Machine learning with domain knowledge for predictive quality monitoring in resistance spot welding

Machine learning with domain knowledge for predictive quality monitoring in resistance spot welding

(2022) Baifan Zhou et al.   JOURNAL OF INTELLIGENT MANUFACTURING

SemML: Facilitating development of ML models for condition monitoring with semantics

(2021) Baifan Zhou et al.   Journal of Web Semantics

Re-epithelialization and immune cell behaviour in an ex vivo human skin model

(2020) Ana Rakita et al.   Scientific Reports

Applications of ultrasonic testing and machine learning methods to predict the static & fatigue behavior of spot-welded joints

(2020) N. Amiri et al.   Journal of Manufacturing Processes

Burn-through prediction and weld depth estimation by deep learning model monitoring the molten pool in gas metal arc welding with gap fluctuation

(2020) Kazufumi Nomura et al.   Journal of Manufacturing Processes

Deep learning assisted vision inspection of resistance spot welds

(2020) Wei Dai et al.   Journal of Manufacturing Processes

An ontology-mediated analytics-aware approach to support monitoring and diagnostics of static and streaming data

(2019) Evgeny Kharlamov et al.   Journal of Web Semantics

Improving Root Cause Analysis by Detecting and Removing Transient Changes in Oscillatory Time Series with Application to a 1,3-Butadiene Process

(2019) Baifan Zhou et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

Welding defects detection based on deep learning with multiple optical sensors during disk laser welding of thick plates

(2019) Yanxi Zhang et al.   JOURNAL OF MANUFACTURING SYSTEMS

A Survey of Feature Set Reduction Approaches for Predictive Analytics Models in the Connected Manufacturing Enterprise

(2019) Phillip LaCasse et al.   Applied Sciences-Basel

Performance analysis and comparison of machine learning algorithms for predicting nugget width of resistance spot welding joints

(2019) Saeed Zamanzad Gavidel et al.   INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY

Weld image deep learning-based on-line defects detection using convolutional neural networks for Al alloy in robotic arc welding

(2019) Zhifen Zhang et al.   Journal of Manufacturing Processes

High accuracy beam splitting using spatial light modulator combined with machine learning algorithms

(2019) Dmitriy Mikhaylov et al.   OPTICS AND LASERS IN ENGINEERING

Semantic weldability prediction with RSW quality dataset and knowledge construction

(2018) Kyoung-Yun Kim et al.   ADVANCED ENGINEERING INFORMATICS

Deep learning and its applications to machine health monitoring

(2018) Rui Zhao et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Ontology Based Data Access in Statoil

(2017) Evgeny Kharlamov et al.   Journal of Web Semantics

Semantic access to streaming and static data at Siemens

(2017) Evgeny Kharlamov et al.   Journal of Web Semantics

Automatic welding quality classification for the spot welding based on the Hopfield associative memory neural network and Chernoff face description of the electrode displacement signal features

(2017) Hongjie Zhang et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

RODI: Benchmarking relational-to-ontology mapping generation quality

(2017) Christoph Pinkel et al.   Semantic Web

Using Semantic Technology to Tame the Data Variety Challenge

(2016) Ian Horrocks et al.   IEEE INTERNET COMPUTING

Quality monitoring based on dynamic resistance and principal component analysis in small scale resistance spot welding process

(2016) Xiaodong Wan et al.   INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY

Resistance spot welding quality identification with particle swarm optimization and a kernel extreme learning machine model

(2016) Hongchun Sun et al.   INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY

Direct quality prediction in resistance spot welding process: Sensitivity, specificity and predictive accuracy comparative analysis

(2015) M. Pereda et al.   SCIENCE AND TECHNOLOGY OF WELDING AND JOINING

A new method for nondestructive quality evaluation of the resistance spot welding based on the radar chart method and the decision tree classifier

(2014) Hongjie Zhang et al.   INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY

Statistical modeling and optimization of resistance spot welding process parameters using neural networks and multi-objective genetic algorithm

(2014) Hamed Pashazadeh et al.   JOURNAL OF INTELLIGENT MANUFACTURING

Representation Learning: A Review and New Perspectives

(2013) Y. Bengio et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

Artificial neural networks for pitting potential prediction of resistance spot welding joints of AISI 304 austenitic stainless steel

(2010) Óscar Martín et al.   CORROSION SCIENCE

Quality prediction of resistance spot welding joints of 304 austenitic stainless steel

(2008) Óscar Martín et al.   MATERIALS & DESIGN