Fostering End-of-Life Utilization by Information-driven Robotic Disassembly

Poschmann, Hendrik; Brüggemann, Holger; Goldmann, Daniel

doi:10.1016/j.procir.2021.01.104

Cited by 32 publications

(19 citation statements)

References 5 publications

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“…Due to new options on robot cognition processors (Poschmann et al 2021 ) and future developments based on artificial intelligence new opportunities evolve. Specifically for highly complex and valuable products, these technologies will be applied.…”

Section: New and Potential Technical Developments In Collection Disma...mentioning

confidence: 99%

Recycling and circular economy—towards a closed loop for metals in emerging clean technologies

Hagelüken

Goldmann

2022

Miner Econ

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Resource efficiency, energy, and mobility transition are crucial strategies to mitigate climate change. The focus is on reducing the consumption of resources, especially energy and raw materials. While raw materials are the basis of our material world, their excessive consumption over the last decades has also contributed significantly to climate change. However, raw materials, and here especially metals, play a key enabling role as well for climate protection technologies, such as electro mobility, the hydrogen economy, and solar and wind power plants, and also for digitalization. Accordingly, it is necessary to make the use of raw materials much more resource-efficient than before and to use them as purposefully as possible instead of consuming them. Advanced circular economy systems and sophisticated recycling technologies build the backbone for the development of a resource efficient and sustainable society. Closed metal cycles contribute for a paramount share to this by securing relevant parts of the raw material supply for high-tech products and by reducing CO 2 emissions in their production at the same time. Interacting steps in multistage treatment processes by mechanical, chemical, and thermal unit operations are challenging but will give a competitive advantage for networks of industry and science that are able to handle that.

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Section: New and Potential Technical Developments In Collection Disma...mentioning

confidence: 99%

Recycling and circular economy—towards a closed loop for metals in emerging clean technologies

Hagelüken

Goldmann

2022

Miner Econ

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“…The easier a product is to disassemble, the easier it is to recycle and reuse. A robot-based disassembly system is shown by Poschmann et al [88]. They used TensorFlow object detection API using faster RCNN and Inception ResNet V2 structure.…”

Section: Recycling and Reusementioning

confidence: 99%

“…They used TensorFlow object detection API using faster RCNN and Inception ResNet V2 structure. The whole system can use the life cycle data of products to decide the actual degree of disassembly for the best end-of-life utilization of that product [88]. .…”

Section: Recycling and Reusementioning

confidence: 99%

Machine Learning and Artificial Intelligence in Circular Economy: A Bibliometric Analysis and Systematic Literature Review

Noman¹,

Akter²,

Pranto³

et al. 2022

AETiC

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With unorganized, unplanned and improper use of limited raw materials, an abundant amount of waste is being produced, which is harmful to our environment and ecosystem. While traditional linear production lines fail to address far-reaching issues like waste production and a shorter product life cycle, a prospective concept, namely circular economy (CE), has shown promising prospects to be adopted at industrial and governmental levels. CE aims to complete the product life cycle loop by bringing out the highest values from raw materials in the design phase and later on by reusing, recycling, and remanufacturing. Innovative technologies like artificial intelligence (AI) and machine learning(ML) provide vital assistance in effectively adopting and implementing CE in real-world practices. This study explores the adoption and integration of applied AI techniques in CE. First, we conducted bibliometric analysis on a collection of 104 SCOPUS indexed documents exploring the critical research criteria in AI and CE. Forty papers were picked to conduct a systematic literature review from these documents. The selected documents were further divided into six categories: sustainable development, reverse logistics, waste management, supply chain management, recycle & reuse, and manufacturing development. Comprehensive research insights and trends have been extracted and delineated. Finally, the research gap needing further attention has been identified and the future research directions have also been discussed.

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“…In the scope of the research project Recycling 4.0, an autonomous robot system to disassemble traction batteries was already presented [13]. During its process lifecycle, the robot system needs different kinds of data: For example, different training datasets for the AI-based functions and data about the current product, like the state of health.…”

Section: Autonomous Systemsmentioning

confidence: 99%

Data Trading Similarity Signature An Extended Data Trading Framework for Human and Non-Human Actors

Lawrenz¹,

Poschmann²,

Rausch³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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Fair and secure data trading is one of the most prominent challenges of the 21st century. This paper presents a second iteration of an approach to develop a data marketplace concept by checking consumer requirements. The main problem we identified is data quality and the question: Would a dataset fulfill the consumer requirements? Starting from an approach that uses a binary response set to answer the question of whether requirements are met, we concluded that a description of consumer requirements needs to be quantitatively comparable. The novel approach presented here identifies similarities between datasets and consumer requirements. It forms a unique, fingerprint-like similarity signature for each dataset, which can be interpreted by both human and non-human actors. The approach is deducted and designed by using the Design Science Research Methodology and discussed critically in the end.

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Fostering End-of-Life Utilization by Information-driven Robotic Disassembly

Cited by 32 publications

References 5 publications

Recycling and circular economy—towards a closed loop for metals in emerging clean technologies

Recycling and circular economy—towards a closed loop for metals in emerging clean technologies

Machine Learning and Artificial Intelligence in Circular Economy: A Bibliometric Analysis and Systematic Literature Review

Data Trading Similarity Signature An Extended Data Trading Framework for Human and Non-Human Actors

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