Roberto Pérez-Rodríguez

Sensing, smart and sustainable product development (S3 product) reference framework

Enterprises must become ‘sensing, smart and sustainable (S3)’ to face global challenges related to local, national and global market dynamics. Therefore, reconceptualisation and redesign in these enterprises must accommodate emergent technologies, new practices and strategies. In this sense, enterprises have used new product development as a strategy for remaining competitive in the marketplace; thus, they can provide a new generation of products offering solutions to contemporary social problems and responding to changing consumer demands. These new-generation products are mostly technology-based and consider sustainable objectives. In this context, concepts such as sensing, smart and sustainable products (S3 products) have emerged to satisfy different social requirements. Therefore, this work focuses on providing a reference framework that presents a systematic process for the development of S3 products. This reference framework is based on the integrated product, process and manufacturing system development reference model. The main objective of this work is to fill the gap vis-à-vis the current lack of design roadmaps that permit the development of this new generation of products in S3 enterprises. The development of a reconfigurable micro-machine tool is presented as that of an S3 product.

Predictive Model for Specific Energy Consumption in the Turning of AISI 316L Steel

This article presents an approach for the simulation of machining operations through Artificial Intelligence, which guarantees an automatic learning of the distinctive features in the processes of metal cutting. In the research, an Artificial Neural Network was designed, which establishes the relationships between the parameters of cutting regime and the technological indexes of machining, based on the information generated in real experimentation. For the conception of suitable cutting strategies, the following magnitudes were considered for the input of the model: lubrication regime, cutting speed, feed rate and machining time; which determined the behavior of the cutting forces in the turning of the AISI 316L steel, in order to obtain the cutting powers that define the specific energy consumption. Several designs were considered according to the features of Multi-Layer Perceptron architecture and the selected model was evaluated according to the mean square error and the regression coefficient R2, reflecting high precision in the approximation. The deviation for the error made in the estimation of the cutting force values represents approximately 2% of the average value. These results showed a good level of reliability in the prediction of energy consumption under various machining conditions, in order to adopt relevant saving measures.

Strategies aimed contributing to the renewable sources of energy from the university processes

At present we are facing a scenario characterized by the increasing exhaustion of the traditional sources of energy. The use of the Renewable Sources of Energy in Cuba is insufficient, since in some cases there is a lack of technologies, procedures and methods adjusted to the Cuban context for its effective use of this type of technology. A research related to these topics themes are carried out at de University of Holguin, for the employment of technologies that take advantage of Renewable Sources of Energy departing mainly from the production of solar, wind, biomass, gasification, biogas, optimization of processes, equipment; as well as the use of design tools, manufacture and technology assisted simulation. In this paper, some strategies are presented from the university processes. To support such strategies, the authors have designed, formulated and prepared diverse papers linked to the processes of academic formation of graduate, postgraduate and scientific research.