scholarly journals Multivariable regression and gradient boosting algorithms for energy prediction in the radial-axial ring rolling (rarr) process

2021 ◽  
Author(s):  
Irene Mirandola ◽  
Guido A. Berti ◽  
Roberto Caracciolo ◽  
Seungro Lee ◽  
Naksoo Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Numerical Simulations ◽  
Manufacturing Industry ◽  
Ring Rolling ◽  
Gradient Boosting ◽  
Process Conditions ◽  
Forming Process ◽  
Practical Utilization ◽  
Energy Prediction ◽  
Axial Ring

Energy prediction and starvation have become an essential part of process planning for the XXI century manufacturing industry due to cost-saving policies and environmental regulations. To this aim, the research presented in this paper details how machine learning-based algorithms can be an effective way to predict and minimize the energy consumptions in the widely spread radial-axial ring rolling (RARR) process. To analyze this bulk metal forming process, 380 numerical simulations have been developed using the commercial SW Simufact Forming 15 and considering three largely utilized materials, the 42CrMo4 steel, the IN 718 superalloy, and the AA6082 aluminum alloy. To create the database for both multi-variable regression and machine learning models, ring outer diameters ranging from 650 mm to 2000 mm and various process conditions including different sets of tool speeds and initial temperatures have been considered. For the case of the multi-variable regression model, to account for all the cross-influences between all the parameters, a second-order function including 26 parameters has been developed, resulting in a reasonable average accuracy (94 %) but also in an impractical huge equation. On the other hand, the machine learning model based on the Gradient Boosting (GB) approach allows obtaining a similar accuracy (96 %) but its compact form allows a more practical utilization and its training can be expanded almost indefinitely, by adding more results from both numerical simulations and experiments. The proposed approach allows to quickly and precisely predict the energy consumption in the RARR process and can be extended to other manufacturing processes.

scholarly journals Machine Learning-Based Models for the Estimation of the Energy Consumption in Metal Forming Processes

Metals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 833
Author(s):  
Irene Mirandola ◽  
Guido A. Berti ◽  
Roberto Caracciolo ◽  
Seungro Lee ◽  
Naksoo Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Finite Element ◽  
Energy Consumption ◽  
Metal Forming ◽  
Mutual Influence ◽  
Rolling Process ◽  
Ring Rolling ◽  
Gradient Boosting ◽  
Average Error ◽  
Ring Rolling Mill

This research provides an insight on the performances of machine learning (ML)-based algorithms for the estimation of the energy consumption in metal forming processes and is applied to the radial-axial ring rolling process. To define the mutual influence between ring geometry, process settings, and ring rolling mill geometries with the resulting energy consumption, measured in terms of the force integral over the processing time (FIOT), FEM simulations have been implemented in the commercial SW Simufact Forming 15. A total of 380 finite element simulations with rings ranging from 650 mm < DF < 2000 mm have been implemented and constitute the bulk of the training and validation datasets. Both finite element simulation settings (input), as well as the FI (output), have been utilized for the training of eight machine learning models, implemented with Python scripts. The results allow defining that the Gradient Boosting (GB) method is the most reliable for the FIOT prediction in forming processes, being its maximum and average errors equal to 9.03% and 3.18%, respectively. The trained ML models have been also applied to own and literature experimental cases, showing a maximum and average error equal to 8.00% and 5.70%, respectively, thus proving once again its reliability.

