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To Refurbish or not to Refurbish? Towards an AI-based Evaluation System for Power Tool Batteries

Authors:
Dominique Briechle,
Marit Briechle-Mathiszig
Tobias Geger
Nelly Nyeck
Robert Werner

Keywords: Circular Economy, Recommendation, Digital Service Design, Product Lifecycle, Refurbishing, Artificial Neural Networks

Abstract:
The earth’s resources are limited. Nevertheless, humans use more natural resources every year than the earth can provide. For that reason, sustainable usage of products is needed. Refurbishing processes offer an opportunity to extend the lifecycle of products like accumulators. For the refurbishing process, it is important for the operator to not only know the condition of the product but as well the possible expenditures it will cost to restore its functioning condition. The question whether it is possible to determine this kind of information about an accumulator from its external image has not yet been answered. Investigating this question can support the velocity of decision processes of whether a battery should be refurbished or given directly to recycling. This work describes the development of a refurbish and data collecting service and the design of a concept for adjunct data evaluation to investigate if Artificial Intelligence can draw a connection between the external features of an accumulator and the internal condition of the same. The preliminary results include the conception of the service as well as the derivation of assumptions based on the so far collected images of the batteries.

Pages: 8 to 12

Copyright: Copyright (c) IARIA, 2024

Publication date: April 14, 2024

Published in: conference

ISSN: 2308-4146

ISBN: 978-1-68558-153-4

Location: Venice, Italy

Dates: from April 14, 2024 to April 18, 2024