@article{Engelmann, author = {Engelmann, Bastian}, title = {Towards Material-Batch-Aware Tool Condition Monitoring}, series = {Journal of Manufacturing and Materials Processing}, volume = {5}, journal = {Journal of Manufacturing and Materials Processing}, number = {4}, issn = {2504-4494}, abstract = {In subtractive manufacturing, process monitoring systems are used to observe the manufacturing process, to predict maintenance actions and to suggest process optimizations. One challenge, however, is that the observable signals are influenced not only by the degradation of the cutting tool, but also by deviations in machinability among material batches. Thus it is necessary to first predict the respective material batch before making maintenance decisions. In this study, an approach is shown for batch-aware tool condition monitoring using feature extraction and unsupervised learning to analyze high-frequency control data in order to detect clusters of materials with different machinability, and subsequently optimize the respective manufacturing process. This approach is validated using cutting experiments and implemented as an edge framework.}, language = {en} } @article{PfeiferEngelmann, author = {Pfeifer, Tilo and Engelmann, Bastian}, title = {Strategies for the surface measurement with a confocal laserscanning-microscope}, series = {Tagungsband zur Konferenz IX. Internationales Oberfl{\"a}chenkolloquium in Chemnitz}, journal = {Tagungsband zur Konferenz IX. Internationales Oberfl{\"a}chenkolloquium in Chemnitz}, pages = {249 -- 257}, language = {en} } @article{SchmittEngelmann, author = {Schmitt, Robert and Engelmann, Bastian}, title = {Approaches for the control of microsystem assembly by image processing}, series = {Conference Proceedings of Micro System 2005 in Munich}, journal = {Conference Proceedings of Micro System 2005 in Munich}, language = {en} } @article{DriessenDietrichEngelmann, author = {Driessen, Sascha and Dietrich, B. and Engelmann, Bastian}, title = {Mit Planung ans Ziel}, series = {Qualit{\"a}t und Zuverl{\"a}ssigkeit}, journal = {Qualit{\"a}t und Zuverl{\"a}ssigkeit}, number = {9}, pages = {19 -- 20}, language = {de} } @article{SchmittDietrichHafneretal., author = {Schmitt, Robert and Dietrich, Bj{\"o}rn and Hafner, Philip and Engelmann, Bastian}, title = {Messstrategien f{\"u}r die zerst{\"o}rungsfreie Schichtmessung mittels Ultraschallmikroskopie Strategies for the Non-Destructive Characterization of Thin Layers with Scanning Acoustic Microscopy}, series = {tm - Technisches Messen}, volume = {75}, journal = {tm - Technisches Messen}, number = {11}, pages = {577 -- 585}, language = {en} } @article{SchmittHafnerEngelmann, author = {Schmitt, Robert and Hafner, Philip and Engelmann, Bastian}, title = {Strategies for the non-destructive characterization of thin layers with Scanning Acoustic Microscopy}, series = {tm - Technisches Messen}, volume = {74}, journal = {tm - Technisches Messen}, number = {6}, pages = {365 -- 373}, language = {en} } @article{PfeiferDusslerEngelmann, author = {Pfeifer, Tilo and Dussler, G and Engelmann, Bastian}, title = {Visualisierungsmethoden f{\"u}r Mikrostrukturen mit dem konfokalen Laser-Scanning-Mikroskop}, series = {VDI-Berichte}, journal = {VDI-Berichte}, pages = {175 -- 185}, language = {en} } @article{SchmittEngelmann, author = {Schmitt, Robert and Engelmann, Bastian}, title = {Keine Ermessensfrage!: Warum f{\"a}hige Messger{\"a}te wichtige Voraussetzung f{\"u}r Oberfl{\"a}chenoptimierungen sind}, series = {Metalloberfl{\"a}che}, volume = {59}, journal = {Metalloberfl{\"a}che}, number = {9}, pages = {39 -- 42}, language = {en} } @book{Engelmann, author = {Engelmann, Bastian}, title = {Entwicklung einer Systematik zur Modellierung oberfl{\"a}chenabh{\"a}ngiger Funktionseigenschaften}, publisher = {Shaker}, isbn = {978-3-8322-6558-8}, pages = {152}, language = {en} } @article{SchmittDriessenEngelmann, author = {Schmitt, Robert and Driessen, Sascha and Engelmann, Bastian}, title = {Controlling