An Approach to Describe Gentelligent Components in their Life Cycle

Lachmayer, Roland; Mozgova, Iryna; Scheidel, Wieben

doi:10.1016/j.protcy.2016.08.027

Cited by 8 publications

(6 citation statements)

References 4 publications

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“…Generally, the requirements are manufacturing, flexibility, environment, the best fit for technical inheritance information use, and the development description. The classification key of collected data is divided into two parts mainly [37]. The first part is common for all components of the current generation and the second part considers the individual information.…”

Section: Gentelligent Productsmentioning

confidence: 99%

The Impact of Industry 4.0 on the Future of Green Supply Chain

Bányai¹,

Akkad²

2021

Green Supply Chain - Competitiveness and Sustainability

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The fourth industrial revolution offers new technologies to transform conventional supply chain solutions into cyber-physical supply chain ones. This transformation makes it possible to increase the efficiency, availability, quality, and cost-efficiency of the value-making chain, while the energy consumption and the GHG emission can be decreased. Within the frame of this chapter, the authors introduce the most important Industry 4.0 technologies and Internet of Things tools and demonstrate their potentials to update supply chain operations. This update of conventional operations can lead to greener and more sustainable purchasing, production, and distribution processes. The successful future of the green supply chain is based on a wide range of factors, like production management, logistics management, societal and regulatory environment. However, the Industry 4.0 technologies are expected to strongly influence the whole supply chain performance positively. This chapter aims to explore the potentials of Industry 4.0 technologies and the transformation of conventional supply chain solutions into cyber-physical systems, especially from a municipal waste collection point of view. The research findings can provide useful insights for supply chain experts, manufacturing, and service companies.

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Section: Gentelligent Productsmentioning

confidence: 99%

The Impact of Industry 4.0 on the Future of Green Supply Chain

Bányai¹,

Akkad²

2021

Green Supply Chain - Competitiveness and Sustainability

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“…From a product development point of view, these integrated sensors enable a robust data acquisition for the product generation development (Welzbacher et al, 2023). Those sensor-integrated structural components for product generation development could be described as "gentelligent" components in context of technical inheritance, where new product generations are optimized for the loads during the use phase of data from previous product generations (Lachmayer et al, 2014;Lachmayer et al, 2016). Especially composite materials, which are often used in the aircraft industry, and their layup are well suited for the integration of sensors into the material itself like it is shown in Klein and Middendorf (2016) and Damm et al (2020).…”

Section: Introductionmentioning

confidence: 99%

Design methodology for optimal sensor placement for cure monitoring and load detection of sensor-integrated, gentelligent composite parts

Meyer zu Westerhausen,

Kyriazis,

Hühne

et al. 2024

Proc. Des. Soc.

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Selecting right positions for composite-integrated sensors for monitoring cure during manufacturing and loads during product use presents challenges for engineering design. Since an optimal sensor placement (OSP) methodology for both phases is not emphasised enough in literature, a new methodology is proposed. This methodology is based on a Genetic Algorithm and strain gauges, temperature sensors and interdigitated electrode sensors for cure monitoring and physics-informed neural network-based load detection. Additionally, it includes sensor node positions optimization in a sensor network.

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“…Especially when the sensors or the WSN are directly integrated into the component, like e. g. into the layup of a composite, the load information becomes an integral part of it and is 2 "inherited" from one product generation to the next. Therefore, such components are called as gentelligent in the paradigm of technical inheritance, which were detailed investigated in the collaborative research center 653 [8,9]. Considering the aircraft wing in Figure 1 as such gentelligent component, the WSN fulfills its function by measuring the strains over time.…”

Section: Introductionmentioning

confidence: 99%

Reliability Assessment of Wireless Sensor Networks by strain-based Region Analysis for Redundancy Estimation in Measurements on the Example of an Aircraft Wing Box

Meyer zu Westerhausen,

Raveendran,

Lauth

et al. 2024

Preprint

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Wireless Sensor networks (WSNs) are gaining more and more research interest due to their ability to monitor large areas more or less independent. However, their reliability is an important issue, since their system reliability is influenced from a hardware, a data and an energetic point of view due to different factors like loading conditions, signal attenuation and battery lifetime. Regarding these issues, the positions of sensor nodes have to be chosen correctly to maximize the system reliability, which has to be considered during the development of new products equipped with WSNs. In this paper, an approach to estimate a WSNs system reliability during the development phase based on the analysis of the measurements on the example of strain measurements in finite elements (FE) models is presented. For that, the part under consideration is divided into regions with similar strains by the use of a Region Growing Algorithm (RGA). Afterwards, the WSN’s configuration is analyzed regarding its reliability in accordance to the data paths and measurement redundancy due to the sensor positions in the found measuring regions. This methodology is tested on an exemplary WSN configuration at an aircraft wing box under bending load and was found to work very well regarding the estimation of the hardware perspective on the system reliability. Therefore, the methodology and the developed algorithm show potentials for optimizations in sensor node positions for better reliability results.

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An Approach to Describe Gentelligent Components in their Life Cycle

Cited by 8 publications

References 4 publications

The Impact of Industry 4.0 on the Future of Green Supply Chain

The Impact of Industry 4.0 on the Future of Green Supply Chain

Design methodology for optimal sensor placement for cure monitoring and load detection of sensor-integrated, gentelligent composite parts

Reliability Assessment of Wireless Sensor Networks by strain-based Region Analysis for Redundancy Estimation in Measurements on the Example of an Aircraft Wing Box

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