A hybrid approach using machine learning to predict the cutting forces under consideration of the tool wear

Recommendation systems have emerged as a response to overload in terms of increased amounts of information online, which has become a problem for users regarding the time spent on their search and the amount of information retrieved by it. In the field of recommendation systems in education, the relevance of recommended educational resources will improve the student’s learning process, and hence the importance of being able to suitably and reliably ensure relevant, useful information. The purpose of this systematic review is to analyze the work undertaken on recommendation systems that support educational practices with a view to acquiring information related to the type of education and areas dealt with, the developmental approach used, and the elements recommended, as well as being able to detect any gaps in this area for future research work. A systematic review was carried out that included 98 articles from a total of 2937 found in main databases (IEEE, ACM, Scopus and WoS), about which it was able to be established that most are geared towards recommending educational resources for users of formal education, in which the main approaches used in recommendation systems are the collaborative approach, the content-based approach, and the hybrid approach, with a tendency to use machine learning in the last two years. Finally, possible future areas of research and development in this field are presented.

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New Hybrid Approach for Developing Automated Machine Learning Workflows: A Real Case Application in Evaluation of Marcellus Shale Gas Production

Fuels ◽

10.3390/fuels2030017 ◽

2021 ◽

Vol 2 (3) ◽

pp. 286-303

Author(s):

Vuong Van Pham ◽

Ebrahim Fathi ◽

Fatemeh Belyadi

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Shale Gas ◽

Field Data ◽

Marcellus Shale ◽

Hybrid Approach ◽

Gas Production ◽

Bayesian Optimization ◽

Real Field ◽

Engineering Problems

The success of machine learning (ML) techniques implemented in different industries heavily rely on operator expertise and domain knowledge, which is used in manually choosing an algorithm and setting up the specific algorithm parameters for a problem. Due to the manual nature of model selection and parameter tuning, it is impossible to quantify or evaluate the quality of this manual process, which in turn limits the ability to perform comparison studies between different algorithms. In this study, we propose a new hybrid approach for developing machine learning workflows to help automated algorithm selection and hyperparameter optimization. The proposed approach provides a robust, reproducible, and unbiased workflow that can be quantified and validated using different scoring metrics. We have used the most common workflows implemented in the application of artificial intelligence (AI) and ML in engineering problems including grid/random search, Bayesian search and optimization, genetic programming, and compared that with our new hybrid approach that includes the integration of Tree-based Pipeline Optimization Tool (TPOT) and Bayesian optimization. The performance of each workflow is quantified using different scoring metrics such as Pearson correlation (i.e., R2 correlation) and Mean Square Error (i.e., MSE). For this purpose, actual field data obtained from 1567 gas wells in Marcellus Shale, with 121 features from reservoir, drilling, completion, stimulation, and operation is tested using different proposed workflows. A proposed new hybrid workflow is then used to evaluate the type well used for evaluation of Marcellus shale gas production. In conclusion, our automated hybrid approach showed significant improvement in comparison to other proposed workflows using both scoring matrices. The new hybrid approach provides a practical tool that supports the automated model and hyperparameter selection, which is tested using real field data that can be implemented in solving different engineering problems using artificial intelligence and machine learning. The new hybrid model is tested in a real field and compared with conventional type wells developed by field engineers. It is found that the type well of the field is very close to P50 predictions of the field, which shows great success in the completion design of the field performed by field engineers. It also shows that the field average production could have been improved by 8% if shorter cluster spacing and higher proppant loading per cluster were used during the frac jobs.

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An expert-model and machine learning hybrid approach to predicting human-agent negotiation outcomes in varied data

Journal on Multimodal User Interfaces ◽

10.1007/s12193-021-00368-w ◽

2021 ◽

Author(s):

Johnathan Mell ◽

Markus Beissinger ◽

Jonathan Gratch

Keyword(s):

Machine Learning ◽

Hybrid Approach ◽

Agent Negotiation ◽

Human Agent ◽

Expert Model

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Experimental Study of tool Wear Mechanisms in Conventional and High Pressure Coolant Assisted Machining of Titanium Alloy Ti17

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.554-557.1961 ◽

2013 ◽

Vol 554-557 ◽

pp. 1961-1966 ◽

Cited By ~ 3

Author(s):

