A Framework for Data Integration and Analysis in Radial-Axial Ring Rolling

Data-driven analytical approaches such as machine learning bear great potential for increasing productivity in industrial applications. The primary requirement for using those approaches is data. The challenge is to not only have any kind of data but data which has been transformed into an analytically useful form. Building upon this initial requirement, this paper presents the current state concerning data analysis and data integration in the industrial branch of hot forming, specifically focussing on radial-axial ring rolling. The state of the art is represented by the results of a data survey which was completed by six of Germany’s representing radial-axial ring rolling companies. The survey’s centre of interest focuses on how data is currently stored and analysed and how it gets depicted into eight different statements. Based on the results of the survey a framework is proposed to integrate data of the whole production process of ring rolling (furnace, punch, ring rolling machine, heat treatment and quality inspection) so that data-driven techniques can be applied to reduce form and process errors. The proposed framework takes into account that a generalized standard is hard to set because of already grown structures and a huge variety of analytical methods. Therefore, the framework focuses on data integration issues commonly found in an industrial setting as opposed to controlled research environments. The paper proposes methodologies on how to utilize the potential of each company's data. As a result, the proposed framework creates awareness for saving the data in a standardized and thoughtful manner as well as building a data-driven culture within the company

Approach to a Decision Support Method for Feature Engineering of a Classification of Hydraulic Directional Control Valve Tests

Advancing digitalization and high computing power are drivers for the progressive use of machine learning (ML) methods on manufacturing data. Using ML for predictive quality control of product characteristics contributes to preventing defects and streamlining future manufacturing processes. Challenging decisions must be made before implementing ML applications. Production environments are dynamic systems whose boundary conditions change continuously. Accordingly, it requires extensive feature engineering of the volatile database to guarantee high generalizability of the prediction model. Thus, all following sections of the ML pipeline can be optimized based on a cleaned database. Various ML methods such gradient boosting methods have achieved promising results in industrial hydraulic use cases so far. For every prediction model task, there is the challenge of making the right choice of which method is most appropriate and which hyperparameters achieve the best predictions. The goal of this work is to develop a method for selecting the best feature engineering methods and hyperparameter combination of a predictive model for a dataset with temporal variability that treats both as equivalent parameters and optimizes them simultaneously. The optimization is done via a workflow including a random search. By applying this method, a structured procedure for achieving significant leaps in performance metrics in the prediction of hydraulic test steps of directional valves is achieved

Transfer Learning Approaches In The Domain Of Radial-Axial Ring Rolling For Machine Learning Applications

Due to increased data accessibility, data-centric approaches, such as machine learning, are getting more represented in the forming industry to improve resource efficiency and to optimise processes. Prior research shows, that a classification of the roundness of shaped rings, using machine learning algorithms, is applicable to radial-axial ring rolling. The accuracy of these predictions nowadays is still limited by the amount and quality of the data. Therefore, this paper will focus on how to make the best use of the limited amount of data, using transfer learning approaches. Since acquiring data for homogenised databases is time, energy and resource consuming, logged data gathered by the industry is often used in research. This paper takes both, industrial data from thyssenkrupp rothe erde Germany GmbH and a smaller dataset of an inhouse research plant, into account. Additionally, a synthetic dataset, created by generative adversarial networks, is considered. To accomplish an improvement of machine learning predictions using accessible data, three transfer learning approaches are investigated in order to extend existing models: (I) transferring from a radial-axial ring rolling mill to a different mill containing less available data with a ratio of 20:1, (II) learning from unlabelled data using an autoencoder and (III) training on synthetic data. The obtained improvements are further evaluated. Based on these results, future possible investigations are elaborated, in particular the consideration of transfer learning from the less complex cold ring rolling process

Entwicklung eines maschinellen Lernansatzes zur Qualitätsverbesserung im Radial-Axial Ringwalzen durch Zeitreihenklassifikation

