Cargando…

Explainable Remaining Tool Life Prediction for Individualized Production Using Automated Machine Learning

The increasing demand for customized products is a core driver of novel automation concepts in Industry 4.0. For the case of machining complex free-form workpieces, e.g., in die making and mold making, individualized manufacturing is already the industrial practice. The varying process conditions an...

Descripción completa

Detalles Bibliográficos
Autores principales:	Krupp, Lukas, Wiede, Christian, Friedhoff, Joachim, Grabmaier, Anton
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	MDPI 2023
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10610891/ https://www.ncbi.nlm.nih.gov/pubmed/37896615 http://dx.doi.org/10.3390/s23208523

Ejemplares similares

A Machining State-Based Approach to Tool Remaining Useful Life Adaptive Prediction
por: Li, Yiming, et al.
Publicado: (2020)

Automated machine learning and explainable AI (AutoML-XAI) for metabolomics: improving cancer diagnostics
por: Bifarin, Olatomiwa O., et al.
Publicado: (2023)

A machine learning Automated Recommendation Tool for synthetic biology
por: Radivojević, Tijana, et al.
Publicado: (2020)

Multiform Informed Machine Learning Based on Piecewise and Weibull for Engine Remaining Useful Life Prediction
por: Zhou, Shuang, et al.
Publicado: (2023)

Explainable Preoperative Automated Machine Learning Prediction Model for Cardiac Surgery-Associated Acute Kidney Injury
por: Thongprayoon, Charat, et al.
Publicado: (2022)

Aliro: an automated machine learning tool leveraging large language models
por: Choi, Hyunjun, et al.
Publicado: (2023)

Automated Machine Learning
por: Masood, Adnan, et al.
Publicado: (2021)

Principles and Practice of Explainable Machine Learning
por: Belle, Vaishak, et al.
Publicado: (2021)

Explainable machine learning for diffraction patterns
por: Nawaz, Shah, et al.
Publicado: (2023)

Explaining Accurate Predictions of Multitarget Compounds with Machine Learning Models Derived for Individual Targets
por: Lamens, Alec, et al.
Publicado: (2023)

Multi-Sensor Data Fusion for Remaining Useful Life Prediction of Machining Tools by IABC-BPNN in Dry Milling Operations
por: Liu, Min, et al.
Publicado: (2020)

A Roadmap for Automating Lineage Tracing to Aid Automatically Explaining Machine Learning Predictions for Clinical Decision Support
por: Luo, Gang
Publicado: (2021)

Remaining Useful Life Prognosis for Turbofan Engine Using Explainable Deep Neural Networks with Dimensionality Reduction
por: Hong, Chang Woo, et al.
Publicado: (2020)

Prediction of caregiver quality of life in amyotrophic lateral sclerosis using explainable machine learning
por: Antoniadi, Anna Markella, et al.
Publicado: (2021)

Explaining machine-learned particle-flow reconstruction
por: Mokhtar, Farouk, et al.
Publicado: (2021)

Endoglin Targeting: Lessons Learned and Questions That Remain
por: Liu, Yingmiao, et al.
Publicado: (2020)

Explainable Machine Learning for Breakdown Prediction in High Gradient RF Cavities
por: Obermair, Christoph, et al.
Publicado: (2022)

Unsupervised Machine Learning Applied to Seismic Interpretation: Towards an Unsupervised Automated Interpretation Tool
por: Celecia, Alimed, et al.
Publicado: (2021)

Machine learning powered tools for automated analysis of muscle sympathetic nerve activity recordings
por: Nolde, Janis M., et al.
Publicado: (2021)

Clinical assessment of a novel machine‐learning automated contouring tool for radiotherapy planning
por: Hu, Yunfei, et al.
Publicado: (2023)

Remaining Useful-Life Prediction of the Milling Cutting Tool Using Time–Frequency-Based Features and Deep Learning Models
por: Sayyad, Sameer, et al.
Publicado: (2023)

A Machine Learning Framework for Automated Accident Detection Based on Multimodal Sensors in Cars
por: Hozhabr Pour, Hawzhin, et al.
Publicado: (2022)

Explainable and interpretable models in computer vision and machine learning
por: Escalante, Hugo Jair, et al.
Publicado: (2019)

Editorial: Interpretable and explainable machine learning models in oncology
por: Hrinivich, William Thomas, et al.
Publicado: (2023)

PredicT-ML: a tool for automating machine learning model building with big clinical data
por: Luo, Gang
Publicado: (2016)

Novel application of an automated-machine learning development tool for predicting burn sepsis: proof of concept
por: Tran, Nam K., et al.
Publicado: (2020)

Research Screener: a machine learning tool to semi-automate abstract screening for systematic reviews
por: Chai, Kevin E. K., et al.
Publicado: (2021)

MantaID: a machine learning–based tool to automate the identification of biological database IDs
por: Zeng, Zhengpeng, et al.
Publicado: (2023)

Common orthopaedic trauma may explain 31,000-year-old remains
por: Murphy, Nicholas J., et al.
Publicado: (2023)

A machine-learning based model for automated recommendation of individualized treatment of rifampicin-resistant tuberculosis
por: Verboven, Lennert, et al.
Publicado: (2023)

Diagnosing Breast Cancer with Microwave Technology: Remaining Challenges and Potential Solutions with Machine Learning
por: Oliveira, Bárbara L., et al.
Publicado: (2018)

HOPS: automated detection and authentication of pathogen DNA in archaeological remains
por: Hübler, Ron, et al.
Publicado: (2019)

Machine learning approach towards explaining water quality dynamics in an urbanised river
por: Schäfer, Benjamin, et al.
Publicado: (2022)

An Explainable and Robust Deep Learning Approach for Automated Electroencephalography-based Schizophrenia Diagnosis
por: Sattiraju, Abhinav, et al.
Publicado: (2023)

Explainable machine-learning predictions for the prevention of hypoxaemia during surgery
por: Lundberg, Scott M., et al.
Publicado: (2018)

Explainable AI: A Review of Machine Learning Interpretability Methods
por: Linardatos, Pantelis, et al.
Publicado: (2020)

Explainable Machine Learning Model for Glaucoma Diagnosis and Its Interpretation
por: Oh, Sejong, et al.
Publicado: (2021)

Explainable machine learning for precise fatigue crack tip detection
por: Melching, David, et al.
Publicado: (2022)

An Explainable Machine Learning Pipeline for Stroke Prediction on Imbalanced Data
por: Kokkotis, Christos, et al.
Publicado: (2022)

Explainable machine learning predictions to support personalized cardiology strategies
por: Loh, De Rong, et al.
Publicado: (2021)

Cannot write session to /tmp/vufind_sessions/sess_quuhcdvlolmoe0l7j1crlj2fqk