Utilizing Machine Learning (ML) to oversee the status of hobbing cutters aims to enhance the gear manufacturing process’s effectiveness, output, and quality. Manufacturers can proactively enact measures to optimize tool performance and minimize downtime by conducting precise real-time assessments of hobbing cutter conditions. This proactive approach contributes to heightened product quality and decreased production costs. This study introduces an innovative condition monitoring system utilizing a Machine Learning approach. A Failure Mode and Effect Analysis (FMEA) were executed to gauge the severity of failures in hobbing cutters of Computer Numerical Control (CNC) Hobbing Machine, and the Risk Probability Number (RPN) was computed. This numerical value aids in prioritizing preventive measures by concentrating on failures with the most substantial potential impact. Failures with high RPN numbers were considered to implement the Machine Learning approach and artificial faults were induced in the hobbing cutter. Vibration signals (displacement, velocity, and acceleration) were then measured using a commercial high-capacity and high-frequency range Data Acquisition System (DAQ). The analysis covered operating parameters such as speed (ranging from 35 to 45 rpm), feed (ranging from 0.6 to 1 mm/rev), and depth of cut (6.8 mm). MATLAB code and script were employed to extract statistical features. These features were subsequently utilized to train seven algorithms (Decision Tree, Naive Bayes, Support Vector Machine (SVM), Efficient Linear, Kernel, Ensemble and Neural Network) as well as the application of Bayesian optimization for hyperparameter tuning and model evaluation were done. Amongst these algorithms, J48 Decision tree (DT) algorithm demonstrated impeccable accuracy, correctly classifying 100% of instances in the provided dataset. These algorithms stand out for their accuracy and efficiency in building, making them well-suited for this purpose. Based on ML model performance, it is recommended to employ J48 Decision Tree Model for the condition monitoring of a CNC hobbing cutter. The emerging confusion matrix was crucial in creating a condition monitoring system. This system can analyze statistical features extracted from vibration signals to assess the health of the cutter and classify it accordingly. The system alerts the operator when a hobbing cutter approaches a worn or damaged condition, enabling timely replacement before any issues arise.

Machine learning for monitoring hobbing tool health in CNC hobbing machine

The widespread adoption of smart healthcare technologies raises significant concerns about privacy in the healthcare sector. In fact, patients' privacy is at risk as these technologies become more prevalent, since they allow the sharing of vast amounts of patients' data among different healthcare stakeholders. In response to this challenge, ensuring that service providers respect patients' privacy preferences requires the expression of these preferences and privacy policies in a standardized syntax. This paper proposes a methodology for translating service privacy policies from natural language to a dedicated standardized language, called S4P. The objective isto provide a clear and comprehensible representation of the policies, thereby promoting a well-established understanding of their meaning and allowing appropriate decision-making regarding patient privacy protection. This will help [in] overcoming language barriers, minimizing the risk of privacy violations, and facilitating compliance with various laws and regulations concerning patient privacy protection. The effectiveness of our proposal was validated through experiments against real-world privacy policies of healthcare providers. 

CL2S4P: A Privacy Policy Translator for Comprehensive Interpretation in Smart Healthcare Systems

Este artículo presenta el diseño de una aplicación móvil soportada por una ontología sobre terminología y palabras de uso cotidiano en Colombia. En esta investigación, la aplicación de la ingeniería ontológica permite definir el vocabulario de representación para el dominio de jergas colombianas, que representa los elementos conceptuales y las relaciones entre términos, así como establecer equivalencias y similitudes. Se utilizó el marco METHONTOLOGY, así como el lenguaje de consulta semántica SPARQL para la inferencia y traducción de términos. Como resultado final, la aplicación permite la interpretación de términos y facilita la comunicación entre personas de diferentes regiones colombianas. Además, se integra la aplicación con el servicio de mensajería instantánea de la red social Twitter.

https://revistas.udistrital.edu.co/index.php/revcie/article/download/20593/19222

Aplicación de ingeniería ontológica en la interpretación de jergas colombianas en mensajería instantánea

The triplet extraction (TE) task aims to identify the entities and relations mentioned in a given text. TE consists of two tasks: named entity recognition (NER) and relation classification (RC). Previous work has either treated TE as two separate tasks with independent encoders, or as a single task with a unified encoder. However, both approaches have limitations in capturing the interaction and independence of the features for different subtasks. In this paper, we propose a simple and direct feature selection and interaction scheme. Specifically, we use a pretraining language model (e.g., BERT) to extract various features, including entity recognition, shared, and relation classification features. To capture the interaction, shared features consist of the common semantic information used by the two tasks simultaneously. We use a gate module to obtain the task-specific features. Experimental results on various public benchmarks show that our proposed method can achieve competitive performance, and the calculation speed of our model is seven times faster than CasRel, and two times faster than PFN.

