The Harmony Search Algorithm (HSA) is a swarm intelligence optimization algorithm which has been successfully applied to a broad range of clustering applications, including data clustering, text clustering, fuzzy clustering, image processing, and wireless sensor networks. We provide a comprehensive survey of the literature on HSA and its variants, analyze its strengths and weaknesses, and suggest future research directions.

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A Comprehensive Survey of the Harmony Search Algorithm in Clustering Applications

Learning information from a single or a few samples is called few-shot learning. This learning method will solve deep learning’s dependence on a large sample. Deep learning achieves few-shot learning through meta-learning: “how to learn by using previous experience”. Therefore, this paper considers how the deep learning method uses meta-learning to learn and generalize from a small sample size in image classification. The main contents are as follows. Practicing learning in a wide range of tasks enables deep learning methods to use previous empirical knowledge. However, this method is subject to the quality of feature extraction and the selection of measurement methods supports set and the target set. Therefore, this paper designs a multi-scale relational network (MSRN) aiming at the above problems. The experimental results show that the simple design of the MSRN can achieve higher performance. Furthermore, it improves the accuracy of the datasets within fewer samples and alleviates the overfitting situation. However, to ensure that uniform measurement applies to all tasks, the few-shot classification based on metric learning must ensure the task set’s homologous distribution.

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A Few Shot Classification Methods Based on Multiscale Relational Networks

Strategies based on various aspects of clustering in wireless sensor networks using classical, optimization and machine learning techniques: Review, taxonomy, research findings, challenges and future directions

Kh 2 po 4 型結晶の複屈折と構造との關係

Clustering protocols in wireless sensor network: A survey, classification, issues, and future directions

An increase in unplanned downtime of machines disrupts and degrades the industrial business, which results in substantial credibility damage and monetary loss. The cutting tool is a critical asset of the milling machine; the failure of the cutting tool causes a loss in industrial productivity due to unplanned downtime. In such cases, a proper predictive maintenance strategy by real-time health monitoring of cutting tools becomes essential. Accurately predicting the useful life of equipment plays a vital role in the predictive maintenance arena of industry 4.0. Many active research efforts have been done to estimate tool life in varied directions. However, the consolidated study of the implemented techniques and future pathways is still missing. So, the purpose of this paper is to provide a systematic and comprehensive literature survey on the data-driven approach of Remaining Useful Life (RUL) estimation of cutting tools during the milling process. The authors have summarized different monitoring techniques, feature extraction methods, decision-making models, and available sensors currently used in the data-driven model. The authors have also presented publicly available datasets related to milling under various operating conditions to compare the accuracy of the prediction model for tool wear estimation. Finally, the article concluded with the challenges, limitations, recent advancements in RUL prognostics techniques using Artificial Intelligence (AI), and future research scope to explore more in this area.

/pdf/data-driven-remaining-useful-life-estimation-for-milling-5baxeupiyx.pdf

Data-Driven Remaining Useful Life Estimation for Milling Process: Sensors, Algorithms, Datasets, and Future Directions

Cluster based routing strategies are popular categories of routing protocols among wireless sensor networks (WSNs). In a typical cluster based routing protocols, few nodes are elected as cluster Head (CH) nodes and they form clusters with other nodes of the networks known as cluster members. The data sensed by cluster members is sent to CH node for data aggregation and further processing. Data Aggregation is considered to be one of the important methods used to prolong the network lifetime of WSN. Data aggregation is quite popular among cluster based routing protocols for WSN, where the data packets are aggregated by intermediate nodes till it reaches to BS. Data aggregation ultimately helps in reducing the number of data messages thereby assisting in network life prolongation. In this paper, an intra-cluster data aggregation technique (ICA) for WSNs is proposed. ICA constructs the intra cluster data aggregation path from a source node to its CH node in an energy efficient way. The data packets are aggregated along the aggregation path by intermediate relay nodes till the message reaches to designated CH node. Comparative analysis of ICA, LEACH and LEACH-C protocols are carried by considering the parameters such as number of alive nodes, energy consumption, number of data packets received by BS. Results obtained show that ICA performs better compared to LEACH and LEACH-C protocols.

