14 research outputs found
Machine Learning Methods for Product Quality Monitoring in Electric Resistance Welding
Elektrisches WiderstandsschweiĂen (Englisch: Electric Resistance Welding, ERW) ist eine Gruppe von vollautomatisierten Fertigungsprozessen, bei denen metallische Werkstoffe durch WĂ€rme verbunden werden, die von elektrischem Strom und Widerstand erzeugt wird. Eine genaue QualitĂ€tsuÌberwachung von ERW kann oft nur teilweise mit destruktiven Methoden durchgefuÌhrt werden. Es besteht ein groĂes industrielles und wirtschaftliches Potenzial, datengetriebene AnsĂ€tze fuÌr die QualitĂ€tsuÌberwachung in ERW zu entwickeln, um die Wartungskosten zu senken und die QualitĂ€tskontrolle zu verbessern. Datengetriebene AnsĂ€tze wie maschinelles Lernen (ML) haben aufgrund der enormen Menge verfuÌgbarer Daten, die von Technologien der Industrie 4.0 bereitgestellt werden, viel Aufmerksamkeit auf sich gezogen. Datengetriebene AnsĂ€tze ermöglichen eine zerstörungsfreie, umfassende und prĂ€zise QualitĂ€tsuÌberwachung, wenn eine bestimmte Menge prĂ€ziser Daten verfuÌgbar ist. Dies kann eine umfassende Online-QualitĂ€tsuÌberwachung ermöglichen, die ansonsten mit herkömmlichen empirischen Methoden Ă€uĂerst schwierig ist.
Es gibt jedoch noch viele Herausforderungen bei der Adoption solcher AnsĂ€tze in der Fertigungsindustrie. Zu diesen Herausforderungen gehören: effiziente Datensammlung, die dasWissen von erforderlichen Datenmengen und relevanten Sensoren fuÌr erfolgreiches maschinelles Lernen verlangt; das anspruchsvolle Verstehen von komplexen Prozessen und facettenreichen Daten; eine geschickte Selektion geeigneter ML-Methoden und die Integration von DomĂ€nenwissen fuÌr die prĂ€diktive QualitĂ€tsuÌberwachung mit inhomogenen Datenstrukturen, usw.
Bestehende ML-Lösungen fuÌr ERW liefern keine systematische Vorgehensweise fuÌr die Methodenauswahl. Jeder Prozess der ML-Entwicklung erfordert ein umfassendes Prozess- und DatenverstĂ€ndnis und ist auf ein bestimmtes Szenario zugeschnitten, das schwer zu verallgemeinern ist. Es existieren semantische Lösungen fuÌr das Prozess- und DatenverstĂ€ndnis und Datenmanagement. Diese betrachten die Datenanalyse als eine isolierte Phase. Sie liefern keine Systemlösungen fuÌr das Prozess- und DatenverstĂ€ndnis, die Datenaufbereitung und die ML-Verbesserung, die konfigurierbare und verallgemeinerbare Lösungen fuÌr maschinelles Lernen ermöglichen.
Diese Arbeit versucht, die obengenannten Herausforderungen zu adressieren, indem ein Framework fĂŒr maschinelles Lernen fuÌr ERW vorgeschlagen wird, und demonstriert fuÌnf industrielle AnwendungsfĂ€lle, die das Framework anwenden und validieren. Das Framework ĂŒberprĂŒft die Fragen und DatenspezifitĂ€ten, schlĂ€gt eine simulationsunterstuÌtzte Datenerfassung vor und erörtert Methoden des maschinellen Lernens, die in zwei Gruppen unterteilt sind: Feature Engineering und Feature Learning. Das Framework basiert auf semantischen Technologien, die eine standardisierte Prozess- und Datenbeschreibung, eine Ontologie-bewusste Datenaufbereitung sowie halbautomatisierte und Nutzer-konfigurierbare ML-Lösungen ermöglichen. Diese Arbeit demonstriert auĂerdem die Ăbertragbarkeit des Frameworks auf einen hochprĂ€zisen Laserprozess.