Intended Dishing in Ring Rolling

2014 ◽  
Vol 611-612 ◽  
pp. 194-201 ◽  
Author(s):  
Joachim Seitz ◽  
Gideon Schwich ◽  
Gerhard Hirt
Keyword(s):  
Ring Rolling ◽  
Process Time ◽  
Fe Model ◽  
Forming Process ◽  
Cross Sectional ◽  
Axial Ring ◽  
Flow Mechanisms ◽  
Metal Forming Process ◽  
Additional Costs ◽  
Ring Climbing

Ring Rolling is a versatile metal-forming process to manufacture seamless rings of various cross-sectional geometries. Rings with a “dish shape” are used in different areas such as offshore, aeronautics or the energy sector. Current ways to produce dish shaped rings have the disadvantages of limited or inflexible geometries and either high material waste, additional costs for special tools or long process time. Instead, when manufacturing dish shaped rings on conventional radial-axial ring rolling mills, ring producers will be able to expand the range of their products easily. In a prior investigation, the general feasibility of an alternative to the current manufacturing processes was shown in experiments and in finite element method (FEM) simulations, avoiding major additional machining and material costs. Resulting from an analysis of the geometrical requirements and material flow mechanisms for dishing and ring climbing, a rolling strategy was derived, applying a large height reduction of the ring. A major problem of this rolling strategy is that whenever the contact between the ring and the main roll is lost in the process, the ring starts to oscillate around the mandrel and neither dishing nor ring climbing can be observed. In order to ensure a permanent contact between ring and main roll and in order to stabilize the ring in its inclined position in the rolling mill, additional stabilizing measures of the process will be developed and investigated. With the developed FE-model, a stabilizing measure by the use of pressure rolls and automatic guide roll movement for ring climbing was tested and appears promising for the application in a real experimental environment.

scholarly journals Experimental Investigation and Optimal Prediction of Maximum Forming Angle and Surface Roughness of an Al/SUS Bimetal Sheet in an Incremental Forming Process Using Machine Learning

Materials ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4150
Author(s):  
Raneen Abd Ali ◽  
Wenliang Chen ◽  
M.S.H. Al-Furjan ◽  
Xia Jin ◽  
Ziyu Wang
Keyword(s):  
Machine Learning ◽  
Surface Roughness ◽  
Incremental Forming ◽  
Single Point ◽  
Continuous Variable ◽  
Gradient Boosting ◽  
Forming Process ◽  
Step Size ◽  
Forming Angle ◽  
Tool Diameter

Bimetal sheets have superior properties as they combine different materials with different characteristics. Producing bimetal parts using a single-point incremental forming process (SPIF) has increased recently with the development of industrial requirements. Such types of sheets have multiple functions that are not applicable in the case of monolithic sheets. In this study, the correlation between the operating variables, the maximum forming angle, and the surface roughness is established based on the ensemble learning using gradient boosting regression tree (GBRT). In order to obtain the dataset for the machine learning, a series of experiments with continuous variable angle pyramid shape were carried out based on D-Optimal design. This design is created based on numerical variables (i.e., tool diameter, step size, and feed rate) and categorical variable (i.e., layer arrangement). The grid search cross-validation (CV) method was used to determine the optimum GBRT parameters prior to model training. After the parameter tuning and model selection, the model with a better generalization performance is obtained. The reliability of the predictive models is confirmed by the testing samples. Furthermore, the microstructure of the aluminum/stainless steel (Al/SUS) bimetal sheet is analyzed under different levels of operating parameters and layer arrangements. The microstructure results reveal that severe cracks are attained in the case of a small tool diameter while a clear refinement is observed when a high tool diameter value with small step down is used for both Al and SUS layers.

scholarly journals Improving quality prediction in radial-axial ring rolling using a semi-supervised approach and generative adversarial networks for synthetic data generation

2021 ◽  
Author(s):  
Simon Fahle ◽  
Thomas Glaser ◽  
Andreas Kneißler ◽  
Bernd Kuhlenkötter
Keyword(s):  
Machine Learning ◽  
Synthetic Data ◽  
Ring Rolling ◽  
Supervised Machine Learning ◽  
Generative Adversarial Networks ◽  
Quality Prediction ◽  
Data Generation ◽  
Adversarial Networks ◽  
Synthetic Data Generation ◽  
Axial Ring

AbstractAs artificial intelligence and especially machine learning gained a lot of attention during the last few years, methods and models have been improving and are becoming easily applicable. This possibility was used to develop a quality prediction system using supervised machine learning methods in form of time series classification models to predict ovality in radial-axial ring rolling. Different preprocessing steps and model implementations have been used to improve quality prediction. A semi-supervised approach is used to improve the prediction and analyze, to what extend it can improve current research in machine learning for quality prediciton. Moreover, first research steps are taken towards a synthetic data generation within the radial-axial ring rolling domain using generative adversarial networks.

