Laser dimpling process parameters selection and optimization using surrogate-driven process capability space

Özkat, Erkan Caner; Franciosa, Pasquale; Ceglarek, Darek

doi:10.1016/j.optlastec.2017.02.012

Cited by 11 publications

(2 citation statements)

References 41 publications

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“…Light is used in advanced manufacturing processes as the power/tool to accomplish manufacturing tasks. This includes for example, forming (i.e., selective laser sintering) 7 , joining (i.e., laser welding) 8 , machining (i.e., laser cutting) 9 , surface engineering (laser peening or dimpling) 10 and dimensional inspection and control 11,12 .…”

Section: Multi-wave Light Technology (Mwlt)mentioning

confidence: 99%

Multi-wave light technology enabling closed-loop in-process quality control for automotive battery assembly with remote laser welding

Franciosa

Sun

Ceglarek

et al. 2019

Multimodal Sensing: Technologies and Applications

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Section: Multi-wave Light Technology (Mwlt)mentioning

confidence: 99%

Multi-wave light technology enabling closed-loop in-process quality control for automotive battery assembly with remote laser welding

Franciosa

Sun

Ceglarek

et al. 2019

Multimodal Sensing: Technologies and Applications

Self Cite

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“…A product must be manufactured to meet the defined permissible upper and lower limits of each attribute and any deviation from these specified limits is considered a defect (Ozkat et al, 2017a;Liu et al, 2019;Kahveci et al, 2022). Without an effective monitoring and control strategy, today's complex manufacturing systems tend to produce faulty parts more frequently.…”

Section: Introductionmentioning

confidence: 99%

A Method to Classify Steel Plate Faults Based on Ensemble Learning

Özkat

2022

Journal of Materials and Mechatronics: A

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With the industrial revolution 4.0, machine learning methods are widely used in all aspects of manufacturing to perform quality prediction, fault diagnosis or maintenance. In the steel industry, it is important to precisely detect faults/defects in order to produce high quality steel plates. However, determining the exact first-principal model between process parameters and mechanical properties is a challenging process. In addition, steel plate defects are detected through manual, costly and less productive offline inspection in the traditional manufacturing process of steel. Therefore, it is a great necessity to enable automatic detection of steel plate faults. To this end, this study explores the capabilities of the following three machine learning models Adaboost, Bagging and Random Forest in detecting steel plate faults. The well-known steel plate failure dataset provided by Communication Sciences Research Center Semeion was used in this study. The aim of many studies using this dataset is to correctly classify defects in steel plates using traditional machine learning models, ignoring the applicability of the developed models to real-world problems. Manufacturing is a dynamic process with constant adjustments and improvements. For this reason, it is necessary to establish learning process that determine the best model based on the arrival of new information. Contrary to previous studies on the steel plate failure dataset, this article presents a systematic modelling approach that includes the normalization step in the data preparation stage to reduce the effects of outliers, the feature selection step in the dimension reduction stage to develop a machine learning model with less inputs, and hyperparameter optimization step in the model development stage to increase the accuracy of the machine learning model. The performances of the developed machine learning models were compared according to statistical metrics in terms of precision, recall, sensitivity and accuracy. The results revealed that AdaBoost performed well on this dataset, achieving accuracy scores of 93.15% and 91.90% for the training and test datasets, respectively.

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Machine learning models to estimate the elastic modulus of weathered magmatic rocks

et al. 2021

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Laser dimpling process parameters selection and optimization using surrogate-driven process capability space

Cited by 11 publications

References 41 publications

Multi-wave light technology enabling closed-loop in-process quality control for automotive battery assembly with remote laser welding

Multi-wave light technology enabling closed-loop in-process quality control for automotive battery assembly with remote laser welding

A Method to Classify Steel Plate Faults Based on Ensemble Learning

Machine learning models to estimate the elastic modulus of weathered magmatic rocks

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