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The consumer age of the Personal Computer and mobile devices has opened up a new world of opportunities for innovative teaching methodologies, many based on serious games and virtual worlds. Similar levels of market penetration are expected for the use of Immersive Virtual Reality (iVR) over upcoming decades, once all the core technologies for game engines and head-mounted displays are available on the market at affordable prices. In this chapter, a general overview of the state of the art of iVR learning experiences is presented. Firstly, the advantages of iVR over traditional learning are described - advantages that must be considered when defining iVR experiences for the optimization of student learning and satisfaction. Secondly, the relationship between learning theories and iVR experiences is briefly summarized; an area where constructivist theories appear to be the most commonly used theory in iVR experiences. Thirdly, some examples of the success of iVR applications at different learning levels, from primary school to higher education, are summarized. Fourthly, the key factors for the successful design and use of an iVR experience in education are identified, from the predesign stage to the final evaluation - with special attention given to the different possibilities of each type of HMD for different kinds of educational experiences. Finally, the main limitations of iVR for learning today and the future trends of this technology for teaching are also identified and discussed.

Immersive Virtual Reality (iVR) is a new technology, the novelty effect of which can reduce the enjoyment of iVR experiences and, especially, learning achievements when presented in the classroom; an effect that the interactive tutorial proposed in this research can help overcome. Its increasingly complex levels are designed on the basis of Mayer’s Cognitive Theory of Multimedia Learning, so that users can quickly gain familiarity with the iVR environment. The tutorial was included in an iVR learning experience for its validation with 65 users. It was a success, according to the user satisfaction and tutorial usability survey. First, it gained very high ratings for satisfaction, engagement, and immersion. Second, high skill rates suggested that it helped users to gain familiarity with controllers. Finally, a medium-high value for flow pointed to major concerns related to skill and challenges with this sort of iVR experience. A few cases of cybersickness also arose. The survey showed that only intense cybersickness levels significantly limited performance and enjoyment; low levels had no influence on flow and immersion and little influence on skill, presence, and engagement, greatly reducing the benefits of the tutorial, despite which it remained useful., This work was partially supported by the ACIS project (Reference Number INVESTUN/21/BU/0002) of the Consejeria de Empleo e Industria of the Junta de Castilla y León (Spain), the Erasmus+ RISKREAL Project (Reference Number 2020-1-ES01-KA204-081847) of the European Commission and the HumanAid Project (Reference Number TED2021-129485B-C43) of the Proyectos Estratégicos Orientados a la Transición Ecológica y a la Transición Digital of the Spanish Ministry of Science and Innovation.

Este trabajo analiza las posibilidades de la Realidad Virtual Inmersiva (RVI) en la formacion en prevencion de riesgos laborales en la industria manufacturera actual. En primer lugar, se presenta un marco general con aspectos clave que hay que considerar en el diseno de experiencias RVI. En segundo lugar, se presentan dos ejemplos concretos ya validados experimentalmente, para demostrar la utilidad de este esquema para la deteccion de riesgos laborales. En el primero de ellos, el trabajador puede controlar en primera persona un puente grua en un entorno virtual realista, tanto por la calidad visual como por la forma de movimiento de los objetos y cargas; ademas de realizar distintas operaciones criticas tanto por el nivel sonoro del entorno, iluminacion, interaccion con otros trabajadores y, en especial, por el equilibrado y geometria de las cargas a transportar. En estas condiciones debe realizar distintas tareas habituales (carga, movimiento, descarga de piezas…) y atender a la prevencion de accidentes. En el segundo, el trabajador debe desplazarse por una fabrica e identificar situaciones de riesgo, asi como tomar las medidas correctivas apropiadas. Mientras que en la primera aplicacion el usuario interactua con el entorno virtual utilizando una botonera real de puente grua para aumentar su inmersion, en el segundo se utilizan mandos estandar de RVI por su facilidad para simular la interaccion con objetos virtuales. En ambos casos un sistema de adquisicion de datos, tanto de posicionamiento como de control de la mirada, permiten al formador proporcionar rapidamente al usuario retroalimentacion sobre su rendimiento y los puntos de mejora observados. Palabras clave: Realidad Virtual; Prevencion de Riesgos Laborales; Industria 4.0; Puente grua; Juegos Educativos