the assembly of micro systems by image processing}, series = {Microsystem technologies}, volume = {12}, journal = {Microsystem technologies}, pages = {640 -- 645}, language = {en} } @article{PfeiferBenzEngelmannetal., author = {Pfeifer, Tilo and Benz, Manfred and Engelmann, Bastian and Hafner, Philip}, title = {High precision ultrasonic on-machine measurement}, series = {Measurement}, volume = {39}, journal = {Measurement}, number = {5}, pages = {407 -- 414}, language = {en} } @incollection{BosseDriessenEngelmannetal., author = {Bosse, L and Driessen, Sascha and Engelmann, Bastian and Gillner, A and Poprawe, R and Pfeifer, Tilo and Schmitt, Robert}, title = {Inline-Verfahren}, series = {Montage hybrider Mikrosysteme: Handhabungs-und F{\"u}getechniken f{\"u}r die Klein-und Mittelserienfertigung}, booktitle = {Montage hybrider Mikrosysteme: Handhabungs-und F{\"u}getechniken f{\"u}r die Klein-und Mittelserienfertigung}, doi = {10.1007/3-540-27536-3_16}, pages = {187 -- 218}, language = {en} } @article{DriessenEngelmann, author = {Driessen, Sascha and Engelmann, Bastian}, title = {Methoden und Sensoren zur {\"u}berwachung von Handhabungs-und F{\"u}geprozessen in der Mikrosystemtechnik (Methods and Sensors for the Control of Handling and Assembly Processes in Microtechnology)}, series = {tm - Technisches Messen}, volume = {71}, journal = {tm - Technisches Messen}, number = {7-8}, pages = {404 -- 416}, language = {en} } @incollection{PfeiferSchmittDriessenetal., author = {Pfeifer, Tilo and Schmitt, Robert and Driessen, Sascha and Engelmann, Bastian}, title = {Offline-Verfahren}, series = {Montage hybrider Mikrosysteme: Handhabungs-und F{\"u}getechniken f{\"u}r die Klein-und Mittelserienfertigung}, booktitle = {Montage hybrider Mikrosysteme: Handhabungs-und F{\"u}getechniken f{\"u}r die Klein-und Mittelserienfertigung}, doi = {10.1007/3-540-27536-3_15}, pages = {175 -- 186}, language = {en} } @inproceedings{SchmittEngelmann, author = {Schmitt, Robert and Engelmann, Bastian}, title = {Untersuchung von Zusammenh{\"a}ngen zwischen Oberfl{\"a}chenkenngr{\"o}ßen und Funktionsparametern}, series = {Tagungs-CD zur Konferenz Oberfl{\"a}chentage 2005 in Innsbruck}, booktitle = {Tagungs-CD zur Konferenz Oberfl{\"a}chentage 2005 in Innsbruck}, language = {en} } @inproceedings{PfeiferDriessenEngelmann, author = {Pfeifer, Tilo and Driessen, Sascha and Engelmann, Bastian}, title = {Control of the Assembly of Hybrid Micro Systems}, series = {Conference Proceeding of Photonics in Measurement}, booktitle = {Conference Proceeding of Photonics in Measurement}, pages = {357 -- 368}, language = {en} } @inproceedings{PfeiferDusslerEngelmann, author = {Pfeifer, Tilo and Dussler, G. and Engelmann, Bastian}, title = {Methods and sensors for the observation of micro assembly}, series = {Conference Proceeding of Micro System 2003 in Munich}, booktitle = {Conference Proceeding of Micro System 2003 in Munich}, pages = {139 -- 145}, language = {en} } @inproceedings{DriessenEngelmann, author = {Driessen, Sascha and Engelmann, Bastian}, title = {{\"u}berwachung von Handhabungs- und F{\"u}geprozessen in der Mikromontage}, series = {Tagungsband zur Konferenz Kolloquium Mikroproduktion}, booktitle = {Tagungsband zur Konferenz Kolloquium Mikroproduktion}, pages = {59 -- 70}, language = {en} } @inproceedings{PfeiferEngelmann, author = {Pfeifer, Tilo and Engelmann, Bastian}, title = {Process observation of the assembly of hybrid microsystems}, series = {Conference Proceeding of 12th International Conference on Experimental Mechanics in Bari}, booktitle = {Conference Proceeding of 12th International Conference on Experimental Mechanics in Bari}, language = {en} } @inproceedings{PfeiferDriessenEngelmann, author = {Pfeifer, Tilo and Driessen, Sascha and Engelmann, Bastian}, title = {Control for the Assembly of Micro Systems}, series = {Conference Proceeding of 8th International Symposium on Measurement and Quality Control in Production in Erlangen}, booktitle = {Conference