Purpose This paper aims to propose a learning evaluation model for Green Belts and Black Belts at the training level. A question bank has been developed on the basis of Bloom’s learning classification and applied to a group of employees who were being trained in Six Sigma (SS). Their results were then used to decide on the students’ approval and to guide the instructor’s plan of teaching for the next classes. Design/methodology/approach An action research has been conducted to develop a question bank of 310 questions based on the revised Bloom’s Taxonomy, to implement the evaluation model, and to apply it during the SS training. Findings The evaluation model has been designed so that the students do not proceed unless they have acquired the conceptual knowledge at each step of the DMAIC (Define, Measure, Analyze, Improve and Control) roadmap. At the end of the evaluation process, the students’ results have been analyzed. The number of mistakes in all stages of DMAIC was equal, implying that the training was uniform the entire roadmap. However, the opposite happened in each of the Bloom’s Taxonomy levels, showing that some skills need to be better stimulated by the instructor than others. Research limitations/implications The learning evaluation model proposed in this paper has been applied to a group of 70 employees who were being trained in SS at a Brazilian aircraft manufacturer. The data have been analyzed using Microsoft Excel® and Minitab® 17 Statistical Software. Originality/value Despite the abundance of courses offering the SS Green Belt and Black Belt certifications, there is no standard evaluation to ensure the training quality. Thus, this paper proposes an innovative learning evaluation model.
This paper aims the planning, construction and modeling of a low-cost multivariable level plant for didactic purposes. The developed model has two pumps that feed four tanks coupled to each other. Between the tanks and the pumps, there are inlet and outlet valves that can change the system dynamics according to its opening configuration. To automate the pumps and read the instrumentation used, the Arduino microcontroller was chosen because it is a model of great use in the academic environment and of easy parameterization. For sensing, the HC-SR04 ultrasonic sensor was chosen, which already has native compatibility with the microcontroller. In order to validate the constructed plant, it was necessary to identify its model, using the empirical step response method for this purpose. In this way, this work has both qualitative and quantitative characteristics, since the planning and construction of the didactic plant involved an exploratory research of the problem, and then the modeling and simulation method was applied to obtain the mathematical model of the plant. Finally, an experimental research was conducted, comparing the data obtained in the real plant with the model data for validation. Having completed all research stages, the work result is a didactic plant with good linearity, able to provide implementation of level control strategies of coupled tanks and to assist in teaching and learning subjects that involve concepts of dynamic systems, besides multivariable systems identification and control.
The present work consists of carrying out the identification and implementation of a predictive controller in a thermal system to follow a didactic design methodology for learning in systems identification, analysis, and controller design. For the identification of the thermal system, the method of least squares was chosen because it allows to obtain a faithful mathematical model that describes the temperature system. The data used in the system identification step are from a didactic industrial plant of a temperature system. From the model obtained for the system in question, the design and implementation of a classic Proportional Integral Derivative (PID) controller and a predictive controller are carried out to verify the behavior of the system against the actions of both controllers. The entire implementation will be carried out in the MATLAB/Simulink computer simulation environment.
This work aims to use ESP32 and ESP8266 microprocessors with built-in Wi-Fi modules to activate the digital inputs of a PLC via the Web, in addition to sending the command signal via wireless network to the respective actuators of the system. A local wireless network is created to allow control of inputs via a browser on a cell phone or computers, such as allowing the exchange of information between the controller and its actuators. To test the functionality of the proposed prototype, a three-phase motor was started using the browser of a cell phone connected to the ESP32 Wi-Fi network.
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