Serious games have to meet certain characteristics relating to gameplay and educational content to be effective as educational tools. There are some models that evaluate these aspects, but they usually lack a good balance between both ludic and learning requirements, and provide no guide for the design of new games. This study develops the Gaming Educational Balanced (GEB) Model which addresses these two limitations. GEB is based on the Mechanics, Dynamics and Aesthetics framework and the Four Pillars of Educational Games theory. This model defines a metric to evaluate serious games, which can also be followed to guide their subsequent development. This rubric is tested with three indie serious games developed using different genres to raise awareness of mental illnesses. This evaluation revealed two main issues: the three games returned good results for gameplay, but the application of educational content was deficient, due in all likelihood to the lack of expert educators participating in their development. A statistical and machine learning validation of the results is also performed to ensure that the GEB metric features are clearly explained and the players are able to evaluate them correctly. These results underline the usefulness of the new metric tool for identifying game design strengths and weaknesses. Future works will apply this metric to more serious games to further test its effectiveness and to guide the design of new serious games., This research was partially funded by the ACIS project (Reference Number INVESTUN/21/BU/0002) of the Consejería de Empleo of the Junta de Castilla y León (Spain) and the Ministry of Science, Innovation and Universities (FPU18/04688).

Machine learning algorithms for classification are employed in this study to generate different models that can predict the surface roughness of parts manufactured from polyvinyl butyral by means of Fused Deposition Modeling (FDM). Five input variables are defined (layer height, print speed, number of perimeters, wall angle, and extruder temperature), and 16 parts are 3D printed, each with three different surfaces (48 surfaces in total). The print values used to print each part were defined by a fractionated orthogonal experimental design. Using a perthometer, the average value of surface roughness, Ra, on each surface was obtained. From these experimental values, 40 models were trained and validated. The model with the best prediction results was the one generated by bagging and Multilayer Perceptron (BMLP), with a Kappa statistic of 0.9143. The input variables with the highest influence on the surface finish are the wall angle and the layer height.

The modelling of machining processes by means of machine-learning algorithms is still based on principles that are especially adapted to mechanical approaches, in which very few inputs are varied with little repetition of experimental conditions. These principles might not be ideal to achieve accurate machine-learning models and they are certainly not aligned with the practicalities of industrial machining in factories. In this research the effect of a new strategy to improve machine-learning model accuracy is studied: experimental repetition. Tool-life prediction in the face-turning operations of AISI 1045 steel discs, depending on different cooling systems and tool geometries, is selected as a case study. Both the side rake and the relief angles of HSS tools are optimized using the Brandsma facing test under dry, MQL, and flooding conditions. Different machine-learning algorithms, such as regression trees, kNNs, artificial neural networks, and ensembles (bagging and Random Forest) are tested. On the one hand, the results of the study showed that artificial neural networks of Radial Basis Functions presented the highest model accuracy (11.4 mm RMSE), but required a very sensitive and complex tuning process. On the other hand, they demonstrated that ensembles, especially Random Forest, provided models with accuracy in the same range, but with no tuning procedure (12.8 mm RMSE). Secondly, the effect of an increased dataset size, by means of experimental repetition, is evaluated and compared with traditional experimental modelling that used average values. The results showed that some machine-learning techniques, including both ensemble types, significantly improved their accuracy with this strategy, by up to 23%. The results therefore suggested that the use of raw experimental data, rather than their averaged values, can achieve machine-learning models of higher accuracy for tool-wear processes.

The acceptance of the machined surfaces not only depends on roughness parameters but also in the flatness deviation (Δfl). Hence, before reaching the threshold of flatness deviation caused by the wear of the face mill, the tool inserts need to be changed to avoid the expected product rejection. As current CNC machines have the facility to track, in real-time, the main drive power, the present study utilizes this facility to predict the flatness deviation—with proper consideration to the amount of wear of cutting tool insert’s edge. The prediction of deviation from flatness is evaluated as a regression and a classification problem, while different machine-learning techniques like Multilayer Perceptrons, Radial Basis Functions Networks, Decision Trees and Random Forest ensembles have been examined. Finally, Random Forest ensembles combined with Synthetic Minority Over-sampling Technique (SMOTE) balancing technique showed the highest performance when the flatness levels are discretized taking into account industrial requirements. The SMOTE balancing technique resulted in a very useful strategy to avoid the strong limitations that small experiment datasets produce in the accuracy of machine-learning models.