Energy efficient intra-cluster data aggregation technique for wireless sensor network

In the era of Industry 4.0, the idea of 3D printed products has gained momentum and is also proving to be beneficial in terms of financial and time efforts. These products are physically built layer-by-layer based on the digital Computer Aided Design (CAD) inputs. Nonetheless, 3D printed products are still subjected to defects due to variation in properties and structure, which leads to deterioration in the quality of printed products. Detection of these errors at each layer level of the product is of prime importance. This paper provides the methodology for layer-wise anomaly detection using an ensemble of machine learning algorithms and pre-trained models. The proposed combination is trained offline and implemented online for fault detection. The current work provides an experimental comparative study of different pre-trained models with machine learning algorithms for monitoring and fault detection in Fused Deposition Modelling (FDM). The results showed that the combination of the Alexnet and SVM algorithm has given the maximum accuracy. The proposed fault detection approach has low experimental and computing costs, which can easily be implemented for real-time fault detection.

Enhancing Surface Fault Detection Using Machine Learning for 3D Printed Products

The Fourth Industrial Revolution drives industries from traditional manufacturing to the smart manufacturing approach. In this transformation, existing equipment, processes, or devices are retrofitted with some sensors and other cyber-physical systems (CPS), and adapted towards digital production, which is a blend of critical enabling technologies. In the current scenario of Industry 4.0, industries are shaping themselves towards the development of customized and cost-effective processes to satisfy customer needs with the aid of a digital twin framework, which enables the user to monitor, simulate, control, optimize, and identify defects and trends within, ongoing process, and reduces the chances of human prone errors. This paper intends to make an appraisal of the literature on the digital twin (DT) framework in the domain of smart manufacturing with the aid of critical enabling technologies such as data-driven systems, machine learning and artificial intelligence, and deep learning. This paper also focuses on the concept, evolution, and background of digital twin and the benefits and challenges involved in its implementation. The Scopus and Web of Science databases from 2016 to 2021 were considered for the bibliometric analysis and used to study and analyze the articles that fall within the research theme. For the systematic bibliometric analysis, a novel approach known as Proknow-C was employed, including a series of procedures for article selection and filtration from the existing databases to get the most appropriate articles aligned with the research theme. Additionally, the authors performed statistical and network analyses on the articles within the research theme to identify the most prominent research areas, journal/conference, and authors in the field of a digital twin. This study identifies the current scenarios, possible research gaps, challenges in implementing DT, case studies and future research goals within the research theme.

Sustainable Development of Smart Manufacturing Driven by the Digital Twin Framework: A Statistical Analysis

The present research paper focuses on optimisation of wear test parameters for different deep cryogenic treated AISI M2 tool steel material. Commercially available, AISI M2 tool steel material is machined as per ASTM G99-05 standards and was subjected to deep cryogenic treatment for the different holding time of 12, 24 and 36 h, followed by tempering for 2 h at $$150\,^{\circ }\hbox {C}$$
 . The specimens were tested for their wear resistance using a pin-on-disc wear testing setup adopting Taguchi‘s design of experiments approach. The control variables selected were deep cryogenic treatment holding time, the speed of the rotating disc and load applied to the specimen pin. The wear test was conducted as per trials generated by Taguchi‘s $$\hbox {L}_{27}$$
 orthogonal array, and the results are analysed using signal-to-noise ratio and analysis of variance. The results show that the cryogenic holding time has majorly affected the wear resistance followed by the load on the pin and disc speed. It is also observed that 24 h holding time for deep cryogenic treatment yields better wear resistance compared to 12 and 36 h. Microstructure and X-ray diffraction analysis of the cryogenically treated specimens reveals the conversion of retained austenite into martensite and the formation of fine carbides in the martensite lattice after deep cryogenic treatment process which are responsible for the increase in wear resistance. Also, the worn surface analysis shows that the adhesion wear, oxidation wear and abrasion wear are the predominant wear mechanisms observed under different testing conditions.

Arunkumar Bongale

Papers

Data-Driven Remaining Useful Life Estimation for Milling Process: Sensors, Algorithms, Datasets, and Future Directions

Energy efficient intra-cluster data aggregation technique for wireless sensor network

Enhancing Surface Fault Detection Using Machine Learning for 3D Printed Products

Sustainable Development of Smart Manufacturing Driven by the Digital Twin Framework: A Statistical Analysis

Deep Cryogenic Treatment of AISI M2 Tool Steel and Optimisation of Its Wear Characteristics Using Taguchi‘s Approach