/pdf/an-easy-partition-approach-for-joint-entity-and-relation-e1n1nveb.pdf

An Easy Partition Approach for Joint Entity and Relation Extraction

Surface integrity optimization in AZ31 milling using integrated NSGA-II and GRA methodology

This study introduces a comprehensive framework that integrates Multi-objective optimisation with Multiple-Criteria Decision Making (MCDM) techniques to enhance decision-making in sustainable machining processes. Through a systematic literature review, the framework is developed, incorporating sustainability indicators, experimental validation, and multi-objective optimisation algorithms to identify the optimal machining conditions. To address the challenge of subjective judgements and the multiple solutions generated by multi-objective optimisation, MCDM techniques are employed to select the best solution. By considering economic viability, environmental impact reduction, and social commitment, the proposed framework overcomes the limitations of traditional optimisation approaches. It provides an objective and holistic approach to decision-making, promoting sustainability in machining processes. The framework’s effectiveness and applicability are demonstrated through a case study, validating its potential for practical implementation and informed decision-making 

Development of a framework for sustainability assessment of the machining process through machining parameter optimisation technique

With the growing popularity of online social networks, one common desire of people is to use of social networking services for establishing social relations with others. The boom of social networking has transformed common users into content (data) contributors. People highly rely on social sites to share their ideas and interests and express opinions. Social network sites store all such activities in a data form and exploit the data for various purposes, e.g., marketing, advertisements, product delivery, product research, and even sentiment analysis, etc. Privacy policies primarily defined in Natural Language (NL) specify storage, usage, and sharing of the user’s data and describe authorization, obligation, or denial of specific actions under specific contextual conditions. Although these policies expressed in Natural Language (NL) allow users to read and understand the allowed (or obliged or denied) operations on their data, the described policies cannot undergo automatic control of the actual use of the data by the entities that operate on them. This paper proposes an approach to systematically translate privacy statements related to data from NL into a controlled natural one, i.e., CNL4DSA to improve the machine processing. The methodology discussed in this work is based on a combination of standard Natural Language Processing (NLP) techniques, logic programming, and ontologies. The proposed technique is demonstrated with a prototype implementation and tested with policy examples. The system is tested with a number of data privacy policies from five different social network service providers. Predominantly, this work primarily takes into account two key aspects: (i) The translation of social networks’ data privacy policy and (ii) the effectiveness and efficiency of the developed system. It is concluded that the proposed system can successfully and efficiently translate any common data policy based on an empirical analysis performed of the obtained results.

/pdf/systematic-machine-translation-of-social-network-data-35tuiq6n.pdf

Systematic Machine Translation of Social Network Data Privacy Policies

This study delves into a comparative analysis of two multi-criteria decision analysis methods, Grey Relational Analysis (GRA) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), to fine-tune sustainable machining parameters for AZ31 magnesium alloy. In the realm of straight cutting, TOPSIS reveals an optimal configuration, featuring a spindle speed of 1000 rpm, a feedrate of 375 mm/s, and a depth of cut at 0.5 mm. Conversely, GRA prescribes a different setup: spindle speed at 1000 rpm, feedrate at 1900 mm/s, and depth of cut at 2 mm. In the context of angular cutting, TOPSIS suggests a spindle speed of 1000 rpm, a feedrate of 375 mm/s, and a depth of cut at 0.5 mm, while GRA advocates a spindle speed of 1100 rpm, a feedrate of 1900 mm/s, and a depth of cut at 0.5 mm. A closer look reveals that the Sustainability Assessment Index (SAI) signals that GRA delivers superior sustainability outcomes across both cutting modes. These findings present invaluable insights into the optimization of sustainable machining processes, providing decision-makers with the tools to select the most appropriate approach for their unique requirements. The horizon of future research could expand to include additional parameters and materials, further enriching the tapestry of sustainable machining practices. 

Optimizing sustainable machining for magnesium alloys: a comparative study of GRA and TOPSIS

This review paper aims to analyse the research gaps and opportunities in machine parameter optimisation for sustainable machining of magnesium alloys. The study recommends further research in areas such as novel machining methods, multi-objective optimisation techniques, comprehensive methodologies, and sustainability issues. The report emphasises the need for additional research on the optimisation of machine parameters for magnesium alloys, as they could potentially replace steel and aluminium in some applications. Additionally, the study suggests that future research should focus on the wider implications of sustainability and economic viability, as well as the adoption of optimised machining parameters by industry. 

Machine parameter optimization for sustainable machining of magnesium alloys: a review of current research gaps and opportunities

A hybrid Two Stage Taguchi-Regression-NSGA II-AHP-GRA, Multi-Objective Optimization Framework for Sustainable Straight Slot Milling of AZ31 Magnesium Alloy

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