Volume 72, Issue 9 - Special Issue: Perpetual innovative products from circular factories for sustainable production; Guest Editors: Gisela Lanza, Michael Martin and Georg Bretthauer

The growing scarcity of global resources demands a transition from linear to circular production patterns. This article presents a novel vision for integrating linear and circular production processes within a flexible and autonomous production system to achieve perpetual product use. The approach aims to preserve the added value of products and to integrate the design of product generations and production systems. Within the circular factory, the following core aspects must be examined: predicting functions of products, managing uncertainty in used products and process sequences, learning human action for complex tasks, implementing changeable, autonomous production systems, and enabling knowledge modeling for the circular factory across domains. Aspired results are a design for circular factory, effective strategies for uncertainty management and autonomous systems adaptation as well as the externalization of operational knowledge. This research is part of the Collaborative Research Center (CRC) 1574, which explores these aspects in detail. For an in-depth understanding of specific components, it is referred to other publications by the CRC 1574.

In the industrial sector, products evolve significantly over their operational life. A key challenge has been maintaining precise, relevant engineering data. This paper explores the digital twin concept, merging engineering and operational data to enhance product information updates. It examines digital twin applications in construction, material flow, manufacturing and production, citing battery production and additive manufacturing. Digital twins aid in analyzing, experimenting with, and refining a system’s design and its operation, offering insights across product and system lifecycles. This includes tackling data management and model-data consistency challenges, as well as the recognition of synergies. This paper emphasizes sustainable, efficient management of engineering information, reflecting shifts in product longevity and documentation in industrial products and machinery.

The need for sustainability is driving the transition from linear to circular production. Customers, driven by the desire for innovation, prefer new products with the latest cutting-edge functions and design. The problem is that used products from older product generations are hence less attractive, leading to a limited market for remanufactured products and generating less revenue than their new counterparts. Therefore, this research addresses the problem of reprocessing and technologically updating used products into the newest product generation. To address this problem of reprocessing and technological updating with a Circular Factory, the product’s function fulfillment must be predicted in different situations in a Circular Factory. Three key challenges in which the function fulfillment must be predicted are identified as: Analysis and Diagnostics of Used Products and Subsystems , Prediction for Pairing and Reprocess of the Embodiment and Design for Circular Factory . A model-based approach is presented to address these three key challenges by predicting product function fulfillment to enable cross-generational use of subsystems and components. Five different types of models are proposed: A Functional Model , a Reliability Model and a Reference Architecture Model at the high level of general product modeling, as well as a Digital Twin of an object instance and a Tolerance Scheme at the level of individual instances. A theoretical case study shows the possible application in a Circular Factory using the example of an angle grinder. However, it’s essential to acknowledge that substantial work is needed for the enhancement or development of these models before practical application. Successful application of these models allows the Circular Factory not only to maintain but also to increase the value of reprocessed products to enable offering on the primary market and contribute to the vision of realizing a perpetual innovative product.

In the circular factory, uncertain attributes of object instances and process steps are found at diverse occasions. Even if uncertainty can also be found to some extent in linear production, the high variation of product attributes of used objects causes the process steps in the circular factory to generate a much higher variability of the properties of the objects handled in circular processes. In consequence, a methodology is needed to model, handle and manage uncertainties at all relevant situations within the circular factory. In contrast to linear production, the uncertainty of attributes cannot be extended to an object class (with the same production history), but must be assigned to each object instance (with its own history) individually. In this contribution, the basic concepts for managing uncertainty in the circular factory are introduced. As a common basis, probabilities are used to express uncertainty, thus being compatible with the traditional and proven concepts of measurement science and stochastics. To describe the individual information state of object instances, it is complemented with a joint probability distribution describing all relevant object attributes. Some examples for processes within the circular factory demonstrate how uncertainty is considered to manage the uncertainty related challenges of used objects.

Process automation is essential to establish an economically viable circular factory in high-wage locations. This involves using autonomous production technologies, such as robots, to disassemble, reprocess, and reassemble used products with unknown conditions into the original or a new generation of products. This is a complex and highly dynamic issue that involves a high degree of uncertainty. To adapt robots to these conditions, learning from humans is necessary. Humans are the most flexible resource in the circular factory and they can adapt their knowledge and skills to new tasks and changing conditions. This paper presents an interdisciplinary research framework for learning human action knowledge from complex manipulation tasks through human observation and demonstration. The acquired knowledge will be described in a machine-executable form and will be transferred to industrial automation execution by robots in a circular factory. There are two primary research objectives. First, we investigate the multi-modal capture of human behavior and the description of human action knowledge. Second, the reproduction and generalization of learned actions, such as disassembly and assembly actions on robots is studied.

The integration of both linear and circular processes in one production system poses significant challenges. In particular, the reprocessing of end-of-life products is associated with uncertainties at all levels of the production system, from the initial planning and control through to the executing production hardware and intralogistics. To address these challenges, this article presents approaches for self-learning and autonomously adapting production equipment for the Circular Factory. Initially, hardware and software solutions are developed to cover the necessary processes. Reprocessing is covered by modular and reconfigurable manufacturing cells, which also include new process chains such as the combination of additive-subtractive processes. The provided capabilities must be applied to ever new products, for example by transferring human procedures for unknown products to the production equipment. Lastly, an overall robust and dynamic production planning and control system is developed that maintains continuous operation even in unforeseen situations. The resulting highly dynamic overall system is connected by an autonomous intralogistics system.

In a circular factory, new products are produced reusing parts from used products, as well as newly manufactured parts. The production system consists of disassembly, testing as well as assembly steps. Due to the unforeseeable conditions of the used parts, the complexity of such a circular factory is challenging. This paper contributes a concept of an ontology-based knowledge backbone to master the challenges of such a circular factory. The concept addresses the representation of knowledge especially taking into account uncertainty, how to design queries and means to detect similarities and analogies. Furthermore, the role of research data management with automatized workflows as a supplier for FAIR data is elaborated.

Resource scarcity and unsustainable production of products are critical challenges in today’s manufacturing landscape. In this context, adaptive remanufacturing of products could play an important role. This concept prioritizes restoring product functionality over adhering strictly to original specifications, achieving a higher yield in a cost-effective manner. The objective of this work is to explore adaptive remanufacturing and provide methods to analyse the potential in production. For this purpose, four cases of adaptive remanufacturing are differentiated and an assessment matrix to evaluate product suitability for this process, considering various criteria, is presented. These two tools aid industries in assessing the viability and advantages of adaptive remanufacturing, promoting more sustainability across industries.