Adaptive Remanufacturing – Methodology towards an intelligent maintenance strategy for production resources

Burggräf, Peter; Adlon, Tobias; Müller, Klaus; Föhlisch, Nils; Dackweiler, Jonas; Fölling, Carsten

doi:10.1016/j.procir.2021.01.112

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…Although the term "adaptive maintenance" (AdM) has not been coined, Burggräf [42] suggested that the maintenance function should not only restore or maintain the operating condition of productive equipment and used the term "adaptive remanufacturing" (AdR) to suggest that the evolution towards intelligent and strategic maintenance should improve productive resources by increasing the life cycles of assets, monitoring their performance levels and considering the technical, economic and ecological aspects. It is considered relevant for future work to add this new concept of adaptive maintenance to this operational definition, where, in addition to re-establishing the required function of the high-impact asset to be maintained, the availability is ensured and the required function is increasedfor example, when there is an increase in the speed of a machine due to the demands of the changing production context.…”

Section: Discussionmentioning

confidence: 99%

Assisted-Driven Design of Customized Maintenance Plans for Industrial Plants

2022

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Current production systems that respond to market demands with high rates of production change and customization use complex systems. These systems are machines with a high capacity for communication, sensing and self-diagnosis, although they are susceptible to failures, breakdowns and a loss of reliability. The amount of data they provide as a productive system and, individually, as a machine can be treated to improve customized maintenance plans. The objective of this work, with an operational scope, is to collect and exploit the knowledge acquired in the industrial plant on failures and breakdowns based on its historical data. The acquisition of the aforementioned data is channeled through the human intellectual capital of the work groups formed for this purpose. Once this knowledge is acquired and available in a worksheet format according to the Reliability-Centered Maintenance (RCM) methodology, it is implemented using Case-Based Reasoning algorithms in a Java application developed for this purpose to carry out the process of RCM, accessing a base of similar cases that can be adapted. This operational definition allows for the control of the maintenance function of an industrial plant in the short term, with a weekly horizon, to design a maintenance plan adjusted to the reality of the plant in its current operating context, which may differ greatly from the originally projected plan or from any other plan caused by new production requirements. This new plan designed as such will apply changes to the equipment, which make up the production system, as a consequence of the adaptation to the changing market demand. As a result, a computer application has been designed, implemented and validated that allows, through the incorporation of RCM cases already successfully carried out on the productive system of the plant, for the development of a customized maintenance plan through an assistant, which, in a conductive way, guides the plant maintenance engineer through their design process, minimizing human error and design time and leveraging existing intellectual capital.

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Section: Discussionmentioning

confidence: 99%

Assisted-Driven Design of Customized Maintenance Plans for Industrial Plants

2022

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“…However, the sustainability of this growth is increasingly challenged by the pressing issue of resource limitations. In the context of escalating resource scarcity, the imperative to optimize product life cycles has emerged as a critical concern, as delineated by Burggraf et al [1]. The circular economy, as a novel paradigm encompassing the social, environmental, and economic facets of sustainable development, presents a viable solution to mitigate the resource constraints and socio-environmental ramifications inherent in the prevailing linear economic model.…”

Section: Introductionmentioning

confidence: 99%

Advancing the Industrial Circular Economy: The Integrative Role of Machine Learning in Resource Optimization

Lin,

Wei

2023

JGELCD

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In the face of escalating resource scarcity driven by the consumption of non-renewable resources, the industrial circular economy (ICE) emerges as a vital paradigm shift, pivotal for fostering resource-efficient societies and ensuring national resource security. This integrative review aims to critically assess the evolution and challenges inherent within the ICE over recent years, with a specific focus on the burgeoning role of machine learning (ML) in this domain. By synthesizing extant literature, this examination reveals several key findings. Firstly, the ICE significantly contributes to cost reduction through enhanced recycling and secondary utilization, underscoring its environmental stewardship. Secondly, it is evident that ML exhibits substantial promise in the manufacturing sector, not only augmenting production processes but also elevating product precision, reducing defect rates, and minimizing the likelihood of production mishaps. Most crucially, the application of ML within the ICE is identified as a potent catalyst, driving advancements across various facets -data analysis, model development, technological innovation, and equipment refinement. This analysis further elucidates the intrinsic value of ML in resource recycling and waste management, yielding improvements in resource recycling rates and methodologies, which in turn curtails production costs and amplifies output efficiency. Despite the strides made in replacing traditional industrial models with more sustainable ICE practices, challenges persist, particularly regarding the suboptimal levels of resource recycling and the continued generation of industrial waste. The integration of ML within ICE frameworks is posited as a transformative approach, offering not only enhanced resource recycling capabilities and superior product quality but also a sustainable trajectory for future industrial development. This study, therefore, contributes to the growing discourse on sustainable industrial practices, underscoring the synergistic potential of ML in revolutionizing the ICE, thereby aligning with the broader objectives of sustainable economic development.

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Development of a diagnostic tool for product circularity: a redesign approach

Mesa

Gonzalez‐Quiroga

2023

Res Eng Design

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The design of circular products is gaining relevance among academics, practitioners, and industry as a critical step to face material scarcity, the unsustainability of industrial processes, and excessive waste generation. Therefore, product design is a powerful research field to promote material, component, and product circularity throughout several lifecycles. However, there is a lack of formal methods to measure the circularity of products, especially from the early design stages. Therefore, this paper proposes a diagnostic tool for measuring circularity, identifying potential improvement opportunities, and redesigning interventions in the conceptual design phase. The tool consists of eight single indicators that measure disassemblability, reassemblability, durability, modularity, simplicity, standardization, commonality, relative costs, and the number of secondary functions of products. A case study is presented to validate the tool, considering several improvement scenarios around the 6R strategies. Finally, a list of redesign interventions for each improvement scenario was assembled as a primary output of the proposed tool.

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Adaptive Remanufacturing – Methodology towards an intelligent maintenance strategy for production resources

Cited by 4 publications

References 7 publications

Assisted-Driven Design of Customized Maintenance Plans for Industrial Plants

Assisted-Driven Design of Customized Maintenance Plans for Industrial Plants

Advancing the Industrial Circular Economy: The Integrative Role of Machine Learning in Resource Optimization

Development of a diagnostic tool for product circularity: a redesign approach

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