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ayat2 Open sidebar AI in Production Systems. Model: Default (GPT-3.5) Ahmad Humaizi Wikarek, J., Sitek, P., & Juzoń, Z. (2022). A proactive and reactive approach to the allocation and configuration of multidimensional resources in production systems. IFAC-PapersOnLine, 55(2), 445-450. An example of a question that will be considered in future research is: Is it possible to implement a new project with a given schedule with the current additional resources and machine centers? The proposed model implementation using AMPL enables its solution with the use of many mathematical/constraint programming solvers It is also planned to use artificial intelligence and hybrid methods (Sitek and Wikarek 2014; Sitek and Wikarek 2018) to solve larger problems Wittenberg, C. (2022). Challenges for the human-machine interaction in times of digitization, CPS & IIoT, and artificial intelligence in production systems. IFAC-PapersOnLine, 55(29), 114-119. 0 and the Industrial Internet of Things, and now also the use of machine learning and artificial intelligence are leading to immense changes in the working environment for plant operators and maintenance technicians This article highlights the development over the last decades, the current requirements and relevant research topics Mühlbeier, E., Oexle, F., Gerlitz, E., Matkovic, N., Gönnheimer, P., & Fleischer, J. (2022). Conceptual control architecture for future highly flexible production systems. Procedia CIRP, 106, 39-44. 2 Where is artificial intelligence usefully applied in automation technology? If you take the idea of Industry 4 0 a step further and look in particular at flexible production towards batch size 1, then optimal production planning and monitoring of production for very small quantities is certainly a possible field of application for artificial intelligence and machine learning Based on learning data as well as with the inclusion of data from previously executed production, artificial intelligence can certainly plan and implement a production strategy that is as optimal as possible for the product in the case of new production orders In the simplest case, the production order would then simply be accepted, and the actual production would be planned and started fully automatically by the artificial intelligence, including the raw material supplies 4 Machine Learning In addition to supervised and unsupervised machine learning, reinforcement learning (Lorenz, 2020; Nandy & Biswas, 2018) is currently being increasingly researched in the industrial sector and applications are being developed and evaluated Necessary for reinforcement learning are the components perception (of the current state), action planning and action execution (to change the state) and as third the goal, which is to be reached by means of reinforcement learning (Figure 7) RESEARCH ACTIVITIES AT HEILBRONN UNIVERSITY REGARDING ARTIFICIAL INTELLIGENCE AND HUMAN-COMPUTER INTERACTION Both the topic of artificial intelligence and the topic of humancomputer interfaces are being researched in a variety of ways at Heilbronn University In the field of AI and reinforcement learning, the programming of robotic arms is considered as well as topics of predictive maintenance In the area of Human-Computer Interaction, topics of the use of Augmented Reality and Virtual Reality in the industrial context - e Müller, T., Jazdi, N., Schmidt, J. P., & Weyrich, M. (2021). Cyber-physical production systems: enhancement with a self-organized reconfiguration management. Procedia CIRP, 99, 549-554. Energy-flexible Controller The energy-flexible controller implements the DR measures which can be performed by the CPPS DR measures can be initiated as a reaction to changing electricity prices, to direct control signals from the electric utility company or to other incentives The controller can use a simple rule-based algorithm or more complex designs such as control with heuristic methods or artificial intelligence algorithms to optimize the system The operation of the controller and its interactions with other parts of the CPPS are illustrated in the program flow chart in 224 4 Müller, T., Jazdi, N., Schmidt, J. P., & Weyrich, M. (2021). Cyber-physical production systems: enhancement with a self-organized reconfiguration management. Procedia CIRP, 99, 549-554. In order to be able to provide potentials such as a selforganized reconfiguration management, however, prerequisites such as the existence of semantic descriptions of the CPPS and its components must be met Furthermore, there is a need for concepts that adopt existing algorithms from domains such as artificial intelligence or mathematical optimization, if necessary in adapted form [18] Examples of such adoptions are [19] for image recognition and [20] for predictive quality control, both utilized in industrial automation systems In CPPS, data, services and functions are held, retrieved and executed where it brings the greatest benefit in terms of flexible and efficient design, engineering, development and production Baroroh, D. K., & Chu, C. H. (2022). Human-centric production system simulation in mixed reality: An exemplary case of logistic facility design. Journal of Manufacturing Systems, 65, 146-157. In contrast, Mixed Reality (MR) creates a new form of simulation by merging the real and virtual worlds, in which physical and digital elements coexist and interact with each other Through a hand-held device (HHD) or head-mount device (HMD), it augments the real world by making implicit information apparent to the user when required MR is considered an effective user interface for human-artificial intelligence (AI) collaboration and has a potential of realizing human in the loop for smart manufacturing [1517] Not only virtual contents, people wearing an HMD can also be seamlessly integrated into an MR scene This generates a possibility of instantly evaluating human performance in a factory presented in MR and observing their interactions with other machines or work parts Fuhrländer-Völker, D., Borst, F., Theisinger, L., Ranzau, H., & Weigold, M. (2022). Modular data model for energy-flexible cyber-physical production systems. Procedia CIRP, 107, 215-220. 