vol. 52).'
conference:
  end_date: 2019-08-14
  location: Oshawa, CANADA
  name: 13th International-Federation-of-Automatic-Control (IFAC) Workshop on Intelligent
    Manufacturing Systems (IMS)
  start_date: 2019-08-12
date_created: 2025-04-15T09:32:02Z
date_updated: 2025-06-26T13:40:36Z
department:
- _id: DEP7000
doi: 10.1016/j.ifacol.2019.10.032
intvolume: '        52'
keyword:
- Direct rapid tooling
- toolmaking
- additive manufacturing process chain
- process control
- production systems
- selective laser melting
- surface roughness
- laser surface re-melting
language:
- iso: eng
page: 254-259
place: Amsterdam [u.a.]
publication: 13th International-Federation-of-Automatic-Control (IFAC) Workshop on
  Intelligent Manufacturing Systems (IMS)
publication_identifier:
  issn:
  - 2405-8963
publication_status: published
publisher: Elsevier BV
series_title: IFAC-PapersOnLine
status: public
title: Approach Towards Surface Improvement in Additively Manufactured Tools
type: conference_editor_article
user_id: '83781'
volume: 52
year: '2019'
...