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Background: The growing complexity of production processes in the metal–mechanical industry demands ever more effective strategies for managing machine and equipment maintenance, as unexpected failures can incur high operational costs and compromise productivity by interrupting workflows and delaying deliveries. However, few studies have combined end-to-end data analytics and data mining methods to proactively predict and mitigate such failures. This study aims to develop and validate a comprehensive framework combining data analytics and data mining to prevent machine failures and support decision-making in a metal–mechanical manufacturing environment. Methods: First, exploratory data analytics were performed on the sensor and logistics data to identify significant relationships and trends between variables. Next, a preprocessing pipeline including data cleaning, data transformation, feature selection, and resampling was applied. Finally, a decision tree model was trained to identify conditions prone to failures, enabling not only predictions but also the explicit representation of knowledge in the form of decision rules. Results: The outstanding performance of the decision tree (82.1% accuracy and a Kappa index of 78.5%), which was modeled from preprocessed data and the insights produced by data analytics, demonstrates its ability to generate reliable rules for predicting failures to support decision-making. The implementation of the proposed framework enables the optimization of predictive maintenance strategies, effectively reducing unplanned downtimes and enhancing the reliability of production processes in the metal–mechanical industry.

This work explores the modeling and optimization of the conditions to obtain blue color intensities in the dyeing cotton process with Reactive Black 5 (RB5), by means of an approach that combines the techniques response surface methodology (RSM), artificial neural network (ANN) and particle swarm optimization (PSO). By means of RSM technique, the interactions and the effects of the main process variables (factors) on the behavior of coloristic intensity (K S−1) were investigated. For this, a 26 central composite rotational design was carried out considering the factors temperature, NaCl, Na2CO3, NaOH, processing time and RB5 concentration. The investigation conducted with RSM was used to indicate which process variables would compose the input layer of a multilayer perceptron ANN (MLP-ANN), which was trained with the data produced in the dyeing experiments to predict K S−1 values. Then, the PSO and MLP-ANN techniques were combined to determine the optimized condition for obtaining a desired K S−1 value at the lowest production cost. The results achieved by RSM show that all investigated factors have a considerable effect on the behavior of K S−1. In addition, the determination coefficient obtained (R2 = 0.942) in the predictions made by the MLP-ANN confirms its effectiveness in modeling the nonlinear behavior of dyeing with RB5. Finally, the combination of PSO with MLP-ANN proved to be a very useful computational tool for providing optimized conditions to obtain colors of the blue palette using RB5 dye with the lowest production costs, facilitating the assembly of the dyes in the textile industry and promoting the saving of chemical inputs and the reduction of process time and economic costs.Graphic abstract

The objective of this study was to apply simulation and genetic algorithms for the economic and environmental optimization of the reverse network (manufacturers, waste managers, and recyclers in Sao Paulo, Brazil) of waste from electrical and electronic equipment (WEEE) to promote the circular economy. For the economic evaluation, the reduction in fuel, drivers, insurance, depreciation, maintenance, and charges was considered. For the environmental evaluation, the impact of abiotic, biotic, water, land, air, and greenhouse gases was measured. It was concluded that the optimized structure of the WEEE reverse chains for Sao Paulo, Brazil provided a reduction in the number of collections, thus making the most of cubage. It also generated economic and environmental gains, contributing to the strategic actions of the circular economy. Therefore, the proposed approach is replicable in organizational practice, which is mainly required to meet the 2030 agenda of reducing the carbon footprint generated by transport in large cities. Thus, this study can guide companies in structuring the reverse WEEE chains in Sao Paulo, Brazil, and other states and countries for economic and environmental optimization, which is an aspect of great relevance considering the exponential generation of WEEE.

Background: The growing global demand for grains and the pursuit of greater efficiency in agroindustrial production processes have fueled scientific interest in technologies for automatic visual inspection of agricultural grains (AVIAG). Despite the increasing number of studies on this topic, few have addressed the practical implementation of these technologies within industrial environments. Objective: This study aims to investigate the technological demands, analyze the potential applications, and identify the challenges for technology transfer of AVIAG technologies to the agroindustrial sector. Methods: The methodological approach combined a comprehensive literature review, which enabled the mapping of AVIAG technology applications and technological maturity levels, with a structured survey designed to identify practical demands, challenges, and barriers to technology transfer in the agricultural sector. Results: The results show that most of the proposed solutions exhibit low technological maturity and require significant adaptation for practical application, which undermines the discussion on technology transfer. Conclusions: The main barriers to large-scale adoption of AVIAG technologies include limited dissemination of scientific knowledge, a shortage of skilled labor, high implementation costs, and resistance to changes in production processes. Nonetheless, the literature highlights benefits, such as increased automation, enhanced operational efficiency, and reduced post-harvest losses, which reinforce the potential of AVIAG technologies in advancing the modernization of the agroindustrial sector.

The proliferation of electronic goods manufacturing and the subsequent rise in electronic waste (e-waste) generation necessitate the establishment of efficient Waste of Electrical and Electronic Equipment (WEEE) reverse logistics systems, fostering collaborative efforts among manufacturers, retailers, and government agencies. Given its importance, this theme has received considerable attention in recent literature. This study focused on investigating the relationships between socio-spatial characteristics and the distribution of WEEE collection points in the city of São Paulo, Brazil. To this end, data mining (DM) techniques were applied to generate rules representing knowledge that explains the relationship among the considered variables. The results achieved (accuracy 81.25% and Kappa statistic 74.71%), indicating consistent patterns, demonstrate the potential of the proposed approach to aid WEEE reverse chain management. From a practical point of view, the knowledge produced is an important support for decision-making on the installation of new collection points, considering the socio-spatial characteristics of the target locations. In addition, this research contributes to the responsible management of solid waste recommended by the Brazilian National Solid Waste Policy (NSWP), as well as to the advancement of the United Nations’ Sustainable Development Goals (UN SDGs), particularly SDG 11 (Sustainable Cities and Communities) and SDG 12 (Responsible Consumption and Production), by fostering sustainable practices in waste management and resource utilization within urban contexts.