036 216 aqueous cleaning machine Also the energy-flexible control of industrial energy supply systems and the application of artificial intelligence (AI) algorithms for DR measures is described in [20, 9] The presented research focus on the DR algorithm and show the benefit of DR measures in industry However, the execution of DR measures on machine level is not described in detail The presented research focus on the DR algorithm and show the benefit of DR measures in industry For this execution the three domains automation, communication and simulation must be considered collectively: Nomenclature a array AI artificial intelligence b boolean DR demand response f float HMI human-machine interface n integer OPC UA Open Platform Communications Unified Ar- chitecture s string ST structure T temperature RAMI 4 Ehrhardt, J. M., & Hoffmann, C. T. (2020). The digital shadow: developing a universal model for the automated optimization of cyber-physical production systems based on real-time data. Procedia CIRP, 93, 304-310. In general approaches in literature follow specific optimization-objectives, rather than suggesting holistic approaches Main optimization-objectives are energy consumption, against the background of volatile energy prices [21], cost-based examination of reimplementations of production systems [15, 18], product quality aspects, mainly in combination with other optimization objectives [17, 5], as well as the material-flow concerning processing, transporting and storing of products within the production system [6, 8] and production scheduling, as a classic problem of operation management and core-aspect of the manufacturing industry [26, 24, 27, 20] We divide the optimization-methods into analytical approaches, algorithmic approaches and artificial intelligence methods Due to their high performance, analytical approaches are often applied in combination with algorithmic approaches, but mainly left out due to their high specificity [24] To tackle the aspect of automation, we identified three approaches in the literature review: analytic approaches, algorithmic approaches and artificial intelligence applications Despite their high performance, we drop analytical methods, due to their high specificity, colliding with the aspect of universality Artificial intelligence methods, such as artificial neural networks or self-learning algorithms, are often used on problems hard to be tackled with conventional methods To tackle the aspect of automation, we identified three approaches in the literature review: analytic approaches, algorithmic approaches and artificial intelligence applications The problemsolving often cannot be retraced in a detailed manner, since the underlying models are often inherently black-box models Furthermore, these methods are heavily reliant on precedent training and the optimal tuning of their hyperparameters, which can only be automated to a limited extent by now The problemsolving often cannot be retraced in a detailed manner, since the underlying models are often inherently black-box models Prompt These are the articles related to artificial intelligence in production system. The references in APA format are above each articles, while the articles are below it. New articles are separated by new references. Commonly references have the number (20xx) and the journal names. From the contents on the articles above, and also additional resources from your data in your system Create a tabular form which includes the following 1st column , named = references in APA format , which contents are references in APA format 2nd column , named = description , which contents are reviews on what the authors write. 3rd column, named = year which is the year of that references References in APA format Description Year Wikarek, J., Sitek, P., & Juzoń, Z. (2022) Proposes a proactive and reactive approach to allocate and configure multidimensional resources in production systems. 2022 Wittenberg, C. (2022) Discusses challenges in human-machine interaction in the context of digitization, CPS, IIoT, and AI in production. 2022 Mühlbeier, E., Oexle, F., Gerlitz, E., Matkovic, N., Gönnheimer, P., & Fleischer, J. (2022) Explores the application of AI and machine learning in flexible production systems, with emphasis on optimal production planning and monitoring of production. 2022 Müller, T., Jazdi, N., Schmidt, J. P., & Weyrich, M. (2021) Describes a self-organized reconfiguration management for cyber-physical production systems, with a focus on energy-flexible controllers using AI algorithms. 2021 Baroroh, D. K., & Chu, C. H. (2022) Investigates the use of mixed reality as an interface for human-AI collaboration in manufacturing systems. 2022 Fuhrländer-Völker, D., Borst, F., Theisinger, L., Ranzau, H., & Weigold, M. (2022) Proposes a modular data model for energy-flexible cyber-physical production systems, with emphasis on the execution of DR measures using AI algorithms. 2022 Ehrhardt, J. M., & Hoffmann, C. T. (2020) Proposes a universal model for the automated optimization of cyber-physical production systems using AI algorithms. 2020 Send a message... ChatGPT Mar 23 Version. ChatGPT may produce inaccurate information about people, places, or facts New chat