With the advent of the fourth industrial revolution, the application of artificial intelligence in the manufacturing domain is becoming prevalent. Maintenance is one of the important activities in the manufacturing process, and it requires proper attention. To decrease maintenance costs and to attain sustainable operational management, Predictive Maintenance (PdM) has become important in industries. The principle of PdM is forecasting the next failure; thus, the respective maintenance is scheduled before the predicted failure occurs. In the construction of maintenance management, facility managers generally employ reactive or preventive maintenance mechanisms. However, reactive maintenance does not have the ability to prevent failure and preventive maintenance does not have the ability to predict the future condition of mechanical, electrical, or plumbing components. Therefore, to improve the facilities’ lifespans, such components are repaired in advance. In this paper, a PdM planning model is developed using intelligent methods. The developed method involves five main phases: (a) data cleaning, (b) data normalization, (c) optimal feature selection, (d) prediction network decision-making, and (e) prediction. Initially, the data pertaining to PdM are subjected to data cleaning and normalization in order to arrange the data within a particular limit. Optimal feature selection is performed next, to reduce redundant information. Optimal feature selection is performed using a hybrid of the Jaya algorithm and Sea Lion Optimization (SLnO). As the prediction values differ in range, it is difficult for machine learning or deep learning face to provide accurate results. Thus, a support vector machine (SVM) is used to make decisions regarding the prediction network. The SVM identifies the network in which prediction can be performed for the concerned range. Finally, the prediction is accomplished using a Recurrent Neural Network (RNN). In the RNN, the weight is optimized using the hybrid J-SLnO. A comparative analysis demonstrates that the proposed model can efficiently predict the future condition of components for maintenance planning by using two datasets—aircraft engine and lithium-ion battery datasets.

Many advanced product manufacturing approaches have been introduced in the market in recent years. Thus, it is critical to develop modern techniques which can effectively familiarize budding minds with the latest manufacturing procedures. In fact, the contemporary training methods and advanced education practices are crucial to uphold the interest of the new generation as well as to equip them with state-of the art systems. There is a need for innovative ideas and effective methodologies to inculcate the desired competency and prepare students for prospective manufacturing set ups. In the latest Industry 4.0 paradigm, visualization technologies, especially virtual reality, have been emphasized to sustainably train and educate young students. This work presents a technique for utilizing the leading visualization method based on virtual reality in product manufacturing. It aims to acquaint students with the prominent concept of Industry 4.0, the reconfigurable manufacturing system (RMS). The RMS has been a demanding topic for the novice and, most often, amateurs are not able to grasp and interpret it. Therefore, this paper outlines the various steps that can be useful for students in order to anticipate the RMS design, interact with it, understand its operation, and evaluate its performance.

Electric discharge machining (EDM) is one of the most efficient manufacturing technologies used in highly accurate processing of all electrically conductive materials irrespective of their mechanical properties. It is a non-contact thermal energy process applied to a wide range of applications, such as in the aerospace, automotive, tools, molds and dies, and surgical implements, especially for the hard-to-cut materials with simple or complex shapes and geometries. Applications to molds, tools, and dies are among the large-scale initial applications of this process. Machining these items is especially difficult as they are made of hard-to-machine materials, they have very complex shapes of high accuracy, and their surface characteristics are sensitive to machining conditions. The review of this kind with an emphasis on tool and die materials is extremely useful to relevant professions, practitioners, and researchers. This review provides an overview of the studies related to EDM with regard to selection of the process, material, and operating parameters, the effect on responses, various process variants, and new techniques adopted to enhance process performance. This paper reviews research studies on the EDM of different grades of tool steel materials. This article (i) pans out the reported literature in a modular manner with a focus on experimental and theoretical studies aimed at improving process performance, including material removal rate, surface quality, and tool wear rate, among others, (ii) examines evaluation models and techniques used to determine process conditions, and (iii) discusses the developments in EDM and outlines the trends for future research. The conclusion section of the article carves out precise highlights and gaps from each section, thus making the article easy to navigate and extremely useful to the related research community.

Traceability plays a vital role in the success of Halal Supply Chain (HSC). HSC revolve around the essential dimension of Halal Integrity (HI), whereas traceability is seemed to be medium to assure integrity. Thus, a need is felt to identify the factors which are critical to the successful implementation of traceability in Halal Supply Chain Management (HSCM). Identified Twelve Critical Success Factors (CSFs) through an extensive review of literature and opinion of experts. Further, a contextual relationship among the CSFs is developed using Total Interpretive Structure Modelling (TISM) approach and derived a model. The structural model is analyzed using Fuzzy MICMAC (Matrice d’Impacts Croises-Multipication Applique and Classment-cross-impact matrix multiplication applied to classification) approach to identify the importance of CSFs by driving and dependence power. The primary result indicates towards; that improving the HSCM with the higher level of Halal awareness. Assuring HI will enhance the consumer satisfaction which leads to a competitive advantage for the organization. Academic researchers, industrial practitioners and Supply Chain executives can understand the complex interrelationship of CSFs by visualizing the TISM. It can help the management, lobbies and government to develop the policies regarding the implementation.

Particularly in the last decade, Internet usage has been growing rapidly. However, as the Internet becomes a part of the day to day activities, cybercrime is also on the rise. Cybercrime will cost nearly $6 trillion per annum by 2021 as per the cybersecurity ventures report in 2020. For illegal activities, cybercriminals utilize any network computing devices as a primary means of communication with a victims’ devices, so attackers get profit in terms of finance, publicity and others by exploiting the vulnerabilities over the system. Cybercrimes are steadily increasing daily. Evaluating cybercrime attacks and providing protective measures by manual methods using existing technical approaches and also investigations has often failed to control cybercrime attacks. Existing literature in the area of cybercrime offenses suffers from a lack of a computation methods to predict cybercrime, especially on unstructured data. Therefore, this study proposes a flexible computational tool using machine learning techniques to analyze cybercrimes rate at a state wise in a country that helps to classify cybercrimes. Security analytics with the association of data analytic approaches help us for analyzing and classifying offenses from India-based integrated data that may be either structured or unstructured. The main strength of this work is testing analysis reports, which classify the offenses accurately with 99 percent accuracy.