Gwosch, Thomas

Matthiesen, Sven

English

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Citation: Gwosch, T.; Matthiesen, S.Reliability of Mechatronic Systemsand Machine Elements: Testing andValidation. Machines 2023, 11, 317.https://doi.org/10.3390/machines11030317Received: 13 February 2023Accepted: 20 February 2023Published: 21 February 2023Copyright: © 2023 by the authors.Licensee MDPI, Basel, Switzerland.This article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).machinesEditorialReliability of Mechatronic Systems and Machine Elements:Testing and ValidationThomas Gwosch * and Sven MatthiesenInstitute of Product Engineering, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany* Correspondence: thomas.gwosch@kit.edu1. Reliability of Complex Mechatronic SystemsThe design of reliable systems is a key challenge in product engineering [1]. Functionalreliability means the ability of a system to fulfill its required functionality under specifiedconditions for a specified period of time [2], ensuring the performance and robustnessin operation of a system. In case of complex systems, system reliability is particularlychallenging due to strong interactions between the subsystems of a single technical systemand its environment. It is therefore important to be aware of future applications of thesystem in order to take them into account during product development. This can be enabledby application data using integrated sensors and validation, verification and testing (VVT)strategies.At the system level, investigation of functionality and system modeling as well assimulation techniques are needed. Strategies for the design and operation of mechatronicsystems also require optimization methods and control approaches.On the topic of machine elements, research is needed in sensor technologies andsignal processing for machine diagnostics and prognosis. For mechatronic systems, thereare approaches for integrating sensors into common machine elements for intelligentmechatronic systems [3,4].In addition, for the system design and the development of intelligent machine ele-ments, research on VVT methods for complex mechatronic systems is needed. The role oftesting and validation is a key activity in industry, but is often underestimated in research.VVT is the knowledge base for successful engineering, both for the next generation ofproducts and for proving the functionality and reliability of the current product design.Therefore, research in the field of VVT is essential to manage the complexity of mechatronicsystems in the future. This Special Issue (SI), entitled “Reliability of Mechatronic Systemsand Machine Elements: Testing and Validation”, makes a valuable contribution to researchin this field.The Special Issue presents original research on reliability strategies and design forreliability, machine elements for intelligent mechatronic systems, and the role of testingand validation during the product development of these systems.2. Reliability Strategies and Design for ReliabilityFrom the perspective of the design of complex mechatronic systems, there is a needfor operating strategies and design methods that force system reliability and robust design.This concerns the topic areas of maintenance strategies, design optimization and controlstrategies.With a focus on reliable and robust mechatronic systems, methodical approaches andsystem modeling for machines and dynamic systems are needed to solve key challenges inproduct engineering.This SI includes contributions on preventive maintenance strategies and system re-liability, robust and optimized systems, and system control for powertrains and powersystems. Uncertainty in manufacturing is considered in some of the contributing articles.Machines 2023, 11, 317. https://doi.org/10.3390/machines11030317 https://www.mdpi.com/journal/machinesMachines 2023, 11, 317 2 of 4In [5], the selection of a preventive maintenance strategy for industrial machines isproposed based on a multi-criteria decision method. As a result, a multidimensional matrixis shown to determine the best preventive maintenance strategy for the components of amultistage machine.In [6], a method for the optimization of an additive manufacturing process is shown.The developed method, which is based on a physically informed machine learning ap-proach, helps to reduce the uncertainty of mechanical properties and enhance the qualityof printed