The emergence of Industry 4.0, also referred to as the fourth industrial revolution, has entirely transformed how the industry or business functions and evolves. It can be attributed to its broadening focus on automation, decentralization, system integration, cyber-physical systems, etc. Its implementation promises numerous benefits in terms of higher productivity, greater volatility, better control and streamlining of processes, accelerated enterprise growth, sustainable development, etc. Despite the worldwide recognition and realization of Industry 4.0, its holistic adoption is constrained by the requirements of specific skills among the workforce. The personnel are expected to acquire adaptive thinking, cognitive and computational skills, predominantly in the area of information technology, data analytics, etc. Thus, the universities that laid the foundation for future talents or trends in society have to adapt and modernize the existing programs, facilities, and infrastructure. This reshaping of higher education in consonance with the vision of Industry 4.0 possesses its opportunities and challenges. There are, of course, a multitude of factors involved and they need a reasonable assessment to strategically plan this metamorphosis. Therefore, this work aims to explore and analyze the different factors that influence the progression and enactment of Industry 4.0 in universities for sustainable education. For this purpose, a systematic approach based on a questionnaire as well as a SWOT (strengths (S), weaknesses (W), opportunities (O), and threats (T)) integrated with the analytic hierarchy process (AHP) is adopted. The questionnaires are administered to university employees and students (or stakeholders) to assess their viewpoint, as well as to estimate the priority values for individual factors to be included in SWOT. The AHP is implemented to quantify the different factors in terms of weights using a pairwise comparison matrix. Finally, the SWOT matrix is established depending on the questionnaire assessment and the AHP weights to figure out stakeholders’ perspectives, in addition to the needed strategic scheme. The SWOT implementation of this research proposes an aggressive approach for universities, where they must make full use of their strengths to take advantage of the emerging opportunities in Industry 4.0. The results also indicate that there are fundamental requirements for universities in Industry 4.0, including effective financial planning, skilled staff, increased industrial partnerships, advanced infrastructure, revised curricula, and insightful workshops. This investigation undoubtedly underlines the importance of practical expertise and the implementation of digital technologies at the university level to empower novices with the requisite skills and a competitive advantage for Industry 4.0.

With the development of Industry 4.0 and the emergence of the smart factory concept, the traditional philosophy of manufacturing systems will change. The smart factory introduces changes to the factors and elements of traditional manufacturing systems and incorporates the current requirements of smart systems so that it can compete in the future. An increasing amount of research in both academia and industry is dedicated to transitioning the concept of the smart factory from theory to practice. The purpose of the current research is to highlight the perspectives that shape the smart factory and to suggest approaches and technical support to enable the realization of those perspectives. This paper fills this gap by identifying and analyzing research on smart factories. We suggest a framework to analyze existing research and investigate the elements and features of smart factory systems.

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.

Titanium alloy is widely used in modern automobile industries due to its higher strength with corrosion resistance. Such higher strength materials can be effectively machined using unconventional machining processes, especially the electro-chemical micro machining (ECMM) process. It is important to enhance the machining process by investigating the effects of electrolytes and process parameters in ECMM. The presented work describes the influence of three different combinations of Sodium Chloride-based electrolytes on machining Titanium (Ti-6Al-4V) alloy. Based on the ECMM process parameters such as applied voltage, electrolytic concentration, frequency and duty cycle on response, characteristics are determined by the Taguchi design of experiments. The highest material removal rate (MRR) was achieved by the Sodium Chloride and Sodium Nitrate electrolyte. The combination of Sodium Chloride and Citric Acid achieve highest Overcut and Circularity. The optimal overcut was observed from the Sodium Chloride and Glycerol electrolyte due to the presence of glycerol. The better conicity was obtained from Sodium Chloride and Citric Acid in comparison with other electrolytes. A Sodium Chloride and Glycerol combination could generate better machined surface owing to the chelating effect of Glycerol.

Dispatching rules are generally useful for scheduling jobs in flexible manufacturing systems (FMSs). However, the appropriateness of these rules relies heavily on the condition of the system; thus, there is no single rule that always outperforms others. In this state of affairs, diverse machine-learning technology offers an effective approach for dynamic scheduling, as it allows managers to identify the most suitable rule at each moment. Nonetheless, various machine-learning algorithms may provide diverse recommendations. The main objective of this study is to implement FMS scheduling using intelligent hybrid learning algorithms with metaheuristic improvements. The developed model involves three phases: feature extraction, optimal weighted feature extraction, and prediction. After the benchmark datasets for the FMS are gathered, feature extraction is performed using t-distributed stochastic neighbor embedding, linear discriminant analysis, linear square regression, and higher-order statistical features. Further, an optimal weighted feature extraction method is developed to select the optimal features with less correlation using the improved lion algorithm (LA), which is called the modified nomadic-based LA (MN-LA). Finally, the optimally selected weighted features are subjected to a hybrid learning algorithm with the integration of a fuzzy classifier and a deep belief network (DBN). For improving the prediction model, the membership function of the fuzzy classifier is optimized using the proposed MN-LA. Moreover, the activation function and the number of hidden neurons in the DBN are optimized using the MN-LA. The main objective of the optimized hybrid classifier is to enhance prediction accuracy. The experimental results indicate the effectiveness of the proposed heuristic-based scheduling method for FMSs.