XGBoost-based on-line prediction of seam tensile strength for Al-Li alloy in laser welding: Experiment study and modelling

Zhang, Zhifen; Huang, Yue; Qin, Rui; Ren, Wenjing; Wen, Guangrui

doi:10.1016/j.jmapro.2020.12.004

Cited by 54 publications

(14 citation statements)

References 32 publications

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“…Extreme Gradient Boosting (XGBoost) is a gradient boosting algorithm enhanced for efficiency, versatility, and scalability [15] , [16] , [17] . In recent years, XGBoost has been widely used by researchers, and it has shown impressive performance in a variety of Machine Learning (ML) challenges [18] , [19] .…”

Section: Xgboostmentioning

confidence: 99%

COV-ADSX: An Automated Detection System using X-ray Images, Deep Learning, and XGBoost for COVID-19

Hasani

Nasiri

2022

Software Impacts

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Following the COVID-19 pandemic, scientists have been looking for different ways to diagnose COVID-19, and these efforts have led to a variety of solutions. One of the common methods of detecting infected people is chest radiography. In this paper, an Automated Detection System using X-ray images (COV-ADSX) is proposed, which employs a deep neural network and XGBoost to detect COVID-19. COV-ADSX was implemented using the Django web framework, which allows the user to upload an X-ray image and view the results of the COVID-19 detection and image’s heatmap, which helps the expert to evaluate the chest area more accurately.

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Section: Xgboostmentioning

confidence: 99%

COV-ADSX: An Automated Detection System using X-ray Images, Deep Learning, and XGBoost for COVID-19

Hasani

Nasiri

2022

Software Impacts

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“…9 The XGBoost algorithm has been applied in various domains of manufacturing, e.g., Machining processes for predicting tool wear of drilling, 10 predicting material removal using a robotic grinding process, 11 and correlating the input parameters of CNC turning via predicting values of surface roughness and material removal rate of the process. 12 It has also been applied to various joining processes, including the prediction of metal active gas (MAG) weld bead geometry, 13 prediction of laser welding seam tensile strength, 14 and prediction of the geometry of multilayer and multi-bead wire and arc additive manufacturing (WAAM). 15 The XGBoost algorithm has also been applied to material characterization and quality assessment, e.g., predicting the fatigue strength of steels, 16 optimizing steel properties by correlating chemical compositions and process parameters with tensile strength and plasticity, 17 predicting aluminum alloy ingot quality in casting, 18 porosity prediction in oilfield exploration and development, 19 and diagnosing wind turbines blade icing.…”

Section: Related Workmentioning

confidence: 99%

Industry 4.0 Foundry Data Management and Supervised Machine Learning in Low-Pressure Die Casting Quality Improvement

et al. 2022

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Low-pressure die cast (LPDC) is widely used in high performance, precision aluminum alloy automobile wheel castings, where defects such as porosity voids are not permitted. The quality of LPDC parts is highly influenced by the casting process conditions. A need exists to optimize the process variables to improve the part quality against difficult defects such as gas and shrinkage porosity. To do this, process variable measurements need to be studied against occurrence rates of defects. In this paper, industry 4.0 cloud-based systems are used to extract data. With these data, supervised machine learning classification models are proposed to identify conditions that predict defectives in a real foundry Aluminum LPDC process. The root cause analysis is difficult, because the rate of defectives in this process occurred in small percentages and against many potential process measurement variables. A model based on the XGBoost classification algorithm was used to map the complex relationship between process conditions and the creation of defective wheel rims. Data were collected from a particular LPDC machine and die mold over three shifts and six continuous days. Porosity defect occurrence rates could be predicted using 36 features from 13 process variables collected from a considerably small sample (1077 wheels) which was highly skewed (62 defectives) with 87% accuracy for good parts and 74% accuracy for parts with porosity defects. This work was helpful in assisting process parameter tuning on new product pre-series production to lower defectives.

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“…This model has the advantages of efficient tree pruning, regularization, and parallel processing. It has been used in many engineering fields to solve the industrial application problems [28][29][30].…”

Section: Xgboostmentioning

confidence: 99%

Vibration prediction and analysis of strip rolling mill based on XGBoost and Bayesian optimization

Yang

Lin

Zhang

et al. 2022

Complex Intell. Syst.

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The stable operation of strip rolling mill is the key factor to ensure the stability of product quality. The design capability of existing domestic imported and self-developed strip rolling mills cannot be fully developed, and the frequent occurrence of mill vibration and operation instability problems seriously restrict the equipment capacity and the production of high-end strip products. The vibration prediction analysis method for hot strip mill based on eXtreme gradient boosting (XGBoost) and Bayesian optimization (BO) is proposed. First, an XGBoost prediction model is developed based on a self-built data set to construct a complex functional relationship between process parameters and rolling mill vibration. Second, the important hyperparameters and parameters of XGBoost are optimized using Bayesian optimization algorithm to improve the prediction accuracy, computational efficiency, and stability of the model. Third, a comprehensive comparison is made between the prediction model in this paper and other well-known machine learning benchmark models. Finally, the prediction results of the model are interpreted using the SHapley Additive exPlanations (SHAP) method. The proposed model outperforms existing models in terms of prediction accuracy, computational speed and stability. At the same time, the degree of influence of each feature on rolling mill vibration is also obtained.

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XGBoost-based on-line prediction of seam tensile strength for Al-Li alloy in laser welding: Experiment study and modelling

Cited by 54 publications

References 32 publications

COV-ADSX: An Automated Detection System using X-ray Images, Deep Learning, and XGBoost for COVID-19

COV-ADSX: An Automated Detection System using X-ray Images, Deep Learning, and XGBoost for COVID-19

Industry 4.0 Foundry Data Management and Supervised Machine Learning in Low-Pressure Die Casting Quality Improvement

Vibration prediction and analysis of strip rolling mill based on XGBoost and Bayesian optimization

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