parts, increasing system reliability.In [7], an optimization method for brushless direct current (BLDC) motors that takesmanufacturing uncertainty into account is presented. The authors demonstrated a reductionin the motor failure rate and performance variation by the proposed method.In [8], a new control strategy for heating portable fuel cell systems is presented. Thisstudy investigates reliable operation under subfreezing conditions and evaluates systemefficiency. The results were verified by simulation and experiments on a test bench.In [9], a new position control of an electrohydraulic actuator (EHA) for use in earlyproduct development stages is presented. Based on a multicriteria optimization of thecontrol in simulations, system performance was investigated in simulation and verifiedon a test bench. The study shows a good prediction of system behavior for the optimizedcontrol.3. Machine Elements for Intelligent Mechatronic SystemsMachine diagnostics and prognosis are key factors for successful and innovativemechatronic products in future. Product innovation requires knowledge about the stateof the system. This requires sensor technologies as well as prognostic methods for stateestimation.In order to manufacture intelligent mechatronic systems, sensor integration intosystem components is especially important. Therefore, research on the prognosis andhealth prediction of machine elements is necessary. This SI includes contributions to thediagnosis of gears, the investigation of bearing damage, research on the estimation ofexternal excitations and loads, and a scaling approach for drive components.In [10], the dynamic characteristics of high-contact-ratio spur gear bearing systemsare investigated. The vibrational behavior of the system is evaluated based on a proposeddynamic model with tooth spalling defects. The dynamic model is verified by experimentalresults.In [11], a scaling model for drive components is investigated. A functional investiga-tion of the drive system was conducted on a X-in-the-Loop test bench based on the couplingof a geometrically scaled drive component and powertrain. This study demonstrates a newmethod for scaled component tests.In [12], a model of a two-tooth difference swing-rod movable teeth transmissionsystem is proposed. The influence of external excitation and system load on the systemdynamics is investigated. The simulation results were verified by experiments on a testbench.In [13], a study of electrical bearing damage is presented. The authors propose a metricto evaluate damage progression using surface property data. The experimental data showa suitable approach for quantifying electrical bearing damage.In [14], the degradation effects of thin-film sensors are investigated. The investigationincludes reliability measurements after high loads, which can occur after the application of acritical component load. Based on the results, a sensor data fusion method is proposed. Thepresented results can be used for system design using these sensors in machine elements.In [15], sensor integration in an industrial gear is shown. Based on a simulation, a gearmodification was proposed and the effect on the load carrying capacity was investigated.The approach shows the trade-off between gear performance and sensor integration usedin intelligent systems.Machines 2023, 11, 317 3 of 44. The Role of Verification, Validation and Testing of Mechatronic SystemsThe papers in this SI present new approaches and related research to address thereliability challenges of mechatronic systems and machine elements. From a productengineering perspective, the role of VVT is a key aspect of reliable and robust systems.The increasing complexity of mechatronic systems encourages research of new tech-nologies and methods, especially in the field of VVT. In this context, current challengesare related to research on new reliability approaches, but also on machine elements tobe used in intelligent mechatronic systems [3,4]. Especially in human–machine systems,interactions with humans and the environment are highly sophisticated [16,17].Methods and system models are needed to study and simulate the strong interactionsbetween the subsystems of these complex systems. It is important to appreciate the valueof VVT activities in research and their relevance for successful product engineering in thefuture.Acknowledgments: We thank all our colleagues who are