Bionic Inspired Structures Based on Smart Materials

Creating systems with low complexity but high functionality

Welf-Guntram Drossel, Holger Kunze, Martin Ettrichrätz
JournalIndustrie 4.0 Management
Issue Volume 34, 2018, Edition 4, Pages 15-18
Open Accesshttps://doi.org/10.30844/I40M_18-4_15-18
Bibliography Share Cite Download

Abstract

Functional materials, so called smart materials, have the ability to adapt to external environmental conditions i.e. they are able to change their properties through external physical stimuli so that, they are optimally adapted to their surroundings. These processes are accompanied by energy conversion processes. These properties predestine smart materials to act as sensors and actuators. They enable high functionality in simplified structures, already on the material level. Therewith, a new and better quality can be achieved in product design.

Keywords

Bibliography

[1] Neugebauer, R.; Drossel, W.-G.; Ihlenfeldt, S.; Harzbecker, C.: Design method for machine tools with bionic inspired kinematics. In: CIRP Annals 58 (2009) 1, S. 371-374.
[2] BBC Research LLC: Smart Materials: Technologies and Global Markets. 2011.
[3] Wenzel, E.; Dziemba, O.; Langwieser, C.; Pock, B.; Sturm, D.: Die Zukunftsmatrix; Zukunftsletter der ITZ GmbH 2012, S. 138-139.
[4] Bundesministerium für Bildung und Forschung: Ideen. Innovation. Wachstum. Hightech-Strategie 2020 für Deutschland. Bonn 2010
[5] Bundesministerium für Bildung und Forschung: Abschlussbericht des Strategievorhabens „Start Smart“ des Konsortiums smart3. Dresden, 10.05.2016, BMBF-Datenbank.
[6]  Sydow, J.; van Well, B: Wissensintensiv durch Netzwerkorganisation – strukturationstheoretische Analyse eines wissensintensiven Netzwerkes. In: Sydow, S. (Hrsg): Management von Netzwerkorganisationen: Beiträge aus der Managementforschung. Wiesbaden 2006, S. 107-150.
[7]  Funken,C.;Hörlin,S.:Misstrauen als Ressource. In: OrganisationsEntwicklung 31 (2012) 1, S. 41-45.
[8]  Liu, C.; Xu, X.: Cyber-physical Machine Tool – The Era of Machine Tool 4.0. ; Procedia CIRP 63:70-5; 2017
[9]  Liang, Q.; Zhang, D.; Wu, W.; Zou, K.: Methods and Research for Multi-Component Cutting Force Sensing Devices and Approaches in Machining; Sensors (Basel, Switzerland) 16(11; 2016.
[10] Drossel, W.-G.; Gebhardt, S.; Bucht, A.; Kranz, B.; Schneider, J.; Ettrichrätz, M.: Performance of a new piezoceramic thick film sensor for measurement and control of cutting forces during milling; CIRP Annals; 2018.

Your downloads

You might also be interested in

Interoperable Data Access in Automotive Body Manufacturing

Interoperable Data Access in Automotive Body Manufacturing

Deterministic integration of structured target parameters into tact-time production
Tim Richter ORCID Icon, Robert Weidner ORCID Icon
AI has long been capable of analyzing production processes, yet why is it still so difficult to bring its insights back into production without intermediate steps? In automotive body-in-white mass production, the challenge is less a lack of data than the absence of holistic integration concepts that extend all the way to the machines. This paper demonstrates why bidirectionally communicative information systems are critical to addressing this challenge and identifies the design principles required to effectively integrate AI-generated results into production processes in the future.
Industry 4.0 Science | Volume 42 | 2026 | Edition 5 | Pages 34-42 | DOI 10.30844/I4SE.26.5.4
Generative AI in Organizational Knowledge Management