Proceeding of 8th International Symposium on Measurement and Quality Control in Production in Erlangen}, pages = {603 -- 608}, language = {en} } @article{SchmittEngelmannManghisietal., author = {Schmitt, Jan and Engelmann, Bastian and Manghisi, Vito Modesto and Wilhelm, Markus and Uva, Antonello and Fiorentino, Michele}, title = {Towards gestured-based technologies for human-centred smart factories}, series = {International Journal of Computer Integrated Manufacturing}, volume = {36}, journal = {International Journal of Computer Integrated Manufacturing}, number = {1}, issn = {1362-3052}, pages = {110 -- 127}, abstract = {Despite the increasing degree of automation in industry, manual or semi-automated are commonly and inevitable for complex assembly tasks. The transformation to smart processes in manufacturing leads to a higher deployment of data-driven approaches to support the worker. Upcoming technologies in this context are oftentimes based on the gesture-recognition, - monitoring or - control. This contribution systematically reviews gesture or motion capturing technologies and the utilization of gesture data in the ergonomic assessment, gesture-based robot control strategies as well as the identification of COVID-19 symptoms. Subsequently, two applications are presented in detail. First, a holistic human-centric optimization method for line-balancing using a novel indicator - ErgoTakt - derived by motion capturing. ErgoTakt improves the legacy takt-time and helps to find an optimum between the ergonomic evaluation of an assembly station and the takt-time balancing. An optimization algorithm is developed to find the best-fitting solution by minimizing a function of the ergonomic RULA-score and the cycle time of each assembly workstation with respect to the workers' ability. The second application is gesture-based robot-control. A cloud-based approach utilizing a generally accessible hand-tracking model embedded in a low-code IoT programming environment is shown.}, language = {en} } @inproceedings{HartmannDobhanEngelmannetal., author = {Hartmann, J{\"u}rgen and Dobhan, Alexander and Engelmann, Bastian and Eberhardt, Lars and Heusinger, Moritz and Raab, C and Schleif, Frank-Michael and T{\"u}rk, M.}, title = {Optimierung von Prozessen und Werkzeugmaschinen durch Bereitstellung, Analyse und Soll-Ist-Vergleich von Produktionsdaten: Digitalkonferenz}, language = {en} } @article{WilhelmManghisiUvaetal., author = {Wilhelm, Markus and Manghisi, Vito Modesto and Uva, Antonello and Fiorentino, Michele and Br{\"a}utigam, Volker and Engelmann, Bastian and Schmitt, Jan}, title = {ErgoTakt: A novel approach of human-centered balancing of manual assembly lines}, series = {Procedia CIRP}, volume = {97}, journal = {Procedia CIRP}, doi = {https://doi.org/10.1016/j.procir.2020.05.250}, pages = {354 -- 360}, abstract = {Although the increasing use of automation in industry, manual assembly stations are still common and, in some situations, even inevitable. Current practice in manual assembly lines is to balance them using the takt-time of each workstation and harmonize it. However, this approach mostly does not include ergonomic aspects and thus it may lead to workforce musculoskeletal disorders, extended leaves, and demotivation. This paper presents a holistic human-centric optimization method for line balancing using a novel indicator ̶ the ErgoTakt. ErgoTakt improves the legacy takt-time and helps to find an optimum between the ergonomic evaluation of an assembly station and its balance in time. The authors used a custom version of the ErgoSentinel Software and a Microsoft Kinect depth camera to perform online and real-time ergonomic assessment. An optimization algorithm is developed to find the best-fitting solution by minimizing a function of the ergonomic RULA-value and the cycle time of each assembly workstation with respect to the worker's ability. The paper presents the concept, the system-setup and preliminary evaluation of an assembly scenario. The results demonstrate that the new approach is feasible and able to optimize an entire manual assembly process chain in terms of both, economic aspects of a well-balanced production line as well as the ergonomic issue of long term human healthy work.