Prediction of geometry deviations in additive manufactured parts: comparison of linear regression with machine learning algorithms

Baturynska, Ivanna; Martinsen, Kristian

doi:10.1007/s10845-020-01567-0

Cited by 68 publications

(24 citation statements)

References 25 publications

(33 reference statements)

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“…Paper [21] is devoted to the problems of size control of medical specimens manufactured by 3D printing. The authors used several machine learning methods to control the specimen's width, thickness, and length in the process of additive production.…”

Section: Related Workmentioning

confidence: 99%

PNN-SVM Approach of Ti-Based Powder’s Properties Evaluation for Biomedical Implants Production

Izonin¹,

Tkachenko²,

Greguš³

et al. 2022

Computers, Materials &Amp; Continua

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The advent of additive technologies has provided a significant breakthrough in the production of medical implants. It has reduced costs, increased productivity and accuracy of the implant manufacturing process. However, there are problems associated with assessing defects in the microstructure, mechanical and technological properties of alloys, both during their production by powder metallurgy and in the process of 3D printing. Thus traditional research methods of alloys properties demand considerable human, material, and time resources. At the same time, artificial intelligence tools create opportunities for intelligent evaluation of the conformity for the microstructure, phase composition, and properties of titanium powder's alloys. It provides new possibilities for the efficient production of biocompatible implants for various functional purposes. However, the accuracy of the methods and models used should be as high as possible. In this paper we designed a hybrid PNN-SVM (Probabilistic Neural Network-Support Vector Machine) high-precision approach for the intelligent evaluation of alloy properties for additive manufacturing of biomedical implants. We have proposed a new approach for extending the dimensionality of input data space by the outputs of the summation layer of the modified PNN topology. Subsequent classification based on the expanded dataset is performed using SVM. We conducted experimental modeling of the proposed approach using a data set on the properties of titanium alloys Ti-6Al-4V and Ti-Al-V-Zr. We have demonstrated a significant increase in the accuracy of the PNN-SVM scheme compared to the single classifiers that form it and other machine learning methods.

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Section: Related Workmentioning

confidence: 99%

PNN-SVM Approach of Ti-Based Powder’s Properties Evaluation for Biomedical Implants Production

Izonin¹,

Tkachenko²,

Greguš³

et al. 2022

Computers, Materials &Amp; Continua

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“…Next to the presented regression models and ANN, Support Vector machines are getting more and more attractive for data driven analysis and their application to manufacturing processes. In particular, for monitoring machinery (health) condition (Goyal et al 2020;Liu et al 2017) or for quality classification of machined, additive manufactured or welded parts (Baturynska & Martinsen, 2021;Çaydaş & Ekici, 2012;He & Li, 2016). In this regard, the support vector machine (SVM) is not only one of the most powerful and robust classification and regression algorithms, but has also significantly improved the handling with multi classification problems and unbalanced data sets (Cervantes et al 2020).…”

Section: Data-driven Monitoring Of Blanking Processesmentioning

confidence: 99%

Smart sheet metal forming: importance of data acquisition, preprocessing and transformation on the performance of a multiclass support vector machine for predicting wear states during blanking

2021

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In consequence of high cost pressure and the progressive globalization of markets, blanking, which represents the most economical process in the value chain of manufacturing companies, is particularly dependent on reducing machine downtimes and increasing the degree of utilization. For this purpose, it is necessary to be able to make a real-time prediction about the current and future process conditions even at high production rates. Therefore, this study investigates the influence of data acquisition, preprocessing and transformation on the performance of a multiclass support vector machine to classify abrasive wear states during blanking based on force signals. The performance of the model was quantitatively evaluated based on the model accuracy and the separability of the classes. As a result, it was shown, that the deviation of time series represents the key parameter for the resulting performance of the classification model and strongly depends on the sensor type and position, the preprocessing procedure as well as the feature extraction and selection. Furthermore, it is shown that the consideration of domain knowledge in the phases of data acquisition, preprocessing and transformation improves the performance of the classification model and is essential to successfully implement AI projects. Summarizing the findings of this study, trustworthy data sets play a crucial role for implementing an automated process monitoring as a basis for resilient manufacturing systems.

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“…Before initiating the training process, the data were scaled using zero mean and unit variance. Five-fold Cross-validation (CV) was employed to avoid overfitting during the training process [23], [37]. To assess the prediction accuracy, Barrionuevo et al [33] introduced an index of merit (IM), which combines multiple metrics to get a unique metric of the algorithms' accuracy.…”

Section: Accuracy Evaluationmentioning

confidence: 99%

Predicting the ultimate tensile strength of AISI 1045 steel and 2017-T4 aluminum alloy joints in a laser-assisted rotary friction welding process using machine learning: a comparison with response surface methodology

Barrionuevo

Mullo

Ramos‐Grez

2021

Int J Adv Manuf Technol

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Welding metal alloys with dissimilar melting points make conventional welding processes not feasible to be used. Friction welding, on the other hand, has proven to be a promising technology. However, obtaining the welded joint's mechanical properties with characteristics similar to the base materials remains a challenge. In the development of this work, several of the machine learning (ML) regressors (e.g., Gaussian process, decision tree, random forest, gradient boosting, and multi-layer perceptron) were evaluated for the prediction of the ultimate tensile strength (UTS) in joints of AISI 1045 steel and 2017-T4 aluminum alloy produced by rotary friction welding with laser assistance. A mixed design of experiments was employed to assess the effect of the rotation speed, friction pressure, and laser power over the UTS. Furthermore, the response surface methodology (RSM) was employed to determine an empirical equation for predicting the UTS, and contours maps determine the main interactions. A total of 48 specimens were employed to train the regressors; the 5-fold cross-validation methodology was used to find the algorithm with greater precision. The gradient boosting regressor (GBR) and Gaussian processes regressors present the highest precision with a less than 3% percentage error for the laser-assisted rotary friction welding process. The capability of the GBR exceeds the accuracy of the RSM with a coefficient of determination (R 2 ) of 90.90 versus 83.24 %, respectively.

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Prediction of geometry deviations in additive manufactured parts: comparison of linear regression with machine learning algorithms

Cited by 68 publications

References 25 publications

PNN-SVM Approach of Ti-Based Powder’s Properties Evaluation for Biomedical Implants Production

PNN-SVM Approach of Ti-Based Powder’s Properties Evaluation for Biomedical Implants Production

Smart sheet metal forming: importance of data acquisition, preprocessing and transformation on the performance of a multiclass support vector machine for predicting wear states during blanking

Predicting the ultimate tensile strength of AISI 1045 steel and 2017-T4 aluminum alloy joints in a laser-assisted rotary friction welding process using machine learning: a comparison with response surface methodology

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