Technical innovations and decades of research have allowed the process of radial-axial ring rolling to grow into a significant manufacturing process for seamlessly formed ring-shaped components. Recent developments in machine learning, especially the breakthrough of deep neural networks, offer novel opportunities in various industrial fields. Linking the latest machine learning models with ring rolling process data enables optimization of the rolling process. This optimization aims at increased resource and cost efficiency by reducing material additions and avoiding scrap. This is implemented by developing a time series classification model for quality prediction and extending it to a time series model for early prediction of ovality while rolling is still in progress.Technische Innovationen und jahrzehntelange Forschung haben den Prozess des Radial-Axial Ringwalzens zu einem bedeutsamen Herstellungsprozess für nahtlos geformte ringförmige Bauteile wachsen lassen. Neuste Entwicklungen im Bereich des Maschinellen Lernens, vor allem der Durchbruch tiefer neuronaler Netze, bieten neuartige Möglichkeiten in den verschiedenen Industriebereichen. Eine Verknüpfung neuester maschineller Lernmodelle mit den Prozessdaten des Ringwalzens ermöglicht die Optimierung des Walzprozesses. Diese Optimierung zielt auf eine erhöhte Ressourcen- und Kosteneffizienz durch die Reduzierung von Materialzugaben und die Vermeidung von Ausschuss ab. Umgesetzt wird dies durch die Entwicklung eines Zeitreihenklassifikationsmodells zur Qualitätsvorhersage und der Erweiterung zu einem Zeitreihenmodell für die frühzeitige Vorhersage von auftretenden Unrundheiten noch während die Walzung stattfindet

Investigation Of Suitable Methods For An Early Classification On Time Series In Radial-Axial Ring Rolling

To increase competitiveness in the hot forming sector, there is a constant urge to improve the rolling process and its products. Industry 4.0 and its impact on data acquisition and data availability enable data driven methods for optimization. In order to optimize the quality prediction of rolled rings in Radial-Axial Ring Rolling (RARR) with regard to ovality as early as possible and hence prevent scrap and unnecessary rework, machine learning methods from the early classification on time series subdomain are used and evaluated within this research. Different approaches from the time series classification domain within supervised learning are used and compared. A so-called minimum prediction length of the ring rolling process time series is analysed using real world production data from thyssenkrupp rothe erde Germany GmbH. Building upon results of earlier research regarding the use of time series classification in RARR by FAHLE ET AL. fully automated as well as domain specific minimum prediction lengths will be investigated. The results of both approaches are compared and evaluated with regards to the current maximum prediction accuracy using the whole sequences, which should provide the highest score as it holds all available information of each sample

Feature Engineering For A Cross-process Quality Prediction Of An End-of-line Hydraulic Leakage Test Using An Experiment Sample

The increasing availability of manufacturing data and advanced analysis tools are forcing the demand for data-driven approaches to improve the quality of workpieces and the efficiency of manufacturing processes. The analysis of real manufacturing data is challenging due to frequent changes in production circumstances. In this work, machine learning methods based on the data along the value chain of hydraulic valves are used to predict the leakage results during end-of-line testing. The leakage volume flow measurement results are very sensitive to changes in gap geometry and temperature level in the measurement cross-section. Additional measurements and experiments are required to interpret the systematic influences of the input data on the target variable and to introduce the missing information into the dataset. The design of a metamodel using experiment data supports the identification of statistical patterns to be applied to the real production dataset as a feature. This paper presents a systematic approach to hand-crafted feature engineering that improves the quality prediction of end-of-line hydraulic leakage testing

Combining Process Monitoring with Text Mining for Anomaly Detection in Discrete Manufacturing

One of the major challenges of today's manufacturing industry is the reliable detection of process anomalies and failures in order to reduce unplanned downtimes and avoid quality issues. Process Monitoring (PM) requires the existence of a Normal Operating Condition (NOC) dataset that is used to train the respective algorithm. Obtaining such a NOC dataset involves extensive test runs aside from the actual production. Machine operators often collect a variety of unstructured process specific data in form of protocols, that contain valuable information about the process condition. We propose an approach that utilizes such text data to efficiently create the NOC dataset for a machining process in one of our learning factories. Using the NOC high-frequency machine sensor readings, we train a principal component analysis (PCA)-based model, which can identify anomalous process behavior. The model is consequently evaluated on a holdout test data set and shows promising results. Estimations of the process condition are visualized with two control charts allowing intuitive insights for the machine operator

Improving quality prediction in radial-axial ring rolling using a semi-supervised approach and generative adversarial networks for synthetic data generation

As artificial intelligence and especially machine learning gained a lot of attention during the last few years, methods and models have been improving and are becoming easily applicable. This possibility was used to develop a quality prediction system using supervised machine learning methods in form of time series classification models to predict ovality in radial-axial ring rolling. Different preprocessing steps and model implementations have been used to improve quality prediction. A semi-supervised approach is used to improve the prediction and analyze, to what extend it can improve current research in machine learning for quality prediciton. Moreover, first research steps are taken towards a synthetic data generation within the radial-axial ring rolling domain using generative adversarial networks