Supervised Learning Applied to Graduation Forecast of Industrial Engineering Students

<p style="text-align: justify;">The article aims to develop a machine-learning algorithm that can predict student’s graduation in the Industrial Engineering course at the Federal University of Amazonas based on their performance data. The methodology makes use of an information package of 364 students with an admission period between 2007 and 2019, considering characteristics that can affect directly or indirectly in the graduation of each one, being: type of high school, number of semesters taken, grade-point average, lockouts, dropouts and course terminations. The data treatment considered the manual removal of several characteristics that did not add value to the output of the algorithm, resulting in a package composed of 2184 instances. Thus, the logistic regression, MLP and XGBoost models developed and compared could predict a binary output of graduation or non-graduation to each student using 30% of the dataset to test and 70% to train, so that was possible to identify a relationship between the six attributes explored and achieve, with the best model, 94.15% of accuracy on its predictions.</p>

The New Safety Trends: The Challenges through Industry 4.0

Industrial engineering achieved rapid growth in providing safety measurements in all industries, following different safety policies to prevent faults in sectors. Industrial safety is an essential feature to give an accident-free environment. The safety policies and measurements encourage the industrial people to work in different perilous conditions. Industries prepare their safety policy and safety manual to identify various faults and risks. It is necessary to create awareness in industrial working members, and industries maintain special departments for safety. The safety guidelines prevent occupational injuries and accidents. The safety rules and regulations reduce the waste of human and other resources in industries. The study evaluates safety models used in industry to identify issues involved in the selection, implementation, and evaluation. This research provides insight into the overall process for industrial safety and, most essential, overviews on the methodology. Predicting industrial faults and risks emphasized the industrial engineering process and used machine learning algorithms for classifications. Many issues and challenges discussed industrial safety and provided novel innovation ideas for researchers.

Optimization of Costs and Production Process of Fire Dampers

Currently, companies are trying to optimize production, even more so as the economic situation is gradually deteriorating in many countries around the world, which is currently caused mainly by the COVID 19 pandemic. A specific type of production is that on production lines, which are individual workplaces where various production and assembly operations take place. The aim of the article is to analyze the impact of the choice of the control method for the production process on the optimization of costs and the process. The subject of the research is the production of fire-fighting throttles on a production line that has three workplaces. The problem of waste on the production line was identified by direct measurements of time consumption at the individual workplaces and by using images of the working day schedule. The article compares three approaches to production line management and waste reduction. The first approach was based on counting the number of finished products on the production line. The second approach analyzed in detail the duration of the individual operations into which the workplaces were divided. In the third approach, a method of balancing workplaces was used, which brought time and cost savings. The achieved results, using different methods of production management, are analyzed in detail, and the conclusions evaluated. The article contributes a comparison of the achieved results of cost optimization through the course of production of fire dampers. The research results were applied in practice. The research used methods of industrial engineering in the field of ergonomics, technical and economic analysis, and production management. Visualization of hourly performances, downtimes and failures, execution of work procedures, Shop Floor Management and many others, are recommended as ways to implement waste elimination when using the methods of industrial engineering.