Generative AI in Organizational Knowledge Management

AI literacy as a prerequisite for augmentation
Uta Wilkens ORCID Icon, Valentin Langholf ORCID Icon, Niklas Obermann ORCID Icon
Generative Artificial Intelligence (GenAI) offers new opportunities for organizational knowledge management, particularly when it comes to learning processes at the interface between explicit, firm-specific, and tacit knowledge. Its use is therefore of particular interest for application areas such as industrial maintenance. Based on a mechanical engineering case study, this article demonstrates that augmenting both processes and employees with GenAI requires AI literacy combined with professional skills.
Industry 4.0 Science | Volume 42 | 2026 | Edition 5 | Pages 118-126 | DOI 10.30844/I4SE.26.5.14
Explaining AI in Industrial Production in an Accessible Way

Explaining AI in Industrial Production in an Accessible Way

Requirements for AI demonstrators to promote acceptance
Jennifer Link ORCID Icon, Markus Harlacher ORCID Icon, Colin Srebny, Sascha Stowasser ORCID Icon
Artificial intelligence (AI) offers a wide range of possibilities in industrial production, but it also presents challenges regarding employee acceptance. AI demonstrators are therefore of central importance, as they enable hands-on experience with AI. However, there has been a lack of systematically identified requirements for demonstrators that specifically promote acceptance and address negative emotions. Using a multi-stage research design, 69 requirements were identified, structured into functional requirements, quality requirements, and boundary conditions.
Industry 4.0 Science | Volume 42 | 2026 | Edition 5 | Pages 6-14 | DOI 10.30844/I4SE.26.5.1
AI-Based Building Inspection for Large Structures

AI-Based Building Inspection for Large Structures

A new approach to construction progress monitoring
Jan Sender, Konrad Jagusch, Michael Geist ORCID Icon, David Jericho ORCID Icon, Christian Scharr ORCID Icon
Monitoring construction progress, as required in the one-off production of large structures, is very time- and labor-intensive due to a high level of complexity and individuality. The goal of this article is to develop a sensor-based approach for capturing and evaluating multiple inspection characteristics. The use of machine learning models to detect objects and derive relevant information forms the basis for linking current condition to construction schedule. This enables a significant increase in efficiency during construction progress monitoring and a well-founded assessment of progress.
Industry 4.0 Science | Volume 42 | 2026 | Edition 5 | Pages 78-84 | DOI 10.30844/I4SE.26.5.9
Inclusive Work System Design

Inclusive Work System Design

Automation, standardization, and adaptability
Sebastian Schlund ORCID Icon
The design of inclusive work systems is gaining importance due to demographic change and the increasing digital penetration of value creation processes. While traditional ergonomic approaches are based primarily on percentile logic and thus address only a portion of the user population, the integration of digital technologies opens up new possibilities for the dynamic and individualized adaptation of work systems. This article presents a conceptual framework for inclusive work system design that integrates standardization, automation, and adaptability. Methodologically, the article is based on a conceptual analysis of existing approaches from ergonomics and human-centered design. The article makes a theoretical contribution to the systematization of inclusive work system design and identifies areas of focus for further research and industrial practice.
Industry 4.0 Science | Volume 42 | 2026 | Edition 5 | Pages 70-76 | DOI 10.30844/I4SE.26.5.8
Work Design in the Use of Autonomous Systems

Work Design in the Use of Autonomous Systems

Addressing the shortage of skilled workers
Tim Jeske ORCID Icon, Sascha Stowasser ORCID Icon, Nicole Ottersböck, Sebastian Terstegen, Rasmus Adler ORCID Icon
Companies are increasingly challenged to address shortages of skilled workers while meeting rising demands for productivity, flexibility, and innovation. Because labor supply can only be expanded to a limited extent, there is a growing focus on designing work systems with productivity in mind. Autonomous systems offer significant potential in this regard. Their implementation requires not only technical adjustments but, above all, changes in organization, skills, and work design. This article analyzes empirically grounded change requirements in existing work systems as well as associated economic potential.
Industry 4.0 Science | Volume 42 | 2026 | Edition 5 | Pages 44-50 | DOI 10.30844/I4SE.26.5.5
Christoph Brocks