Yessine Ayed ◽

Guenael Germain ◽

Amine Ammar ◽

Benoit Furet

Keyword(s):

High Pressure ◽

Titanium Alloy ◽

Tool Wear ◽

Titanium Alloys ◽

Tool Life ◽

Wear Mechanisms ◽

Cutting Forces ◽

Cutting Edge ◽

Rake Face ◽

High Pressure Coolant

Titanium alloys are known for their excellent mechanical properties, especially at high temperature. But this specificity of titanium alloys can cause high cutting forces as well as a significant release of heat that may entail a rapid wear of the cutting tool. To cope with these problems, research has been taken in several directions. One of these is the development of assistances for machining. In this study, we investigate the high pressure coolant assisted machining of titanium alloy Ti17. High pressure coolant consists of projecting a jet of water between the rake face of the tool and the chip. The efficiency of the process depends on the choice of the operating parameters of machining and the parameters of the water jet such as its pressure and its diameter. The use of this type of assistance improves chip breaking and increases tool life. Indeed, the machining of titanium alloys is generally accompanied by rapid wear of cutting tools, especially in rough machining. The work done focuses on the wear of uncoated tungsten carbide tools during machining of Ti17. Rough and finish machining in conventional and in high pressure coolant assistance conditions were tested. Different techniques were used in order to explain the mechanisms of wear. These tests are accompanied by measurement of cutting forces, surface roughness and tool wear. The Energy-dispersive X-ray spectroscopy (EDS) analysis technique made it possible to draw the distribution maps of alloying elements on the tool rake face. An area of material deposition on the rake face, characterized by a high concentration of titanium, was noticed. The width of this area and the concentration of titanium decreases in proportion with the increasing pressure of the coolant. The study showed that the wear mechanisms with and without high pressure coolant assistance are different. In fact, in the condition of conventional machining, temperature in the cutting zone becomes very high and, with lack of lubrication, the cutting edge deforms plastically and eventually collapses quickly. By contrast, in high pressure coolant assisted machining, this problem disappears and flank wear (VB) is stabilized at high pressure. The sudden rupture of the cutting edge observed under these conditions is due to the propagation of a notch and to the crater wear that appears at high pressure. Moreover, in rough condition, high pressure assistance made it possible to increase tool life by up to 400%.

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Monitoring of Cutting Conditions with Dry Cutting on CNC Turning Machine

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.443.382 ◽

2010 ◽

Vol 443 ◽

pp. 382-387 ◽

Cited By ~ 6

Author(s):

Somkiat Tangjitsitcharoen ◽

Suthas Ratanakuakangwan

Keyword(s):

Surface Roughness ◽

Tool Wear ◽

Cutting Forces ◽

Cutting Temperature ◽

Cutting Conditions ◽

Cutting Condition ◽

Nose Radius ◽

Dry Cutting ◽

Cnc Turning ◽

Coated Carbide

This paper presents the additional work of the previous research in order to verify the previously obtained cutting condition by using the different cutting tool geometries. The effects of the cutting conditions with the dry cutting are monitored to obtain the proper cutting condition for the plain carbon steel with the coated carbide tool based on the consideration of the surface roughness and the tool life. The dynamometer is employed and installed on the turret of CNC turning machine to measure the in-process cutting forces. The in-process cutting forces are used to analyze the cutting temperature, the tool wear and the surface roughness. The experimentally obtained results show that the surface roughness and the tool wear can be well explained by the in-process cutting forces. Referring to the criteria, the experimentally obtained proper cutting condition is the same with the previous research except the rake angle and the tool nose radius.

