There are several methods of analysis used in the metalworking industry for dry machining processes and with Minimum Quantity Lubrication (MQL). Evolutionary methods [1] have been used in the decision-making process in the machining process to select the optimal data and to analyze the behavior of variables such as cutting speed (V), feed rate (f) and cutting depth (a_p). This work addresses the use of evolutionary algorithms of low dominance class II and III (NSGA-II and NSGA-III) to analyze from the multicriteria approach the initial wear of the cutting tool (VB), the energy consumption (E) and the machining time (t) in the turning process of the AISI 316L steel workpiece for biomedical purposes. As input variables to the algorithm with 54 records, there are: cutting speed (V: 200, 300, 400 m/min) and feed rate (f: 0.1, 0.15, 0.2 mm/rev). The experiment was developed for a dry (1) turning operation and with the use of MQL (-1). For the MQL lubrication regime, a TRI-COOL MD-1 lubricant was employed, a vegetable type used in ferrous and non-ferrous metal cutting operations. A BIDEMICS JX1 ceramic cutting tool was used.

Conventional fuzzy multi-criteria decision-making (MCDM) methods support ranking under uncertainty but often provide limited explanation of why alternatives are preferred. This study proposes an explainable fuzzy decision-making framework that integrates the Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy TOPSIS with surrogate modeling, SHAP-based analysis, and linguistic rule extraction. The main contribution is an explanation layer that preserves the original FAHP–FTOPSIS ranking structure while decomposing ranking scores into criterion-level contributions and transforming recurrent attribution patterns into IF–THEN rules. The framework is evaluated through a supplier-selection case study using expert fuzzy evaluations, local perturbation analysis, leave-one-supplier-out cross-validation, and a synthetic benchmark. The results show that the fuzzy MCDM layer produces discriminative rankings and that the top-ranked supplier remains comparatively stable under perturbations. Among the tested surrogates, the Random Forest Regressor achieved the strongest local fidelity, outperforming linear regression and a shallow decision tree. SHAP analysis showed ordinal alignment between FAHP weights and global criterion importance, while the extracted rules achieved high coverage, consistency, and threshold stability. The framework is useful for researchers, decision analysts, procurement managers, and supply chain professionals who require transparent, interpretable, and auditable multicriteria decisions under uncertainty.

La presente investigación tiene como objetivo el diseño e implementación de un sistema para el control de la calidad de los gases de combustión de una caldera pirotubular de 5 BHP. En base al porcentaje de O2 presente en los gases de combustión, medido mediante una sonda Lambda, se determina el porcentaje de CO2 que se emite a la atmósfera. Un control proporcional PID se encarga de la regulación automática de la entrada de aire a la caldera mediante un actuador, según el valor de la concentración de oxígeno registrado en los gases de combustión. El sistema de control cuenta con una pantalla HMI y un PLC modular. Los resultados alcanzados aseguran la emisión de gases contaminantes dentro de los parámetros establecidos por las normas ambientales vigentes lográndose así, la calidad requerida de los gases de combustión y la reducción del consumo de combustible de la caldera.

The load flow is of great importance in assisting the process of decision making and planning of generation, distribution and transmission of electricity. Ignorance of the values in this indicator, as well as their inappropriate prediction, difficult decision making and efficiency of the electricity service, and can cause undesirable situations such as; the on demand, overheating of the components that make up a substation, and incorrect planning processes electricity generation and distribution. Given the need for prediction of flow of electric charge of the substations in Ecuador this research proposes the concept for the development of an automated prediction system employing the use of Artificial Neural Networks. El flujo de carga tiene gran relevancia en la asistencia del proceso de toma de decisiones y planificación de la generación, distribución y trasmisión de energía eléctrica. El desconocimiento de los valores de este indicador, así como su inadecuada predicción, dificulta la toma de decisiones y eficiencia del servicio eléctrico, además puede ocasionar situaciones indeseadas tales como; la sobre demanda, el sobre calentamiento de los componentes que integran una subestación, y la no correcta planificación de los procesos de generación y distribución eléctrica. Dada la necesidad de predicción de flujo de carga eléctrica de las subestaciones en el Ecuador la presente investigación propone la concepción para el desarrollo de un sistema automatizado de predicción empleando el uso de Redes Neuronales Artificiales.

Artificial Neural Networks in the Demand Forecasting of a Metal-Mechanical Industry