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In countries such as Germany, where municipalities have planning sovereignty, problems of urban sprawl often arise. As the dynamics of land development have not substantially subsided over the last years, the national government decided to test the instrument of ‘Tradable Planning Permits’ (TPP) in a nationwide field experiment with 87 municipalities involved. The field experiment was able to implement the key features of a TPP system in a laboratory setting with approximated real socioeconomic and planning conditions. In a TPP system allocated planning permits must be used by municipalities for developing land. The permits can be traded between local jurisdictions, so that they have flexibility in deciding how to comply with the regulation. In order to evaluate the performance of such a system, specific field data about future building areas and their impact on community budgets for the period 2014–2028 were collected. The field experiment contains several sessions with representatives of the municipalities and with students. The participants were confronted with two (municipalities) and four (students) schemes. The results show that a trading system can curb down land development in an effective and also efficient manner. However, depending on the regulatory framework, the trading schemes show different price developments and distributional effects. The unexperienced representatives of the local authorities can easily handle with the permits in the administration and in the established market. A trading scheme sets very high incentives to save open space and to direct development activities to areas within existing planning boundaries. It is therefore a promising instrument for Germany and also other regions or countries with an established land-use planning system.
Governments and public institutions increasingly embrace digital opportunities to involve citizens in public issues and decision making. While public participation is generally seen as an important and promising venture, the design of the participation processes and the utilized digital infrastructure poses challenges, especially to the public sector. Instead of limiting conceptual guidance and exchange to one domain, we therefore develop a taxonomy for digital involvement projects that unites the domains of e-participation, citizen science and crowd-X. Embedded in a design science research approach, we follow an iterative design process to elaborate the key characteristics of a digital involvement project based on the participation process, its individuals and digital infrastructure. Through evaluating the artifact in a focus group with domain practitioners, we find support for the usefulness of our taxonomy and its ability to provide guidance and a basis for discussion of digital involvement projects across domains.
Delphi Markets
(2023)
Delphi markets refer to approaches and implementations of integrating prediction markets and Delphi studies (Real-time Delphi). The combination of the two methods for producing forecasts can potentially compensate for each other´s weaknesses. For example, prediction markets can be used to select participants with expertise and also motivate long-term participation through their gamified approach and incentive mechanisms. In this paper, two potentials for prediction markets and four potentials for Delphi studies, which are made possible by integration, are derived theoretically. Subsequently, three different integration approaches are presented, on the basis of which the integration on user, market and Delphi question-level is exemplified and it is shown that, depending on the approach, not all potentials can be achieved. At the end, recommendations for the use of Delphi markets are derived, existing limitations for Delphi markets as well as future developments are pointed out.
This study explores the application of the PatchCore algorithm for anomaly classification in hobbing tools, an area of keen interest in industrial artificial intelligence application. Despite utilizing limited training images, the algorithm demonstrates capability in recognizing a variety of anomalies, promising to reduce the time-intensive labeling process traditionally undertaken by domain experts. The algorithm demonstrated an accuracy of 92%, precision of 84%, recall of 100%, and a balanced F1 score of 91%, showcasing its proficiency in identifying anomalies. However, the investigation also highlights that while the algorithm effectively identifies anomalies, it doesn't primarily recognize domain-specific wear issues. Thus, the presented approach is used only for pre-classification, with domain experts subsequently segmenting the images indicating significant wear. The intention is to employ a supervised learning procedure to identify actual wear. This premise will be further investigated in future research studies.
The maintenance of special tools is an expensive business. Either manual inspection by an expert costs valuable resources, or the loss of a tool due to irreparable wear is associated with high replacement costs, while reconditioning requires only a fraction. In order to avoid higher costs and drive forward the automation process in production, a German gear manufacturer wants to create an automatic evaluation of skiving gears. As a sub-step of this automated condition detection, it is necessary for wheels to be automatically aligned within a vision-based inspection cell. In extension to a study conducted last year, further image preprocessing steps are implemented in this publication and a new alignment algorithm from the autoencoder family is evaluated. By using an additional synthetic dataset, previous limitations could be clarified. The results show that thorough data preparation is beneficial for all solution approaches and that neural networks can even beat a brute force algorithm.
Thorough maintenance of industrial equipment is crucial for the finances of companies. Whereas the purchase of new tools can be an expensive business, reconditioning special gear often costs just a fraction. In this paper, preliminary steps for an accurate visual based preventive maintenance of hobbing wheels are investigated. To perform robust and reliable decisions about the wheel's condition, tool department specialists require precise taken captures of it. For this reason, a visual control cell is built, which depends on correctly aligned hobbing wheels in its image acquisition construction. The tool needs to be placed on a turn-table and rotated, so that a single tooth is centered in the field-of-view of the camera mounted on a robot arm. For this alignment task, three different main approaches with various preprocessing steps are investigated, a brute-force algorithm, an orb-feature approach and an image regression model. The results show that even a brute-force algorithm can be outperformed by a moderate deep neural network.
Mithilfe von Modellen der erklärbaren Künstlichen Intelligenz (XAI) können die Rüstzeiten in der CNC-Werkzeugherstellung reduziert werden, wodurch nicht nur die betriebliche Effizienz und Produktionskapazität gesteigert, sondern auch die wirtschaftliche Rentabilität von KI-Projekten nachgewiesen werden kann.
The time has come : application of artificial intelligence in small- and medium-sized enterprises
(2022)
Artificial intelligence (AI) is not yet widely used in small- and medium-sized industrial enterprises (SME). The reasons for this are manifold and range from not understanding use cases, not enough trained employees, to too little data. This article presents a successful design-oriented case study at a medium-sized company, where the described reasons are present. In this study, future demand forecasts are generated based on historical demand data for products at a material number level using a gradient boosting machine (GBM). An improvement of 15% on the status quo (i.e. based on the root mean squared error) could be achieved with rather simple techniques. Hence, the motivation, the method, and the first results are presented. Concluding challenges, from which practical users should derive learning experiences and impulses for their own projects, are addressed.
Enhancing power skiving tool longevity: the synergy of AI and robotics in manufacturing automation
(2024)
In gear manufacturing, the longevity and cost-effectiveness of power skiving tools are essential. This study presents an innovative approach that combines artificial intelligence and robotics in manufacturing automation to prevent tool breakage to improve the remaining useful life (RUL). Using a robotic cell, the system captures six images per tooth from different angles. An unsupervised generative deep learning model approach is used because it is more suitable for industrial application as it can be trained with a small number of defect-free images. It is used in a first step as a classifier and, in a second step, to segment tool wear. This approach promises economic benefits by reducing manual inspection and, through automated tool inspection, detecting wear earlier to prevent tool breakage.
Durch das Zusammenspiel von Facharbeitern und Algorithmen in der automatisierten Werkzeugwartung kann die Lebensdauer von Wälzschälwerkzeugen verlängert und die Notwendigkeit manueller Inspektionen reduziert werden. Der Einsatz von Algorithmen, die ausschließlich mit nominalen Daten arbeiten, bietet für Industrieunternehmen erhebliche Vorteile, da nominale Daten weitaus häufiger vorkommen als Anomalien.
