Photothermal techniques allow the detection of characteristics of material without invading it. Researchers have developed hardware for some specific Phase and Amplitude detection (Lock-In Function) applications, eliminating space and unnecessary electronic functions, among others. This work shows the development of a Digital Lock-In Amplifier based on a Field Programmable Gate Array (FPGA) for low-frequency applications. This system allows selecting and generating the appropriated frequency depending on the kind of experiment or material studied. The results show good frequency stability in the order of 1.0 × 10−9 Hz, which is considered good linearity and repeatability response for the most common Laboratory Amplitude and Phase Shift detection devices, with a low error and standard deviation.

This article proposes the creation of a course based on a series of practical sessions, where the students have to develop their practical knowledge about artificial intelligence techniques, specifically multilayer perceptron. The novelty of this paper is based on the constructivism methodology regarding artificial intelligence and sustainable development. Moreover, it can be implemented in different majors because of the flexibility in certain aspects. It is oriented to evaluate skills in the broad education necessary to understand the impact of engineering solutions in a global, economic, environmental, and societal context. The proposal helps the students to turn theoretical concepts into more tangible objects where they can build their knowledge by programming their implementations in software. Then, programming codes for practicing the neural networks theory, finite impulse response, empirical mode decomposition and discrete wavelet transform are achieved to compare percentage classification between different techniques. Also, it measures the interaction between the student and the theoretical mathematics of artificial intelligence. The continuous evaluations at the end of the practical sessions corroborate the increase in the knowledge of the students. A study based on rubrics illustrates an increase in the average grade obtained by the students in the elaboration of each practice. Finally, a senior project is carried out by taking into account sustainable development issues and the usage of tools of artificial intelligence.

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Sistema basado en redes neuronales artificiales para el monitoreo de la herramienta en fresadoras CNC AbstractMost of the companies have as objective to manufacture high-quality products, then by optimizing costs, reducing and controlling the variations in its production processes it is possible. Within manufacturing industries a very important issue is the tool condition monitoring, since the tool state will determine the quality of products. Besides, a good monitoring system will protect the machinery from severe damages. For determining the state of the cutt ing tools in a milling machine, there is a great variety of models in the industrial market, however these systems are not available to all companies because of their high costs and the requirements of modifying the machining tool in order to att ach the system sensors. This paper presents an intelligent classifi cation system which determines the status of cutt ers in a Computer Numerical Control (CNC) milling machine. This tool state is mainly detected through the analysis of the cutt ing forces drawn from the spindle motors currents. This monitoring system does not need sensors so it is no necessary to modify the machine. The correct classifi cation is made by advanced digital signal processing techniques. Just after acquiring a signal, a FIR digital fi lter is applied to the data to eliminate the undesired noisy components and to extract the embedded force components. A Wavelet Transformation is applied to the fi ltered signal in order to compress the data amount and to optimize the classifi er structure. Then a multilayer perceptron-type neural network is responsible for carrying out the classifi cation of the signal. Achieving a reliability of 95%, the system is capable of detecting breakage and a worn cutt er.