Model-based analysis, control and dosing of electroplating electrolytes

被引:0
|
作者
Alexander Leiden
Stefan Kölle
Sebastian Thiede
Klaus Schmid
Martin Metzner
Christoph Herrmann
机构
[1] Technische Universität Braunschweig,Institute of Machine Tools and Production Technology, Chair of Sustainable Manufacturing & Life Cycle Engineering
[2] Fraunhofer-Institute for Manufacturing Engineering and Automation IPA,Division Surface Engineering and Materials Technology, Department Electroplating
[3] Fraunhofer Institute of Surface Engineering and Thin Films IST,undefined
来源
The International Journal of Advanced Manufacturing Technology | 2020年 / 111卷
关键词
Electroplating; Electrolyte control; Electrolyte dosing; Cyber-physical production systems; Simulation;
D O I
暂无
中图分类号
学科分类号
摘要
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.
引用
收藏
页码:1751 / 1766
页数:15
相关论文
共 50 条
  • [21] Green power in Ontario: A dynamic model-based analysis
    Qudrat-Ullah, Hassan
    ENERGY, 2014, 77 : 859 - 870
  • [22] A model-based simulation approach to error analysis of IT services
    Wang, Long
    Sahai, Akhil
    Pruyne, James
    2007 10TH IFIP/IEEE INTERNATIONAL SYMPOSIUM ON INTEGRATED NETWORK MANAGEMENT (IM 2009), VOLS 1 AND 2, 2007, : 805 - +
  • [23] Towards a Method for Combined Model-based Testing and Analysis
    Nielsen, Brian
    PROCEEDINGS OF THE 2014 2ND INTERNATIONAL CONFERENCE ON MODEL-DRIVEN ENGINEERING AND SOFTWARE DEVELOPMENT (MODELSWARD 2014), 2014, : 609 - 618
  • [24] Analysis of the model-based corrector approach for explicit cosimulation
    Haid, Timo
    Watzenig, Daniel
    Stettinger, Georg
    MULTIBODY SYSTEM DYNAMICS, 2022, 55 (1-2) : 137 - 163
  • [25] Model-based analysis of convective grain drying processes
    Stakic, M
    Tsotsas, E
    DRYING TECHNOLOGY, 2005, 23 (9-11) : 1895 - 1908
  • [26] Model-Based Energy Efficiency Analysis of Software Architectures
    Stier, Christian
    Koziolek, Anne
    Groenda, Henning
    Reussner, Ralf
    SOFTWARE ARCHITECTURE (ECSA 2015), 2015, 9278 : 221 - 238
  • [27] Model-Based Computation
    Beebe, Cameron
    UNCONVENTIONAL COMPUTATION AND NATURAL COMPUTATION, UCNC 2016, 2016, 9726 : 75 - 86
  • [28] Automated Maneuvering in Confined Waters using Parameter Space Model and Model-based Control
    Kurowski, Martin
    Schubert, Agnes
    Jeinsch, Torsten
    IFAC PAPERSONLINE, 2020, 53 (02): : 14495 - 14500
  • [29] Research of model-based Aeroengine Control System Design Structure and Workflow
    Zhang, Dong
    Lu, Jin-zhi
    Wang, Lin
    Li, Jun
    2014 ASIA-PACIFIC INTERNATIONAL SYMPOSIUM ON AEROSPACE TECHNOLOGY, APISAT2014, 2015, 99 : 788 - 794
  • [30] Model-based control of a molten carbonate fuel cell (MCFC) process
    Kim, Tae Young
    Kim, Beom Suk
    Park, Tae Chang
    Yeo, Yeong Koo
    KOREAN JOURNAL OF CHEMICAL ENGINEERING, 2018, 35 (01) : 118 - 128