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

BAND NN: A Deep Learning Framework For Energy Prediction and Geometry Optimization of Organic Small Molecules

2019 ◽  
Author(s):  
Siddhartha Laghuvarapu ◽  
Yashaswi Pathak ◽  
U. Deva Priyakumar
Keyword(s):  
Machine Learning ◽  
Density Functional ◽  
Computational Cost ◽  
Geometry Optimization ◽  
Dft Methods ◽  
Energy Prediction ◽  
Machine Learning Model ◽  
Equilibrium Structures ◽  
High Level ◽  
Non Equilibrium

Recent advances in artificial intelligence along with development of large datasets of energies calculated using quantum mechanical (QM)/density functional theory (DFT) methods have enabled prediction of accurate molecular energies at reasonably low computational cost. However, machine learning models that have been reported so far requires the atomic positions obtained from geometry optimizations using high level QM/DFT methods as input in order to predict the energies, and do not allow for geometry optimization. In this paper, a transferable and molecule-size independent machine learning model (BAND NN) based on a chemically intuitive representation inspired by molecular mechanics force fields is presented. The model predicts the atomization energies of equilibrium and non-equilibrium structures as sum of energy contributions from bonds (B), angles (A), nonbonds (N) and dihedrals (D) at remarkable accuracy. The robustness of the proposed model is further validated by calculations that span over the conformational, configurational and reaction space. The transferability of this model on systems larger than the ones in the dataset is demonstrated by performing calculations on select large molecules. Importantly, employing the BAND NN model, it is possible to perform geometry optimizations starting from non-equilibrium structures along with predicting their energies.

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.

Predicting Undesired Treatment Outcome in Mental Healthcare: Machine Learning Study (Preprint)

2019 ◽  
Author(s):  
Kasper Van Mens ◽  
Joran Lokkerbol ◽  
Richard Janssen ◽  
Robert de Lange ◽  
Bea Tiemens
Keyword(s):  
Machine Learning ◽  
Treatment Outcome ◽  
Mental Health Treatment ◽  
Mental Healthcare ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Trade Off ◽  
Trade Offs ◽  
Outcome Monitoring ◽  
Extreme Gradient Boosting

BACKGROUND It remains a challenge to predict which treatment will work for which patient in mental healthcare. OBJECTIVE In this study we compare machine algorithms to predict during treatment which patients will not benefit from brief mental health treatment and present trade-offs that must be considered before an algorithm can be used in clinical practice. METHODS Using an anonymized dataset containing routine outcome monitoring data from a mental healthcare organization in the Netherlands (n = 2,655), we applied three machine learning algorithms to predict treatment outcome. The algorithms were internally validated with cross-validation on a training sample (n = 1,860) and externally validated on an unseen test sample (n = 795). RESULTS The performance of the three algorithms did not significantly differ on the test set. With a default classification cut-off at 0.5 predicted probability, the extreme gradient boosting algorithm showed the highest positive predictive value (ppv) of 0.71(0.61 – 0.77) with a sensitivity of 0.35 (0.29 – 0.41) and area under the curve of 0.78. A trade-off can be made between ppv and sensitivity by choosing different cut-off probabilities. With a cut-off at 0.63, the ppv increased to 0.87 and the sensitivity dropped to 0.17. With a cut-off of at 0.38, the ppv decreased to 0.61 and the sensitivity increased to 0.57. CONCLUSIONS Machine learning can be used to predict treatment outcomes based on routine monitoring data.This allows practitioners to choose their own trade-off between being selective and more certain versus inclusive and less certain.

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