Comparison of machine learning algorithms for the automatic programming of computer numerical control machine

Sharma, Neelima; Chawla, Vivek; Ram, N. Sankar

doi:10.5267/j.ijdns.2019.9.003

Cited by 13 publications

(6 citation statements)

References 21 publications

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“…Collect data from the risk factors of dynamic disorders reported in the study, process the pre collected data, and collect standardized datasets, which are proportionally divided into training and testing groups (Bird and Dale 2022). The training team conducted research using automatic learning algorithms to optimize the reuse pattern to obtain the best model, which can also be incorporated into damage prediction and recognition (Sharma et al 2020). The literature indicates that the biomechanical analysis methods of human joints mainly involve pressure sensitive film method and staining method, but the distribution of KOA patients needs to be analyzed through surface measurement, which is very difficult to obtain joints (Mishra et al 2022).…”

Section: Related Workmentioning

confidence: 99%

Application of Spectral Imaging Technology Based on Computer Simulation in the Prevention of Sports Injuries in Aerobics

Li,

Wei,

Xiang

2024

Preprint

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Calisthenics is a widely popular and high-intensity exercise, but the frequency of sports injuries has increased accordingly. Therefore, this paper explores the application potential of spectral imaging technology based on sports biomechanics in the early warning system of aerobics sports injury to provide opportunities for early injury recognition and intervention. The biomechanical data and spectral imaging data of aerobics athletes during their training were collected through experimental research. Changes in specific joint angles and muscle strength of athletes during exercise were observed to exhibit features associated with injury. Spectral imaging techniques can provide accurate measurements of biological signals such as skin surface temperature and blood flow, further revealing the underlying mechanisms of sports injuries. Therefore, the spectral imaging technology based on sports biomechanics has the potential to be applied to the early warning system of sports injury in aerobics. It enables early identification of potential injury risks and interventions to protect athlete health and safety by monitoring joint Angle changes and muscle strength in athletes, combined with quantitative analysis of spectral imaging data.

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Section: Related Workmentioning

confidence: 99%

Application of Spectral Imaging Technology Based on Computer Simulation in the Prevention of Sports Injuries in Aerobics

Li,

Wei,

Xiang

2024

Preprint

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“…Using existing data to predict the performance parameters can not only expand the space of material data but also provide guidance for material experiments and applications. Different machine learning algorithms have different sensitivities to material data in different ranges of data sets, so it is necessary to make a feature selection on specific material data samples to evaluate algorithm by performance evaluation [30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Predicting Perovskite Performance with Multiple Machine-Learning Algorithms

Qin

Tian

et al. 2021

Crystals

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Perovskites have attracted increasing attention because of their excellent physical and chemical properties in various fields, exhibiting a universal formula of ABO3 with matching compatible sizes of A-site and B-site cations. In this work, four different prediction models of machine learning algorithms, including support vector regression based on radial basis kernel function (SVM-RBF), ridge regression (RR), random forest (RF), and back propagation neural network (BPNN), are established to predict the formation energy, thermodynamic stability, crystal volume, and oxygen vacancy formation energy of perovskite materials. Combined with the fitting diagrams of the predicted values and DFT calculated values, the results show that SVM-RBF has a smaller bias in predicting the crystal volume. RR has a smaller bias in predicting the thermodynamic stability. RF has a smaller bias in predicting the formation energy, crystal volume, and thermodynamic stability. BPNN has a smaller bias in predicting the formation energy, thermodynamic stability, crystal volume, and oxygen vacancy formation energy. Obviously, different machine learning algorithms exhibit different sensitivity to data sample distribution, indicating that we should select different algorithms to predict different performance parameters of perovskite materials.

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“…She found that axial misalignment of openings machined with high-speed steel drills depended on/was caused by tool advance and its rotational frequency. Sharma, Chawla, and Ram describe machine learning algorithms, namely support vector machine (SVM), restricted Boltzmann machine (RBM), and deep belief network (DBN), for the automatic programming of a computer numerical control machine [ 11 ]. Yusup et al estimated optimal abrasive waterjet machining control parameters using artificial bee colony [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

The Use of Neural Networks and Genetic Algorithms to Control Low Rigidity Shafts Machining

Świć

Wołos

Gola

et al. 2020

Sensors

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The article presents an original machine-learning-based automated approach for controlling the process of machining of low-rigidity shafts using artificial intelligence methods. Three models of hybrid controllers based on different types of neural networks and genetic algorithms were developed. In this study, an objective function optimized by a genetic algorithm was replaced with a neural network trained on real-life data. The task of the genetic algorithm is to select the optimal values of the input parameters of a neural network to ensure minimum deviation. Both input vector values and the neural network’s output values are real numbers, which means the problem under consideration is regressive. The performance of three types of neural networks was analyzed: a classic multilayer perceptron network, a nonlinear autoregressive network with exogenous input (NARX) prediction network, and a deep recurrent long short-term memory (LSTM) network. Algorithmic machine learning methods were used to achieve a high level of automation of the control process. By training the network on data from real measurements, we were able to control the reliability of the turning process, taking into account many factors that are usually overlooked during mathematical modelling. Positive results of the experiments confirm the effectiveness of the proposed method for controlling low-rigidity shaft turning.

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Comparison of machine learning algorithms for the automatic programming of computer numerical control machine

Cited by 13 publications

References 21 publications

Application of Spectral Imaging Technology Based on Computer Simulation in the Prevention of Sports Injuries in Aerobics

Application of Spectral Imaging Technology Based on Computer Simulation in the Prevention of Sports Injuries in Aerobics

Predicting Perovskite Performance with Multiple Machine-Learning Algorithms

The Use of Neural Networks and Genetic Algorithms to Control Low Rigidity Shafts Machining

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