Diese Arbeit ist ein Beginn des Wegs zur intelligenten Fertigung von ERW, der mit dem Trend der vierten industriellen Revolution korrespondiert
Real-Time Event Detection with Random Forests and Temporal Convolutional Networks for More Sustainable Petroleum Industry
The petroleum industry is crucial for modern society, but the production
process is complex and risky. During the production, accidents or failures,
resulting from undesired production events, can cause severe environmental and
economic damage. Previous studies have investigated machine learning (ML)
methods for undesired event detection. However, the prediction of event
probability in real-time was insufficiently addressed, which is essential since
it is important to undertake early intervention when an event is expected to
happen. This paper proposes two ML approaches, random forests and temporal
convolutional networks, to detect undesired events in real-time. Results show
that our approaches can effectively classify event types and predict the
probability of their appearance, addressing the challenges uncovered in
previous studies and providing a more effective solution for failure event
management during the production.Comment: Paper accepted at PRICAI 2023 AI-Impact Trac
Comparison of Machine Learning Approaches for Time-series-based Quality Monitoring of Resistance Spot Welding (RSW)
In automatic manufacturing, enormous amounts of data are generated every day. However, labeled production data useful for data analysis is difficult to acquire. Resistance spot welding (RSW), widely applied in automobile production, is a typical automatic manufacturing process with inhomogeneous data structures as well as statistical and systematic dynamics. In resistance spot welding, an electric current flows through electrodes and the materials in between. The materials are first heated and melted, then congeal, forming what is known as a weld nugget, joining the materials together. The nugget size is an important quality indicator, but can only be precisely obtained by using costly destructive methods. This paper strives to address the issue of the scarcity of labeled data by using simulation data generated with a verified finite element model. Physics-based simulation enables large amounts of labeled data to be generated with fewer limits on sensors and costs. Based on the simulation data, this paper explores and compares multiple machine learning methods, predicts the nugget size with a high degree of accuracy, and conducts an analysis of the influence of feature number and amount of training data on prediction accuracy
Towards Ontology Reshaping for KG Generation with User-in-the-Loop: Applied to Bosch Welding
Knowledge graphs (KG) are used in a wide range of applications. The
automation of KG generation is very desired due to the data volume and variety
in industries. One important approach of KG generation is to map the raw data
to a given KG schema, namely a domain ontology, and construct the entities and
properties according to the ontology. However, the automatic generation of such
ontology is demanding and existing solutions are often not satisfactory. An
important challenge is a trade-off between two principles of ontology
engineering: knowledge-orientation and data-orientation. The former one
prescribes that an ontology should model the general knowledge of a domain,
while the latter one emphasises on reflecting the data specificities to ensure
good usability. We address this challenge by our method of ontology reshaping,
which automates the process of converting a given domain ontology to a smaller
ontology that serves as the KG schema. The domain ontology can be designed to
be knowledge-oriented and the KG schema covers the data specificities. In
addition, our approach allows the option of including user preferences in the
loop. We demonstrate our on-going research on ontology reshaping and present an
evaluation using real industrial data, with promising results
Scaling Data Science Solutions with Semantics and Machine Learning: Bosch Case
Industry 4.0 and Internet of Things (IoT) technologies unlock unprecedented
amount of data from factory production, posing big data challenges in volume
and variety. In that context, distributed computing solutions such as cloud
systems are leveraged to parallelise the data processing and reduce computation
time. As the cloud systems become increasingly popular, there is increased
demand that more users that were originally not cloud experts (such as data
scientists, domain experts) deploy their solutions on the cloud systems.
