Sensor and Electronics Integration in Additive Manufacturing

Lehmhus, Dirk; Busse, Matthias

doi:10.1002/9783527679249.ch9

Cited by 7 publications

(4 citation statements)

References 90 publications

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“…The integration of sensors into AM would help to gather product life cycle data and aid industrial practitioners in product life cycle monitoring. Moreover, the data obtained from sensors would be easily accessible from anywhere due to the cloud-based manufacturing facilitating decentralized and distributed manufacturing solutions (Lehmhus and Busse, 2018). This will help industry practitioners and key decision-makers with a deep understanding of I4.0 and AM integration.…”

Section: Resultsmentioning

confidence: 99%

Application of total interpretive structural modeling for analyzing factors of additive manufacturing and industry 4.0 integration

Wankhede

Vinodh

2021

RPJ

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Purpose The purpose of this paper is to develop a model based on the total interpretive structural modeling (TISM) approach for analysis of factors of additive manufacturing (AM) and industry 4.0 (I4.0) integration. Design/methodology/approach AM integration with I4.0 is attributed due to various reasons such as developing complex shapes with good quality, real-time data analysis, augmented reality and decentralized production. To enable the integration of AM and I4.0, a structural model is to be developed. TISM technique is used as a solution methodology. TISM approach supports establishing a contextual relationship-based structural model to recognize the influential factors. Cross-impact matrix multiplication applied to classification (MICMAC) analysis has been used to validate the TISM model and to explore the driving and dependence power of each factor. Findings The derived structural model indicated the dominant factors to be focused on. Dominant factors include sensor integration (F9), resolution (F12), small build volumes (F19), internet of things and lead time (F14). MICMAC analysis showed the number of driving, dependent, linkage and autonomous factors as 3, 2, 12 and 3, respectively. Research limitations/implications In the present study, 20 factors are considered. In the future, additional factors could be considered based on advancements in I4.0 technologies. Practical implications The study has practical relevance as it had been conducted based on inputs from industry practitioners. The industry decision-makers and practitioners may use the developed TISM model to understand the inter-relationship among the factors to take appropriate measures before adoption. Originality/value The study on developing a structural model for analysis of factors influencing AM and I4.0 is the original contribution of the authors.

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Section: Resultsmentioning

confidence: 99%

Application of total interpretive structural modeling for analyzing factors of additive manufacturing and industry 4.0 integration

Wankhede

Vinodh

2021

RPJ

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“…At one time thought to be a consumer-oriented technology, AM has now evolved into a viable industrial manufacturing solution. One of the most important aspects of mechanical systems constructed using additive manufacturing (AM) is finding the optimal location for mounting the sensors [2,3,5] such that they can be integrated.…”

Section: Figure 1 Flow Of the Additive Manufacturing Processmentioning

confidence: 99%

Development of an additive fabricated biped mechanical system

Dugaesescu

Pârvu

Enache

2022

IOP Conf. Ser.: Mater. Sci. Eng.

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The use of mechatronic components and the incorporation of additive manufacturing into the process of product development have grown in popularity over the past several years. The goal of the research conducted in this paper is to develop a prototype of a monomobile biped mechanical system and to optimize the printing parameters for the additive manufacturing stage. This paper presents the modelling of the components of a biped mechanical system and the simulation of their operation. The modelling step is followed by setting the printing parameters in the dedicated Z-Suite software and creating the G-code for the Zortrax M300 Plus additive manufacturing equipment. Lastly, the paper presents the connection of the sensors to the Plusivo board, the mounting procedure to the 3D printed parts that make up the biped mechanical system as well as the operation methodology of the sensors. The resulting prototype is then used to monitor parameters specific to the sensors used.

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“…This drawback regarding sensor positioning can be mitigated by laser powder bed fusion (LPBF) technology. It enables the integration and precise positioning of sensors into metallic components, as presented conceptionally by Lehmhus et al [3,4] and shown practically among others by Petrat et al [5] for electronic components, e.g. an LED, Stoll et al [6] for temperature sensors and Maier et al [7], Mathew et al [8], Stoll et al [9] for optical fibers.…”

Section: Motivation and Visionmentioning

confidence: 99%

Embedding eddy current sensors into LPBF components for structural health monitoring

Stoll

Gasparin²,

Spierings

et al. 2021

Prog Addit Manuf

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Laser powder bed fusion (LPBF) facilitates the integration of external elements like sensors into workpieces during manufacturing. These embedded components enable e.g. part monitoring, thus being a fundamental application of industry 4.0. This study assesses the feasibility of embedding eddy current (EC) sensors for non-destructive testing (NDT) into SLM components aiming at structural health monitoring (SHM). A reliable embedding process for EC sensors is developed, ensuring the survivability of the sensors for the LPBF process and its harsh conditions. The experiments conducted demonstrate the possibility to use the embedded EC sensor to observe and detect a controlled crack growth. The cracks are realized either with direct EDM cutting or on the course of a fatigue test of CT specimens. The data retrieved by the embedded EC sensors are proven to provide a direct information about the severity of a damage and its evolution over time for both approaches. Thus, supporting the validation of such an innovative and promising SHM concept.

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Sensor and Electronics Integration in Additive Manufacturing

Cited by 7 publications

References 90 publications

Application of total interpretive structural modeling for analyzing factors of additive manufacturing and industry 4.0 integration

Application of total interpretive structural modeling for analyzing factors of additive manufacturing and industry 4.0 integration

Development of an additive fabricated biped mechanical system

Embedding eddy current sensors into LPBF components for structural health monitoring

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