scholarly journals Comparative Analysis of Machine Learning Methods for Predicting Robotized Incremental Metal Sheet Forming Force

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 18
Author(s):  
Vytautas Ostasevicius ◽  
Ieva Paleviciute ◽  
Agne Paulauskaite-Taraseviciene ◽  
Vytautas Jurenas ◽  
Darius Eidukynas ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Prediction Models ◽  
Single Point ◽  
Sheet Thickness ◽  
Gaussian Process Regression ◽  
Balance Model ◽  
Forming Force ◽  
Friction Forces ◽  
Force Prediction

This paper proposes a method for extracting information from the parameters of a single point incremental forming (SPIF) process. The measurement of the forming force using this technology helps to avoid failures, identify optimal processes, and to implement routine control. Since forming forces are also dependent on the friction between the tool and the sheet metal, an innovative solution has been proposed to actively control the friction forces by modulating the vibrations that replace the environmentally unfriendly lubrication of contact surfaces. This study focuses on the influence of mechanical properties, process parameters and sheet thickness on the maximum forming force. Artificial Neural Network (ANN) and different machine learning (ML) algorithms have been applied to develop an efficient force prediction model. The predicted forces agreed reasonably well with the experimental results. Assuming that the variability of each input function is characterized by a normal distribution, sampling data were generated. The applicability of the models in an industrial environment is due to their relatively high performance and the ability to balance model bias and variance. The results indicate that ANN and Gaussian process regression (GPR) have been identified as the most efficient methods for developing forming force prediction models.

STUDY ON FORMING FORCES OF PARTS PROCESSED BY SINGLE POINT INCREMENTAL FORMING WITH DUMMY SHEET

2019 ◽  
Vol 14 (3) ◽  
Author(s):  
Vikas Sisodia ◽  
Shailendra Kumar
Keyword(s):  
Process Parameters ◽  
Feed Rate ◽  
Incremental Forming ◽  
Single Point ◽  
Sheet Thickness ◽  
Single Point Incremental Forming ◽  
Forming Force ◽  
Step Size ◽  
Wall Angle ◽  
Influence Of Process Parameters

The present paper describes the experimental investigation on influence of process parameters on maximum forming force in Single Point Incremental Forming (SPIF) process using dummy sheet. Process parameters namely dummy sheet thickness, tool size, step size, wall angle and feed rate are selected. Taguchi L18 orthogonal array is used to design the experiments. From the analysis of variance (ANOVA) dummy sheet thickness, tool size, step size and wall angle are significant process parameters while feed rate is insignificant. It is found that as dummy sheet thickness, tool size, step size and wall angle increase magnitude of peak forming force increases while there is marginal decrease in forming force as feed rate increases. Predictive model is also developed for forming force. Validation tests are performed in order to check the accuracy of developed model. Optimum set of process parameters is also determined to minimize forming force. Experimental results are in good agreement with results predicted by the developed mathematical model.

scholarly journals Comparative Analysis of Single and Hybrid Neuro-Fuzzy-Based Models for an Industrial Heating Ventilation and Air Conditioning Control System

2020 ◽  
Author(s):  
Sina Ardabili ◽  
Bertalan Beszedes ◽  
Laszlo Nadai ◽  
Karoly Szell ◽  
Amir Mosavi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Fuzzy Inference System ◽  
Air Conditioning ◽  
High Performance ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques ◽  
Save Energy

The hybridization of machine learning methods with soft computing techniques is an essential approach to improve the performance of the prediction models. Hybrid machine learning models, particularly, have gained popularity in the advancement of the high-performance control systems. Higher accuracy and better performance for prediction models of exergy destruction and energy consumption used in the control circuit of heating, ventilation, and air conditioning (HVAC) systems can be highly economical in the industrial scale to save energy. This research proposes two hybrid models of adaptive neuro-fuzzy inference system-particle swarm optimization (ANFIS-PSO), and adaptive neuro-fuzzy inference system-genetic algorithm (ANFIS-GA) for HVAC. The results are further compared with the single ANFIS model. The ANFIS-PSO model with the RMSE of 0.0065, MAE of 0.0028, and R2 equal to 0.9999, with a minimum deviation of 0.0691 (KJ/s), outperforms the ANFIS-GA and single ANFIS models.