This work presents a hybrid approach called GA-NN for solving the Capacitated Vehicle Routing Problem (CVRP) using Genetic Algorithms (GA) and Nearest Neighbor heuristic (NN). The first technique was applied to determine the groups of customers to be served by the vehicles while the second is responsible to build the route of each vehicle. In addition, the heuristics of Gillett & Miller (GM) and Downhill (DH) were used, respectively, to generate the initial population of GA and to refine the solutions provided by GA. In the results section, we firstly present experiments demonstrating the performance of the NN heuristic for solving the Shortest Path and Traveling Salesman problems. The results obtained in such experiments constitute the main motivation for proposing the GA-NN. The second experimental study shows that the proposed hybrid approach achieved good solutions for instances of CVRP widely known in the literature, with low computational cost. It also allowed us to evidence that the use of GM and DH helped the hybrid GA-NN to converge on promising points in the search space, with a small number of generations.

In the context of Industry 4.0, managing large amounts of data is essential to ensure informed decision-making in intelligent production environments. It enables, for example, predictive maintenance, which is essential for anticipating and identifying causes of failures in machines and equipment, optimizing processes, and promoting proactive management of human, financial, and material resources. However, generating accurate information for decision-making requires adopting suitable data preprocessing and analysis techniques. This study explores the identification of machine failures based on synthetic industrial data. Initially, we applied the feature selection techniques Principal Component Analysis (PCA), Minimum Redundancy Maximum Relevance (mRMR), Neighborhood Component Analysis (NCA), and Denoising Autoencoder (DAE) to the collected data and compared their results. In the sequence, a comparison among three widely known machine learning classifiers, namely Random Forest (RF), Support Vector Machine (SVM), and Multilayer Perceptron neural network (MLP), was conducted, with and without considering feature selection. The results showed that PCA and RF were superior to the other techniques, allowing the classification of failures with rates of 0.98, 0.97, and 0.98 for the accuracy, precision, and recall metrics, respectively. Thus, this work contributes by solving an industrial problem and detailing techniques to identify the most relevant variables and machine learning algorithms for predicting machine failures that negatively impact production planning. The findings provided by this study can assist industries in giving preference to employing sensors and collecting data that can contribute more effectively to machine failure predictions.

Defect detection is an important task in glass manufacturing. Despite the importance of the visual inspection of glass products, many of the visual inspection processes are performed manually. The problem is that human inspection presents some drawbacks, such as being time-consuming, the high cost involved, and the lack of standardization. In this context, the development of automated processes for the inspection of glass products is important. In this paper, we propose an intelligent vision system for the automatic inspection of two types of defects in glass products: the first one is detection of a critical defect in glass cups for food packaging and domestic use, called glass sparkle or fragment of glass, and the second one is identification of a defect called deformation in plates for domestic use. To evaluate these applications, we used an apparatus consisting of a conveyor belt and a camera controlled by a PC to simulate an industrial line of production. The results indicate that the developed applications are suitable for the detection of investigated defects because for both applications, the hit rate was above 95 %.

With the advent of Industry 4.0, the use of new technologies, robotization and advanced manufacturing has been extended to the agricultural sector, with the aim of increasing productivity, reducing environmental impacts, increasing profits and improving product quality from where emerged the terms Precision Agriculture, Agribusiness 4.0, Agriculture 4.0 and Agri-industry-4.0. However, while much is being said about adopting new technologies in the stages of soil preparation, planting and harvesting, little is said about the processing of agricultural products using, for example, automated systems for visual quality inspection. This work aims to investigate the different approaches for automatic visual inspection of grain quality proposed in the last decade and present a discussion about how these approaches are inserted in the context of these new productive processes of modern agriculture, as well as the positive aspects and limitations found for their uses.

The significant volume of vehicular traffic has been considered one of the main causes of air pollution due to the rapid growth of urbanization and motorization in the world. This trend has instigated efforts to search for sustainable solutions aimed not only at mitigating the deleterious consequences stemming from air pollution but also at implementing efficacious urban mobility strategies and policies. In this context, the present study endeavors to explore the modeling and predicting of hospitalizations and associated costs linked to respiratory diseases, influenced by vehicular pollutants within the urban milieu of São Paulo—a city renowned for harboring one of the largest vehicular fleets globally. Specifically, an adaptive neuro-fuzzy inference system (ANFIS) was developed based on pollutant data encompassing carbon monoxide (CO), Particulate matter with diameters less than 10 µm (PM 10 ), Particulate matter with diameters less than 2.5 µm (PM 2.5 ), nitrogen dioxide (NO 2 ), oone (O 3 ), and sulfur dioxide (SO 2 ), emitted within the city confines spanning the period from 2011 to 2019. The simulations conducted revealed that with knowledge of the monthly concentrations of the analyzed pollutants, it was feasible to forecast hospitalization rates and costs with an error lower than 6%. Additionally, scenarios illustrating the applicability of ANFIS in public health management and its contributions to the United Nations Sustainable Development Goals (SDGs) are presented and discussed. Graphical abstract