Simple machine learning allied with data-driven methods for monitoring tool wear in machining processes

2020 ◽  
Vol 109 (9-12) ◽  
pp. 2491-2501
Author(s):  
Adalto de Farias ◽  
Sérgio Luiz Rabelo de Almeida ◽  
Sergio Delijaicov ◽  
Vanessa Seriacopi ◽  
Ed Claudio Bordinassi
Keyword(s):  
Machine Learning ◽  
Tool Wear ◽  
Data Driven ◽  
Monitoring Tool ◽  
Machining Processes ◽  
Simple Machine

Intelligente Überwachung von Werkzeugverschleiß/Intelligent monitoring of tool wear based on machine internal data

2021 ◽  
Vol 111 (05) ◽  
pp. 309-313
Author(s):  
Christian Brecher ◽  
Tiandong Xi ◽  
Igor Medeiros Benincá ◽  
Sebastian Kehne ◽  
Marcel Fey
Keyword(s):  
Machine Learning ◽  
Tool Wear ◽  
Machine Tools ◽  
Sensor Data ◽  
Machine Learning Method ◽  
Process Variables ◽  
Monitoring Tool ◽  
Intelligent Monitoring ◽  
Current Axis ◽  
Axis Position

Numerische Steuerungen für Werkzeugmaschinen erfassen eine erhebliche Menge an Sensordaten für die Achsregelung. Diese liefern nicht nur Informationen über die aktuellen Achspositionen oder die Ströme, sondern können mithilfe von Modellen auch für das Monitoring von anderen Prozessgrößen verwendet werden. In diesem Beitrag wird ein Machine-Learning-Verfahren zur Überwachung von Werkzeugverschleiß untersucht, welches allein auf maschinen-internen Daten basiert.   Numerical controls for machine tools acquire a considerable amount of sensor data for axis control. This information, such as the current axis position or the motor currents, can be used for monitoring other process variables with the aid of models. This article investigates a machine learning method for monitoring tool wear in machine tools, based on machine-internal data only.

scholarly journals A Multi-Index Generative Adversarial Network for Tool Wear Detection with Imbalanced Data

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Guokai Zhang ◽  
Haoping Xiao ◽  
Jingwen Jiang ◽  
Qinyuan Liu ◽  
Yimo Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Tool Wear ◽  
Imbalanced Data ◽  
Temporal Correlation ◽  
Monitoring Tool ◽  
Generative Adversarial Network ◽  
Multi Index ◽  
Machine Learning Methods ◽  
Adversarial Network

The scarcity of abnormal data leads to imbalanced data in the field of monitoring tool wear conditions. In this paper, a novel multi-index generative adversarial network (MI-GAN) is proposed to detect the tool wear conditions subject to imbalanced signal data. First, the generator in the MI-GAN is trained to produce fake normal signals, and the discriminator computes scores of testing signals and generated signals. Next, the generator detects abnormal signals based on the performance of imitating testing signals, and the discriminator will compute the scores of testing signals and generated signals. Subsequently, two indexes, i.e., L 2 -norm and temporal correlation coefficient (CORT), are put forward to measure the similarity between generated signals and testing signals. Finally, our decision-making function further combines L 2 -norm and CORT with two discriminator scores to determine the tool conditions. Experimental results show that our method obtains 97% accuracy in tool wear detection based on imbalanced data without manual feature extraction, which outperforms traditional machine learning methods.

scholarly journals A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests

2017 ◽  
Vol 139 (7) ◽  
Author(s):  
Dazhong Wu ◽  
Connor Jennings ◽  
Janis Terpenny ◽  
Robert X. Gao ◽  
Soundar Kumara
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Tool Wear ◽  
Random Forests ◽  
Manufacturing Systems ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Tool Wear Prediction

Manufacturers have faced an increasing need for the development of predictive models that predict mechanical failures and the remaining useful life (RUL) of manufacturing systems or components. Classical model-based or physics-based prognostics often require an in-depth physical understanding of the system of interest to develop closed-form mathematical models. However, prior knowledge of system behavior is not always available, especially for complex manufacturing systems and processes. To complement model-based prognostics, data-driven methods have been increasingly applied to machinery prognostics and maintenance management, transforming legacy manufacturing systems into smart manufacturing systems with artificial intelligence. While previous research has demonstrated the effectiveness of data-driven methods, most of these prognostic methods are based on classical machine learning techniques, such as artificial neural networks (ANNs) and support vector regression (SVR). With the rapid advancement in artificial intelligence, various machine learning algorithms have been developed and widely applied in many engineering fields. The objective of this research is to introduce a random forests (RFs)-based prognostic method for tool wear prediction as well as compare the performance of RFs with feed-forward back propagation (FFBP) ANNs and SVR. Specifically, the performance of FFBP ANNs, SVR, and RFs are compared using an experimental data collected from 315 milling tests. Experimental results have shown that RFs can generate more accurate predictions than FFBP ANNs with a single hidden layer and SVR.