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Intelligente Überwachung von Werkzeugverschleiß/Intelligent monitoring of tool wear based on machine internal data

wt Werkstattstechnik online ◽

10.37544/1436-4980-2021-05-43 ◽

2021 ◽

Vol 111 (05) ◽

pp. 309-313

Author(s):

Christian Brecher ◽

Tiandong Xi ◽

Igor Medeiros Benincá ◽

Sebastian Kehne ◽

Marcel Fey

Keyword(s):

Machine Learning ◽

Tool Wear ◽

Machine Tools ◽

Sensor Data ◽

Machine Learning Method ◽

Process Variables ◽

Monitoring Tool ◽

Intelligent Monitoring ◽

Current Axis ◽

Axis Position

Numerische Steuerungen für Werkzeugmaschinen erfassen eine erhebliche Menge an Sensordaten für die Achsregelung. Diese liefern nicht nur Informationen über die aktuellen Achspositionen oder die Ströme, sondern können mithilfe von Modellen auch für das Monitoring von anderen Prozessgrößen verwendet werden. In diesem Beitrag wird ein Machine-Learning-Verfahren zur Überwachung von Werkzeugverschleiß untersucht, welches allein auf maschinen-internen Daten basiert.   Numerical controls for machine tools acquire a considerable amount of sensor data for axis control. This information, such as the current axis position or the motor currents, can be used for monitoring other process variables with the aid of models. This article investigates a machine learning method for monitoring tool wear in machine tools, based on machine-internal data only.

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Artificial Intelligence Assisted Well Portfolio Optimization - An Automated Reservoir Management Advisory System to Maximize the Asset Value - Case Study from ADNOC Onshore

10.2118/207274-ms ◽

2021 ◽

Author(s):

Subba Ramarao Rachapudi Venkata ◽

Nagaraju Reddicharla ◽

Shamma Saeed Alshehhi ◽

Indra Utama ◽

Saber Mubarak Al Nuimi ◽

...

Keyword(s):

Machine Learning ◽

Hybrid Approach ◽

Simulation Models ◽

Production Optimization ◽

Added Value ◽

Risk Scores ◽

Well Performance ◽

Field Development ◽

Advisory System ◽

Gas Lift

Abstract Matured hydrocarbon fields are continuously deteriorating and selection of well interventions turn into critical task with an objective of achieving higher business value. Time consuming simulation models and classical decision-making approach making it difficult to rapidly identify the best underperforming, potential rig and rig-less candidates. Therefore, the objective of this paper is to demonstrate the automated solution with data driven machine learning (ML) & AI assisted workflows to prioritize the intervention opportunities that can deliver higher sustainable oil rate and profitability. The solution consists of establishing a customized database using inputs from various sources including production & completion data, flat files and simulation models. Automation of Data gathering along with technical and economical calculations were implemented to overcome the repetitive and less added value tasks. Second layer of solution includes configuration of tailor-made workflows to conduct the analysis of well performance, logs, output from simulation models (static reservoir model, well models) along with historical events. Further these workflows were combination of current best practices of an integrated assessment of subsurface opportunities through analytical computations along with machine learning driven techniques for ranking the well intervention opportunities with consideration of complexity in implementation. The automated process outcome is a comprehensive list of future well intervention candidates like well conversion to gas lift, water shutoff, stimulation and nitrogen kick-off opportunities. The opportunity ranking is completed with AI assisted supported scoring system that takes input from technical, financial and implementation risk scores. In addition, intuitive dashboards are built and tailored with the involvement of management and engineering departments to track the opportunity maturation process. The advisory system has been implemented and tested in a giant mature field with over 300 wells. The solution identified more techno-economical feasible opportunities within hours instead of weeks or months with reduced risk of failure resulting into an improved economic success rate. The first set of opportunities under implementation and expected a gain of 2.5MM$ with in first one year and expected to have reoccurring gains in subsequent years. The ranked opportunities are incorporated into the business plan, RMP plans and drilling & workover schedule in accordance to field development targets. This advisory system helps in maximizing the profitability and minimizing CAPEX and OPEX. This further maximizes utilization of production optimization models by 30%. Currently the system was implemented in one of ADNOC Onshore field and expected to be scaled to other fields based on consistent value creation. A hybrid approach of physics and machine learning based solution led to the development of automated workflows to identify and rank the inactive strings, well conversion to gas lift candidates & underperforming candidates resulting into successful cost optimization and production gain.