}, language = {en} } @article{MillerBorysenkoHeusingeretal., author = {Miller, Eddi and Borysenko, Vladyslav and Heusinger, Moritz and Niedner, Niklas and Engelmann, Bastian and Schmitt, Jan}, title = {Enhanced Changeover Detection in Industry 4.0 Environments with Machine Learning}, series = {Sensors}, volume = {21}, journal = {Sensors}, number = {17}, pages = {5896 -- 5896}, language = {en} } @article{SchusterEngelmannSponholzetal., author = {Schuster, Florian and Engelmann, Bastian and Sponholz, Uwe and Schmitt, Jan and Engineering, Institute Digital}, title = {Human acceptance evaluation of AR-assisted assembly scenarios}, series = {Journal of Manufacturing Systems}, volume = {61}, journal = {Journal of Manufacturing Systems}, pages = {660 -- 672}, language = {en} } @inproceedings{SchusterSponholzEngelmannetal., author = {Schuster, Florian and Sponholz, Uwe and Engelmann, Bastian and Schmitt, Jan}, title = {A user study on AR-assisted industrial assembly}, series = {2020 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)}, booktitle = {2020 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)}, pages = {135 -- 140}, language = {en} } @inproceedings{RosiliusWirsingvonEitzenetal., author = {Rosilius, Maximilian and Wirsing, Benedikt and von Eitzen, Ingo and Wilhelm, Markus and Schmitt, Jan and Engelmann, Bastian and Br{\"a}utigam, Volker}, title = {Evaluation of Visual Requirements and Software-Design for Immersive Visibility in Industrial Applications}, series = {2021 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)}, booktitle = {2021 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)}, pages = {234 -- 239}, abstract = {Currently, many sources predict increasing use of AR technology in the industrial environment. The task of immersive productive assistance systems is to provide information contextually to the industrial user. Therefore, it is essential to explore the factors and effects that influence the visibility and the corresponding quality of this information. Caused by the technical limitations of additive display technology and application conditions, this new approach has evaluated the immersive visibility of Landolt Rings in various greyscales against ambient illuminance levels on different industrial-like surfaces, coupled with and without a white virtual background. For this purpose, an empirical study in a within-subjects-design with full factorial experimental design (n=23) was conducted on Microsoft HoloLens 2 hardware. The mean values of the main effects indicate that visibility is significantly affected by ambient illuminance (best results at lower level), greyscale (best results at middle level) and virtual background (best results with background). In contrast, the choice of surface is shown to have no statistically significant effect on visibility, however it affects the response time. Additionally, cross-interactions of variables were analyzed and lead to a design recommendation for immersive industrial applications.}, language = {en} } @article{MillerCeballosEngelmannetal., author = {Miller, Eddi and Ceballos, Hector and Engelmann, Bastian and Schiffler, Andreas and Batres, Rafael and Schmitt, Jan}, title = {Industry 4.0 and International Collaborative Online Learning in a Higher Education Course on Machine Learning}, series = {2021 Machine Learning-Driven Digital Technologies for Educational Innovation Workshop}, journal = {2021 Machine Learning-Driven Digital Technologies for Educational Innovation Workshop}, pages = {1 -- 8}, language = {en} } @incollection{EngelmannSchmitt, author = {Engelmann, Bastian and Schmitt, Jan}, title = {Industrie 4.0 f{\"u}r Studierende des Wirtschaftsingenieurwesens}, series = {Kompetenzen f{\"u}r die digitale