Cloud-Based Parallel Machine Learning for Tool Wear Prediction

2018 ◽  
Vol 140 (4) ◽  
Author(s):  
Dazhong Wu ◽  
Connor Jennings ◽  
Janis Terpenny ◽  
Soundar Kumara ◽  
Robert X. Gao
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Tool Wear ◽  
Random Forests ◽  
Predictive Models ◽  
Parallel Machine ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Processing Scheme ◽  
Cloud Computing System

The emergence of cloud computing, industrial internet of things (IIoT), and new machine learning techniques have shown the potential to advance prognostics and health management (PHM) in smart manufacturing. While model-based PHM techniques provide insight into the progression of faults in mechanical components, certain assumptions on the underlying physical mechanisms for fault development are required to develop predictive models. In situations where there is a lack of adequate prior knowledge of the underlying physics, data-driven PHM techniques have been increasingly applied in the field of smart manufacturing. One of the limitations of current data-driven methods is that large volumes of training data are required to make accurate predictions. Consequently, computational efficiency remains a primary challenge, especially when large volumes of sensor-generated data need to be processed in real-time applications. The objective of this research is to introduce a cloud-based parallel machine learning algorithm that is capable of training large-scale predictive models more efficiently. The random forests (RFs) algorithm is parallelized using the MapReduce data processing scheme. The MapReduce-based parallel random forests (PRFs) algorithm is implemented on a scalable cloud computing system with varying combinations of processors and memories. The effectiveness of this new method is demonstrated using condition monitoring data collected from milling experiments. By implementing RFs in parallel on the cloud, a significant increase in the processing speed (14.7 times in terms of increase in training time) has been achieved, with a high prediction accuracy of tool wear (eight times in terms of reduction in mean squared error (MSE)).

scholarly journals Automated Data-Driven Generation of Personalized Pedagogical Interventions in Intelligent Tutoring Systems

2021 ◽  
Author(s):  
Ekaterina Kochmar ◽  
Dung Do Vu ◽  
Robert Belfer ◽  
Varun Gupta ◽  
Iulian Vlad Serban ◽  
...  
Keyword(s):  
Machine Learning ◽  
Student Performance ◽  
Language Processing ◽  
Intelligent Tutoring Systems ◽  
Large Scale ◽  
Intelligent Tutoring ◽  
Performance Outcomes ◽  
Data Driven ◽  
Personalized Feedback ◽  
Tutoring Systems

AbstractIntelligent tutoring systems (ITS) have been shown to be highly effective at promoting learning as compared to other computer-based instructional approaches. However, many ITS rely heavily on expert design and hand-crafted rules. This makes them difficult to build and transfer across domains and limits their potential efficacy. In this paper, we investigate how feedback in a large-scale ITS can be automatically generated in a data-driven way, and more specifically how personalization of feedback can lead to improvements in student performance outcomes. First, in this paper we propose a machine learning approach to generate personalized feedback in an automated way, which takes individual needs of students into account, while alleviating the need of expert intervention and design of hand-crafted rules. We leverage state-of-the-art machine learning and natural language processing techniques to provide students with personalized feedback using hints and Wikipedia-based explanations. Second, we demonstrate that personalized feedback leads to improved success rates at solving exercises in practice: our personalized feedback model is used in , a large-scale dialogue-based ITS with around 20,000 students launched in 2019. We present the results of experiments with students and show that the automated, data-driven, personalized feedback leads to a significant overall improvement of 22.95% in student performance outcomes and substantial improvements in the subjective evaluation of the feedback.

scholarly journals A Data-Driven Method for the Temporal Estimation of Soil Water Potential and Its Application for Shallow Landslides Prediction

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1208
Author(s):  
Massimiliano Bordoni ◽  
Fabrizio Inzaghi ◽  
Valerio Vivaldi ◽  
Roberto Valentino ◽  
Marco Bittelli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Potential ◽  
Soil Water ◽  
Temporal Trends ◽  
Soil Water Potential ◽  
Shallow Landslides ◽  
Water Dynamics ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Physically Based

Soil water potential is a key factor to study water dynamics in soil and for estimating the occurrence of natural hazards, as landslides. This parameter can be measured in field or estimated through physically-based models, limited by the availability of effective input soil properties and preliminary calibrations. Data-driven models, based on machine learning techniques, could overcome these gaps. The aim of this paper is then to develop an innovative machine learning methodology to assess soil water potential trends and to implement them in models to predict shallow landslides. Monitoring data since 2012 from test-sites slopes in Oltrepò Pavese (northern Italy) were used to build the models. Within the tested techniques, Random Forest models allowed an outstanding reconstruction of measured soil water potential temporal trends. Each model is sensitive to meteorological and hydrological characteristics according to soil depths and features. Reliability of the proposed models was confirmed by correct estimation of days when shallow landslides were triggered in the study areas in December 2020, after implementing the modeled trends on a slope stability model, and by the correct choice of physically-based rainfall thresholds. These results confirm the potential application of the developed methodology to estimate hydrological scenarios that could be used for decision-making purposes.

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