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Agreement and Reliability Analysis of Machine Learning Scaling in the Assessment of Acute Proximal Weakness by Experts and Non-Experts: Clinical Feasibility Study (Preprint)

10.2196/preprints.29698 ◽

2021 ◽

Author(s):

Eunjeong Park ◽

Kijeong Lee ◽

Taehwa Han ◽

Hyo Suk Nam

Keyword(s):

Machine Learning ◽

Reliability Analysis ◽

Gold Standard ◽

Assessment Tool ◽

Expert Knowledge ◽

Hybrid Approach ◽

Assessment Model ◽

Lower Limbs ◽

Neurological Deficits ◽

Percent Agreement

BACKGROUND Assessing the symptoms of proximal weakness caused by neurological deficits requires expert knowledge and experienced neurologists. Recent advances in artificial intelligence and the Internet of Things have resulted in the development of automated systems that emulate physicians’ assessments. OBJECTIVE This study provides an agreement and reliability analysis of using an automated scoring system to evaluate proximal weakness by experts and non-experts. METHODS We collected 144 observations from acute stroke patients in a neurological intensive care unit to measure the symptom of proximal weakness of upper and lower limbs. A neurologist performed a gold standard assessment and two medical students performed identical tests as non-expert assessments for manual and machine learning-based scaling of Medical Research Council (MRC) proximal scores. The system collects signals from sensors attached on patients’ limbs and trains a machine learning assessment model using the hybrid approach of data-level and algorithm-level methods for the ordinal and imbalanced classification in multiple classes. For the agreement analysis, we investigated the percent agreement of MRC proximal scores and Bland-Altman plots of kinematic features between the expert- and non-expert scaling. In the reliability analysis, we analysed the intra-class correlation coefficients (ICCs) of kinematic features and Krippendorff’s alpha of the three observers’ scaling. RESULTS The mean percent agreement between the gold standard and the non-expert scaling was 0.542 for manual scaling and 0.708 for IoT-assisted machine learning scaling, with 30.63% enhancement. The ICCs of kinematic features measured using sensors ranged from 0.742 to 0.850, whereas the Krippendorff’s alpha of manual scaling for the three observers was 0.275. The Krippendorff’s alpha of machine learning scaling increased to 0.445, with 61.82% improvement. CONCLUSIONS Automated scaling using sensors and machine learning provided higher inter-rater agreement and reliability in assessing acute proximal weakness. The enhanced assessment supported by the proposed system can be utilized as a reliable assessment tool for non-experts in various emergent environments.

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Stock Price Prediction Using Convolutional Neural Networks on a Multivariate Time Series

10.36227/techrxiv.15088734 ◽

2021 ◽

Author(s):

Sidra Mehtab ◽

Jaydip Sen

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Stock Prices ◽

Stock Price ◽

Regression Models ◽

Research Work ◽

Hybrid Approach ◽

Forecast Horizon ◽

Stock Price Prediction ◽

Price Prediction

Prediction of future movement of stock prices has been a subject matter of many research work. On one hand, we have proponents of the Efficient Market Hypothesis who claim that stock prices cannot be predicted, on the other hand, there are propositions illustrating that, if appropriately modelled, stock prices can be predicted with a high level of accuracy. There is also a gamut of literature on technical analysis of stock prices where the objective is to identify patterns in stock price movements and profit from it. In this work, we propose a hybrid approach for stock price prediction using machine learning and deep learning-based methods. We select the NIFTY 50 index values of the National Stock Exchange (NSE) of India, over a period of four years: 2015 – 2018. Based on the NIFTY data during 2015 – 2018, we build various predictive models using machine learning approaches, and then use those models to predict the “Close” value of NIFTY 50 for the year 2019, with a forecast horizon of one week, i.e., five days. For predicting the NIFTY index movement patterns, we use a number of classification methods, while for forecasting the actual “Close” values of NIFTY index, various regression models are built. We, then, augment our predictive power of the models by building a deep learning-based regression model using Convolutional Neural Network (CNN) with a walk-forward validation. The CNN model is fine-tuned for its parameters so that the validation loss stabilizes with increasing number of iterations, and the training and validation accuracies converge. We exploit the power of CNN in forecasting the future NIFTY index values using three approaches which differ in number of variables used in forecasting, number of sub-models used in the overall models and, size of the input data for training the models. Extensive results are presented on various metrics for all classification and regression models. The results clearly indicate that CNN-based multivariate forecasting model is the most effective and accurate in predicting the movement of NIFTY index values with a weekly forecast horizon.

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