scholarly journals A Machine Learning Study on the Thermostability Prediction of (R)-ω-Selective Amine Transaminase from Aspergillus terreus

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Li-li Jia ◽  
Ting-ting Sun ◽  
Yan Wang ◽  
Yu Shen
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Amino Acid ◽  
Protein Engineering ◽  
Amino Acid Sequence ◽  
High Performance ◽  
Aspergillus Terreus ◽  
Single Point ◽  
Activity Relationship ◽  
Least Squares Regression

Artificial intelligence technologies such as machine learning have been applied to protein engineering, with unique advantages in protein structure, function prediction, catalytic activity, and other issues in recent years. Screening better mutants is still a bottleneck in protein engineering. In this paper, a new sequence-activity relationship method was analyzed for its application in improving the thermal stability of Aspergillus terreus (R)-ω-selective amine transaminase. The experimental data from 6 single-point mutated enzymes were used as a learning dataset to build models and predict the thermostability of 26 mutants. Based on digital signal processing (DSP), this method digitized the amino acid sequence of proteins by fast Fourier transform (FFT) and then established the best model applying partial least squares regression (PLSR) to screen out all possible mutants, especially those with high performance. In protein engineering, the innovative sequence activity relationship (ISAR) method can make a reasonable prediction using limited experimental data and significantly reduce the experimental cost. The half-life ( T 1 / 2 ) of (R)-ω-transaminase was fitted with the amino acid sequence by the ISAR algorithm, resulting in an R 2 of 0.8929 and a cvRMSE of 4.89. At the same time, the mutants with higher T 1 / 2 than the existing ones were predicted, laying the groundwork for better (R)-ω-transaminase in the later stage. The ISAR algorithm is expected to provide a new technique for protein evolution and screening.

Image Data-Based Surface Texture Characterization and Prediction Using Machine Learning Approaches for Additive Manufacturing

2020 ◽  
Vol 20 (2) ◽  
Author(s):  
V. Akhil ◽  
G. Raghav ◽  
N. Arunachalam ◽  
D. S. Srinivas
Keyword(s):  
Machine Learning ◽  
Additive Manufacturing ◽  
Surface Texture ◽  
Prediction Models ◽  
Low Cost ◽  
Gaussian Process Regression ◽  
Image Texture ◽  
Learning Approaches ◽  
Texture Measurement ◽  
Texture Parameters

Abstract The increase in the use of metal additive manufacturing (AM) processes in major industries like aerospace, defense, and electronics indicates the need for maintaining a tight quality control. A quick, low-cost, and reliable online surface texture measurement and verification system are required to improve its industrial adoption. In this paper, a comprehensive investigation of the surface characteristics of Ti-6Al-4V selective laser melted (SLM) parts using image texture parameters is discussed. The image texture parameters extracted from the surface images using first-order and second-order statistical methods, and measured 3D surface roughness parameters are used for characterizing the SLM surfaces. A comparative study of roughness prediction models developed using various machine learning approaches is also presented. Among the models, the Gaussian process regression (GPR) model gives an accurate prediction of roughness values with an R2 value of more than 0.9. The test data results of all models are presented.

scholarly journals Estimating Compressive Strength of High Performance Concrete with Gaussian Process Regression Model

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Nhat-Duc Hoang ◽  
Anh-Duc Pham ◽  
Quoc-Lam Nguyen ◽  
Quang-Nhat Pham
Keyword(s):  
Machine Learning ◽  
Compressive Strength ◽  
Gaussian Process ◽  
High Performance ◽  
High Performance Concrete ◽  
Gaussian Process Regression ◽  
Experimental Tests ◽  
Learning Performance ◽  
Support Vector ◽  
Data Set

This research carries out a comparative study to investigate a machine learning solution that employs the Gaussian Process Regression (GPR) for modeling compressive strength of high-performance concrete (HPC). This machine learning approach is utilized to establish the nonlinear functional mapping between the compressive strength and HPC ingredients. To train and verify the aforementioned prediction model, a data set containing 239 HPC experimental tests, recorded from an overpass construction project in Danang City (Vietnam), has been collected for this study. Based on experimental outcomes, prediction results of the GPR model are superior to those of the Least Squares Support Vector Machine and the Artificial Neural Network. Furthermore, GPR model is strongly recommended for estimating HPC strength because this method demonstrates good learning performance and can inherently express prediction outputs coupled with prediction intervals.