Transformation 2020: Digitalisierung der Arbeit-Kompetenzen-Nachhaltigkeit 1. Digitalkompetenz-Tagung}, booktitle = {Kompetenzen f{\"u}r die digitale Transformation 2020: Digitalisierung der Arbeit-Kompetenzen-Nachhaltigkeit 1. Digitalkompetenz-Tagung}, pages = {265 -- 273}, language = {de} } @article{NeuberSchmittEngelmannetal., author = {Neuber, Till and Schmitt, Anna-Maria and Engelmann, Bastian and Schmitt, Jan}, title = {Evaluation of the Influence of Machine Tools on the Accuracy of Indoor Positioning Systems}, series = {Sensors}, volume = {22}, journal = {Sensors}, number = {24}, pages = {10015 -- 10015}, language = {en} } @article{MillerEngelmannKauppetal., author = {Miller, Eddi and Engelmann, Bastian and Kaupp, Tobias and Schmitt, Jan}, title = {Advanced Cascaded Scheduling for Highly Autonomous Production Cells with Material Flow and Tool Lifetime Consideration using AGVs}, series = {Journal of Machine Engineering}, journal = {Journal of Machine Engineering}, issn = {2391-8071}, language = {en} } @article{EngelmannSchmittMilleretal., author = {Engelmann, Bastian and Schmitt, Simon and Miller, Eddi and Br{\"a}utigam, Volker and Schmitt, Jan}, title = {Advances in machine learning detecting changeover processes in cyber physical production systems}, series = {Journal of Manufacturing and Materials Processing}, volume = {4}, journal = {Journal of Manufacturing and Materials Processing}, number = {4}, pages = {108 -- 108}, abstract = {The performance indicator, Overall Equipment Effectiveness (OEE), is one of the most important ones for production control, as it merges information of equipment usage, process yield, and product quality. The determination of the OEE is oftentimes not transparent in companies, due to the heterogeneous data sources and manual interference. Furthermore, there is a difference in present guidelines to calculate the OEE. Due to a big amount of sensor data in Cyber Physical Production Systems, Machine Learning methods can be used in order to detect several elements of the OEE by a trained model. Changeover time is one crucial aspect influencing the OEE, as it adds no value to the product. Furthermore, changeover processes are fulfilled manually and vary from worker to worker. They always have their own procedure to conduct a changeover of a machine for a new product or production lot. Hence, the changeover time as well as the process itself vary. Thus, a new Machine Learning based concept for identification and characterization of machine set-up actions is presented. Here, the issue to be dealt with is the necessity of human and machine interaction to fulfill the entire machine set-up process. Because of this, the paper shows the use case in a real production scenario of a small to medium size company (SME), the derived data set, promising Machine Learning algorithms, as well as the results of the implemented Machine Learning model to classify machine set-up actions.}, language = {en} } @inproceedings{HartmannDzemkoEngelmannetal., author = {Hartmann, J{\"u}rgen and Dzemko, Mikita and Engelmann, Bastian and Schmitt, Jan}, title = {Toward Shifted Production Strategies Through Additive Manufacturing: A Technology and Market Review for Changing Value Chains}, series = {7th CIRP Global Web Conference (86)}, volume = {86}, booktitle = {7th CIRP Global Web Conference (86)}, doi = {10.1016/j.procir.2020.01.029}, pages = {228 -- 233}, abstract = {In the last decade many different additive manufacturing (AM) technologies for metal, plastic or ceramic processing raise from research to commercialization. As a result, AM grows into different business areas and transforms structures and processes. Hence, the contribution tends to show the change in added values though the availability of different additive manufacturing technologies based on a technology screening and market research. Regarding the named purpose, a broad market research of 83 companies and 339 printer models has been conducted to find patterns of AM technology