The incorporation of game mechanics and dynamics in non-gaming applications is a subject of interest in various sectors such as education, marketing, medicine and military, in the last few years. It is believed that engineering education in a pre-graduate level and in professional practice will bring high pay-offs. The role of the academia is to develop new methodologies and tools to produce, apply and use digital games and gamification techniques in contemporary industry and present scientific evidence on the value and the benefits derived from this technology. In this paper, the relative literature is evaluated and a discussion on the gamification status today is given, by examining various aspects of this novel term. Furthermore, game techniques, gamification practices in education and e-learning are considered. Special discussion on engineering games, gamification platforms and empirical surveys is presented with focus on manufacturing.

Various artificial neural networks types are examined and compared for the prediction of surface roughness in manufacturing technology. The aim of the study is to evaluate different kinds of neural networks and observe their performance and applicability on the same problem. More specifically, feed-forward artificial neural networks are trained with three different back propagation algorithms, namely the adaptive back propagation algorithm of the steepest descent with the use of momentum term, the back propagation Levenberg–Marquardt algorithm and the back propagation Bayesian algorithm. Moreover, radial basis function neural networks are examined. All the aforementioned algorithms are used for the prediction of surface roughness in milling, trained with the same input parameters and output data so that they can be compared. The advantages and disadvantages, in terms of the quality of the results, computational cost and time are identified. An algorithm for the selection of the spread constant is applied and tests are performed for the determination of the neural network with the best performance. The finally selected neural networks can satisfactorily predict the quality of the manufacturing process performed, through simulation and input–output surfaces for combinations of the input data, which correspond to milling cutting conditions.