Exchange Spin Coupling from Gaussian Process Regression

2020 ◽  
Author(s):  
Marc Philipp Bahlke ◽  
Natnael Mogos ◽  
Jonny Proppe ◽  
Carmen Herrmann
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Molecular Magnets ◽  
Molecular Structures ◽  
Spin Coupling ◽  
Structure Property ◽  
Data Set ◽  
Uncertainty Estimates

Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.

Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach

2019 ◽  
Author(s):  
Oskar Flygare ◽  
Jesper Enander ◽  
Erik Andersson ◽  
Brjánn Ljótsson ◽  
Volen Z Ivanov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forests ◽  
Clinical Utility ◽  
Body Dysmorphic Disorder ◽  
Prediction Models ◽  
Behavioral Therapy ◽  
Learning Approach ◽  
Learning Approaches ◽  
Machine Learning Approach

**Background:** Previous attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One way to increase precision and clinical utility could be to use machine learning methods, which can incorporate multiple non-linear associations in prediction models. **Methods:** This study used a random forests machine learning approach to test if it is possible to reliably predict remission from BDD in a sample of 88 individuals that had received internet-delivered cognitive behavioral therapy for BDD. The random forest models were compared to traditional logistic regression analyses. **Results:** Random forests correctly identified 78% of participants as remitters or non-remitters at post-treatment. The accuracy of prediction was lower in subsequent follow-ups (68%, 66% and 61% correctly classified at 3-, 12- and 24-month follow-ups, respectively). Depressive symptoms, treatment credibility, working alliance, and initial severity of BDD were among the most important predictors at the beginning of treatment. By contrast, the logistic regression models did not identify consistent and strong predictors of remission from BDD. **Conclusions:** The results provide initial support for the clinical utility of machine learning approaches in the prediction of outcomes of patients with BDD. **Trial registration:** ClinicalTrials.gov ID: NCT02010619.

COVID-19 Outbreak Prediction with Machine Learning

2020 ◽  
Author(s):  
Sina Faizollahzadeh Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Control Measures ◽  
Future Research ◽  
Complex Nature ◽  
Inference System ◽  
Wide Range ◽  
Standard Models ◽  
High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning

2019 ◽  
Vol 21 (9) ◽  
pp. 662-669 ◽  
Author(s):  
Junnan Zhao ◽  
Lu Zhu ◽  
Weineng Zhou ◽  
Lingfeng Yin ◽  
Yuchen Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Regression Tree ◽  
Large Data ◽  
Thrombin Inhibitors ◽  
Coagulation Cascade ◽  
Gradient Boosting ◽  
Support Vector ◽  
Data Set ◽  
Descriptor Selection

Background: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors. Method: This study was carried out to predict Ki values of thrombin inhibitors based on a large data set by using machine learning methods. Taking advantage of finding non-intuitive regularities on high-dimensional datasets, machine learning can be used to build effective predictive models. A total of 6554 descriptors for each compound were collected and an efficient descriptor selection method was chosen to find the appropriate descriptors. Four different methods including multiple linear regression (MLR), K Nearest Neighbors (KNN), Gradient Boosting Regression Tree (GBRT) and Support Vector Machine (SVM) were implemented to build prediction models with these selected descriptors. Results: The SVM model was the best one among these methods with R2=0.84, MSE=0.55 for the training set and R2=0.83, MSE=0.56 for the test set. Several validation methods such as yrandomization test and applicability domain evaluation, were adopted to assess the robustness and generalization ability of the model. The final model shows excellent stability and predictive ability and can be employed for rapid estimation of the inhibitory constant, which is full of help for designing novel thrombin inhibitors.

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