market share and regions to structure indicators such as accuracy by processed material classes with a specified AM method. Printing materials as metal, plastic, ceramic and carbon have been considered. The categorization is done by the AM principles: power bed fusion, material extrusion, vat photopolymerization and …}, language = {en} } @article{MillerBarthelmeSchiffleretal., author = {Miller, Eddi and Barthelme, Christine and Schiffler, Andreas and Engelmann, Bastian and Schmitt, Jan}, title = {Internationalisierung in Pandemiezeiten, technische M{\"o}glichkeiten, Lehr- und Forschungskonzepte mal anders gedacht}, series = {FHWS Science Journal}, volume = {5}, journal = {FHWS Science Journal}, number = {2}, issn = {2196-6095}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:863-opus-20035}, pages = {143 -- 146}, abstract = {Eines der zentralen strategischen Ziele unserer Hochschule ist die Internationalisierung, sowie der »internationalisation@home«. Als die weltweite Corona-Pandemie die Pr{\"a}senzlehre und -forschung ebenso wie den internationalen Austausch von Studierenden und Forschenden zu Beginn 2020 quasi zum Erliegen brachte wurden die Rufe nach digitalen Angeboten im internationalen Bereich schnell laut. Vor diesem Hintergrund reagierte der »Deutsche Akademische Auslandsdienst (DAAD)« mit der kurzfristig ins Leben gerufenen F{\"o}rderlinie »International Virtual Academic Collaboration« (IVAC), um internationale Hochschulkooperationen und weltweite Mobilit{\"a}t unter digitalen Vorzeichen strategisch zu gestalten und auszubauen [1].}, language = {de} } @inproceedings{SeitzSchmittEngelmann, author = {Seitz, Philipp and Schmitt, Jan and Engelmann, Bastian}, title = {Evaluation of proceedings for SMEs to conduct I4.0 projects}, series = {Procedia Cirp}, volume = {86}, booktitle = {Procedia Cirp}, pages = {257 -- 263}, language = {en} } @article{HofmannEberhardtHeusingeretal., author = {Hofmann, Jan and Eberhardt, Lars and Heusinger, Moritz and Dobhan, Alexander and Engelmann, Bastian and Schleif, Frank-Michael}, title = {Optimierung von Prozessen und Werkzeugmaschinen durch Bereitstellung, Analyse und Soll-Ist-Vergleich von Produktionsdaten}, series = {FHWS Science Journal}, volume = {5}, journal = {FHWS Science Journal}, number = {2}, issn = {2196-6095}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:863-opus-20012}, pages = {135 -- 142}, abstract = {Mit einem Umsatz von 103 Milliarden Euro ist die Metallindustrie eine der gr{\"o}ßten deutschen Industriebranchen. Diese ist von volatilen Marktbedingungen und hohem Wettbewerb gepr{\"a}gt [1][2]. Kleine und mittlere produzierende Unternehmen (sogenannte KMU) sehen zunehmend gravierende Probleme bei der Einhaltung von Lieferterminen bedingt durch hohe Durchlaufzeiten in der Produktion [3]. Neben kaufm{\"a}nnischen Planungssystemen zur Erstellung von Produktionspl{\"a}nen nutzen Unternehmen als Planungsgrundlage weiterhin Excel mit 31 \% und manuelle Prozesse mit 10 \% [4]. Gleiches gilt f{\"u}r Produktwechselvorg{\"a}nge auf Maschinen (R{\"u}sten). Aufgrund dieser Aspekte ist es notwendig, die Rentabilit{\"a}t der KMU in der Metallindustrie zu steigern. Das wird durch effiziente Produktionsplanung und -steuerung, sowie der daraus resultierenden hohen Reaktionsf{\"a}higkeit und Flexibilit{\"a}t realisiert. Daher ist die Produktionsplanung auf die Markt- und Kundenanforderungen und die Anlageneffektivit{\"a}t auf ein hohes und stabiles Niveau auszurichten [5]. Hier bietet die Erfassung von Echtzeitdaten eine ad{\"a}quate Reaktion auf die genannten Anforderungen. Ebenfalls liefert sie großes Potenzial f{\"u}r die Produktionsplanung und -steuerung, um die Disposition und Koordination von Arbeitsauftr{\"a}gen zu optimieren. Weiterhin werden St{\"o}rgr{\"o}ßen oder unvorhergesehene Planungsabweichungen reduziert [4][6]. Zus{\"a}tzlich ist eine erh{\"o}hte Transparenz und Verbesserung menschlicher Entscheidungsprozesse notwendig. Dies kann durch datengetriebene Methoden unterst{\"u}tzt