interested in these research topics andsubmitted their research for our SI. A special thanks is due to all reviewers for their excellent workand efforts to maintain the high quality of all contributions.Conflicts of Interest: The authors declare no conflict of interest.References1. O’Connor, P.D.T. Test Engineering: A Concise Guide to Cost-Effective Design, Development and Manufacture; Wiley: New York, NY,USA, 2001; ISBN 0471498823.2. Birolini, A. Reliability Engineering; Springer: Berlin/Heidelberg, Germany, 2004; ISBN 978-3-662-05411-6.3. Vorwerk-Handing, G.; Gwosch, T.; Schork, S.; Kirchner, E.; Matthiesen, S. Classification and examples of next generation machineelements. Forsch Ingenieurwes 2020, 84, 21–32. [CrossRef]4. Stücheli, M.; Meboldt, M. Mechatronic Machine Elements: On Their Relevance in Cyber-Physical Systems. In Smart ProductEngineering; Abramovici, M., Stark, R., Eds.; Springer: Berlin/Heidelberg, Germany, 2013; pp. 263–272. ISBN 978-3-642-30816-1.5. García, F.J.Á.; Salgado, D.R. Analysis of the Influence of Component Type and Operating Condition on the Selection of PreventiveMaintenance Strategy in Multistage Industrial Machines: A Case Study. Machines 2022, 10, 385. [CrossRef]6. Wenzel, S.; Slomski-Vetter, E.; Melz, T. Optimizing System Reliability in Additive Manufacturing Using Physics-InformedMachine Learning. Machines 2022, 10, 525. [CrossRef]7. Jeon, K.; Yoo, D.; Park, J.; Lee, K.-D.; Lee, J.-J.; Kim, C.-W. Reliability-Based Robust Design Optimization for Maximizing theOutput Torque of Brushless Direct Current (BLDC) Motors Considering Manufacturing Uncertainty. Machines 2022, 10, 797.[CrossRef]8. Zimprich, S.; Dávila-Portals, D.; Matthiesen, S.; Gwosch, T. New Control Strategy for Heating Portable Fuel Cell Power Systemsfor Energy-Efficient and Reliable Operation. Machines 2022, 10, 1159. [CrossRef]9. Dörr, M.; Leitenberger, F.; Wolter, K.; Matthiesen, S.; Gwosch, T. Model-Based Control Design of an EHA Position Control Basedon Multicriteria Optimization. Machines 2022, 10, 1190. [CrossRef]10. Cheng, Z.; Huang, K.; Xiong, Y.; Sang, M. Dynamic Analysis of a High-Contact-Ratio Spur Gear System with Localized Spallingand Experimental Validation. Machines 2022, 10, 154. [CrossRef]11. Steck, M.; Matthiesen, S.; Gwosch, T. Functional Investigation of Geometrically Scaled Drive Components by X-in-the-LoopTesting with Scaled Prototypes. Machines 2022, 10, 165. [CrossRef]12. Wei, R.; Yi, Y.; Wu, M.; Chen, M.; Jin, H. Analysis of Load Inhomogeneity of Two-Tooth Difference Swing-Rod Movable TeethTransmission System under External Excitation. Machines 2022, 10, 502. [CrossRef]13. Harder, A.; Zaiat, A.; Becker-Dombrowsky, F.M.; Puchtler, S.; Kirchner, E. Investigation of the Voltage-Induced DamageProgression on the Raceway Surfaces of Thrust Ball Bearings. Machines 2022, 10, 832. [CrossRef]14. Ottermann, R.; Steppeler, T.; Dencker, F.; Wurz, M.C. Degeneration Effects of Thin-Film Sensors after Critical Load Conditions ofMachine Components. Machines 2022, 10, 870. [CrossRef]15. Bonaiti, L.; Knoll, E.; Otto, M.; Gorla, C.; Stahl, K. The Effect of Sensor Integration on the Load Carrying Capacity of Gears.Machines 2022, 10, 888. [CrossRef]Machines 2023, 11, 317 4 of 416. Amaya-Toral, R.M.; Piña-Monarrez, M.R.; Reyes-Martínez, R.M.; de La Riva-Rodríguez, J.; Poblano-Ojinaga, E.R.; Sánchez-Leal,J.; Arredondo-Soto, K.C. Human–Machine Systems Reliability: A Series–Parallel Approach for Evaluation and Improvement inthe Field of Machine Tools. Appl. Sci. 2022, 12, 1681. [CrossRef]17. Gwosch, T.; Dörr, M.; Matthiesen, S. Modeling of Human-Machine Systems for System Reliability Testing: Investigation of theUser Impact on the Load of the Machine. In Proceedings of the 2021 Stuttgarter Symposium für Produktentwicklung, SSP 2021,Stuttgart, Germany, 20 May 2021; Fraunhofer-Institut für Arbeitswirtschaft und Organisation IAO: Stuttgart, Germany, 2021;pp. 1–10.Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individualauthor(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury topeople or property resulting from any ideas, methods, instructions or products referred to in the content.

Reliability of Mechatronic Systems and Machine Elements: Testing and Validation

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Reliability of Mechatronic Systems and Machine Elements: Testing and Validation

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