However, it is non-trivial to address both the high demand for cloud system
users and the excessive time required to train them. To this end, we propose
SemCloud, a semantics-enhanced cloud system, that couples cloud system with
semantic technologies and machine learning. SemCloud relies on domain
ontologies and mappings for data integration, and parallelises the semantic
data integration and data analysis on distributed computing nodes. Furthermore,
SemCloud adopts adaptive Datalog rules and machine learning for automated
resource configuration, allowing non-cloud experts to use the cloud system. The
system has been evaluated in industrial use case with millions of data,
thousands of repeated runs, and domain users, showing promising results.Comment: Paper accepted at ISWC2023 In-Use trac
Literal-Aware Knowledge Graph Embedding for Welding Quality Monitoring: A Bosch Case
Recently there has been a series of studies in knowledge graph embedding
(KGE), which attempts to learn the embeddings of the entities and relations as
numerical vectors and mathematical mappings via machine learning (ML). However,
there has been limited research that applies KGE for industrial problems in
manufacturing. This paper investigates whether and to what extent KGE can be
used for an important problem: quality monitoring for welding in manufacturing
industry, which is an impactful process accounting for production of millions
of cars annually. The work is in line with Bosch research of data-driven
solutions that intends to replace the traditional way of destroying cars, which
is extremely costly and produces waste. The paper tackles two very challenging
questions simultaneously: how large the welding spot diameter is; and to which
car body the welded spot belongs to. The problem setting is difficult for
traditional ML because there exist a high number of car bodies that should be
assigned as class labels. We formulate the problem as link prediction, and
experimented popular KGE methods on real industry data, with consideration of
literals. Our results reveal both limitations and promising aspects of adapted
KGE methods.Comment: Paper accepted at ISWC2023 In-Use trac
Addressing the Scalability Bottleneck of Semantic Technologies at Bosch
At the heart of smart manufacturing is real-time semi-automatic
decision-making. Such decisions are vital for optimizing production lines,
e.g., reducing resource consumption, improving the quality of discrete
manufacturing operations, and optimizing the actual products, e.g., optimizing
the sampling rate for measuring product dimensions during production. Such
decision-making relies on massive industrial data thus posing a real-time
processing bottleneck
Practical methods for detecting and removing transient changes in univariate oscillatory time series
Machine learning aided phase retrieval algorithm for beam splitting with an LCoS-SLM
Liquid crystal on silicon phase-only spatial light modulators are widely used for the generation of multi-spot patterns. The phase distribution in the modulator plane, corresponding to the target multi-spot intensity distribution in the focal plane, is calculated by means of the so-called phase retrieval algorithms. Due to deviations of the real optical setup from the ideal model, these algorithms often do not achieve the desired power distribution accuracy within the multi-spot patterns. In this study, we present a novel method for generating high quality multi-spot patterns even in the presence of optical system disturbances. The standard Iterative Fourier Transform Algorithm is extended by means of machine learning methods combined with an open camera feedback loop. The machine learning algorithm is used to predict the mapping function between the desired and the measured multi-spot beam profiles. The problem of generation of multispot patterns is divided into three complexity levels. Due to distinct parameter structures, each of the complexity levels requires differing solution approaches, particularly differing machine learning algorithms. This relation is discussed in detail eventually providing a solution for the simplest case of beam splitter pattern generation. Solutions for more complex problems are also suggested. The approach is validated, whereby one machine learning method is successfully implemented and tested experimentally
SemML: Facilitating development of ML models for condition monitoring with semantics
Monitoring of the state, performance, quality of operations and other parameters of equipment and production processes, which is typically referred to as condition monitoring, is an important common practice in many industries including manufacturing, oil and gas, chemical and process industry. In the age of Industry 4.0, where the aim is a deep degree of production automation, unprecedented amounts of data are generated by equipment and processes, and this enables adoption of Machine Learning (ML) approaches for condition monitoring. Development of such ML models is challenging. On the one hand, it requires collaborative work of experts from different areas, including data scientists, engineers, process experts, and managers with asymmetric backgrounds. On the other hand, there is high variety and diversity of data relevant for condition monitoring. Both factors hampers ML modelling for condition monitoring. In this work, we address these challenges by empowering ML-based condition monitoring with semantic technologies. To this end we propose a software system SemML that allows to reuse and generalise ML pipelines for conditions monitoring by relying on semantics. In particular, SemML has several novel components and relies on ontologies and ontology templates for ML task negotiation and for data and ML feature annotation. SemML also allows to instantiate parametrised ML pipelines by semantic annotation of industrial data. With SemML, users do not need to dive into data and ML scripts when new datasets of a studied application scenario arrive. They only need to annotate data and then ML models will be constructed through the combination of semantic reasoning and ML modules. We demonstrate the benefits of SemML on a Bosch use-case of electric resistance welding with very promising results