und sichergestellt werden [7]. Ein Ansatz zur Optimierung des Produktionsergebnisses kann durch die Erh{\"o}hung der Anlagenproduktivit{\"a}t selbst realisiert werden. Dazu muss die Verf{\"u}gbarkeit der Anlagen durch Lokalisierung und Reduzierung von Verlusten erh{\"o}ht werden. Die Umr{\"u}stungsprozesse tragen stark negativ zur Verf{\"u}gbarkeit einer Produktion bei. Eine Steigerung der Gesamtanlageneffektivit{\"a}t (overall equipment effectiveness oder kurz OEE) in einer Fertigungsumgebung ist jedoch m{\"o}glich durch eine intelligente Nutzung von Sensordaten mit Techniken wie z. B. Machine Learning (ML).}, language = {de} } @article{EngelmannSchmittTheilackeretal., author = {Engelmann, Bastian and Schmitt, Anna-Maria and Theilacker, Lukas and Schmitt, Jan}, title = {Implications from Legacy Device Environments on the Conceptional Design of Machine Learning Models in Manufacturing}, series = {Journal of Manufacturing and Materials Processing}, volume = {2024}, journal = {Journal of Manufacturing and Materials Processing}, language = {en} } @article{GeorgeBijuSchmittEngelmann, author = {George Biju, Vinai and Schmitt, Anna-Maria and Engelmann, Bastian}, title = {Assessing the Influence of Sensor-Induced Noise on Machine-Learning-Based Changeover Detection in CNC Machines}, series = {Sensors}, volume = {2024}, journal = {Sensors}, language = {en} } @article{SchmittMillerEngelmannetal., author = {Schmitt, Anna-Maria and Miller, Eddi and Engelmann, Bastian and Batres, Rafael and Schmitt, Jan}, title = {G-code evaluation in CNC milling to predict energy consumption through Machine Learning}, series = {Advances in Industrial and Manufacturing Engineering}, volume = {2024}, journal = {Advances in Industrial and Manufacturing Engineering}, number = {8}, abstract = {Computerized Numeric Control (CNC) plays an essential role in highly autonomous manufacturing systems for interlinked process chains for machine tools. NC-programs are mostly written in standardized G-code. Evaluating CNC-controlled manufacturing processes before their real application is advantageous due to resource efficiency. One dimension is the estimation of the energy demand of a part manufactured by an NC-program, e.g. to discover optimization potentials. In this context, this paper presents a Machine Learning (ML) approach to assess G-code for CNC-milling processes from the perspective of the energy demand of basic G-commands. We propose Latin Hypercube Sampling as an efficient method of Design of Experiments to train the ML model with minimum experimental effort to avoid costly setup and implementation time of the model training and deployment.}, language = {en} } @article{LangEngelmannSchiffleretal.2024, author = {Lang, Silvio and Engelmann, Bastian and Schiffler, Andreas and Schmitt, Jan}, title = {A simplified machine learning product carbon footprint evaluation tool}, series = {Cleaner Environmental Systems}, volume = {13}, journal = {Cleaner Environmental Systems}, publisher = {Elsevier BV}, issn = {2666-7894}, doi = {10.1016/j.cesys.2024.100187}, year = {2024}, abstract = {On the way to climate neutrality manufacturing companies need to assess the Carbon dioxide (CO2) emissions of their products as a basis for emission reduction measures. The evaluate this so-called Product Carbon Footprint (PCF) life cycle analysis as a comprehensive method is applicable, but means great effort and requires interdisciplinary knowledge. Nevertheless, assumptions must still be made to assess the entire supply chain. To lower these burdens and provide a digital tool to estimate the PCF with less input parameter and data, we make use of machine learning techniques and develop an editorial framework called MINDFUL. This contribution shows its realization by providing the software architecture, underlying CO2 factors, calculations and Machine Learning approach as well as the principles of its user experience. Our tool is validated within an industrial case study.}, language = {en} }