The incorporation of new technologies as training methods, such as virtual reality (VR), facilitates instruction when compared to traditional approaches, which have shown strong limitations in their ability to engage young students who have grown up in the smartphone culture of continuous entertainment. Moreover, not all educational centers or organizations are able to incorporate specialized labs or equipment for training and instruction. Using VR applications, it is possible to reproduce training programs with a high rate of similarity to real programs, filling the gap in traditional training. In addition, it reduces unnecessary investment and prevents economic losses, avoiding unnecessary damage to laboratory equipment. The contribution of this work focuses on the development of a VR-based teaching and training application for the condition-based maintenance of induction motors. The novelty of this research relies mainly on the use of natural interactions with the VR environment and the design’s optimization of the VR application in terms of the proposed teaching topics. The application is comprised of two training modules. The first module is focused on the main components of induction motors, the assembly of workbenches and familiarization with induction motor components. The second module employs motor current signature analysis (MCSA) to detect induction motor failures, such as broken rotor bars, misalignments, unbalances, and gradual wear on gear case teeth. Finally, the usability of this VR tool has been validated with both graduate and undergraduate students, assuring the suitability of this tool for: (1) learning basic knowledge and (2) training in practical skills related to the condition-based maintenance of induction motors., This research has been partially supported by Banco Santander under the scholarship program Santander Iberoamérica Research 2019/20. This investigation was partially supported by the ACIS project (Reference Number INVESTUN/21/BU/0002) of the Consejeria de Empleo of the Junta de Castilla y León (Spain).

Immersive virtual reality (VR) environments create a very strong sense of presence and immersion. Nowadays, especially when student isolation and online autonomous learning is required, such sensations can provide higher satisfaction and learning rates than conventional teaching. However, up until the present, learning outcomes with VR tools have yet to prove their advantageous aspects over conventional teaching. The project presents a VR serious game for teaching concepts associated with computer hardware assembly. These concepts are often included in any undergraduate’s introduction to Computer Science. The learning outcomes are evaluated using a pre-test of previous knowledge, a satisfaction/usability test, and a post-test on knowledge acquisition, structured with questions on different knowledge areas. The results of the VR serious game are compared with another two learning methodologies adapted to online learning: (1) an online conventional lecture; and (2) playing the same serious game on a desktop PC. An extensive sample of students (n = 77) was formed for this purpose. The results showed the strong potential of VR serious games to improve student well-being during spells of confinement, due to higher learning satisfaction. Besides, ease of usability and the use of in-game tutorials are directly related with game-user satisfaction and performance. The main novelty of this research is related to academic performance. Although a very limited effect was noted for learning theoretical knowledge with the VR application in comparison with the other methodologies, this effect was significantly improved through visual knowledge, understanding and making connections between different concepts. It can therefore be concluded that the proposed VR serious game has the potential to increase student learning and therefore student satisfaction, by imparting a deeper understanding of the subject matter to students., GruaRV project (Reference Number INVESTUN/18/BU/0002) of the Consejeria de Empleo of the Junta de Castilla y León (Spain), the Erasmus+ RISKREAL Project (Reference Number 2020-1-ES01-KA204-081847) of the European Commission and the SMART-EASY project (Reference Number IDI-20191008) funded by the Centro para el Desarrollo Tecnológico e Industrial (CDTI)., Publicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCLE

Artículo de revisión, Depression and anxiety in children and adolescents are major health problems worldwide. In recent years, serious games research has advanced in the development of tools to address these mental health conditions. However, there has not been an extensive analysis of these games, their tendencies, and capacities. This review aims to gather the most current serious games, published from 2015 to 2020, with a new approach focusing on their applications: awareness, prevention, detection, and therapy. The purpose is also to analyze the implementation, development, and evaluation of these tools to obtain trends, strengths, and weaknesses for future research lines., This work was partially supported by grants INVESTUN/18/0002 and FPU18/04688 from the GruaRV project of the Consejería de Empleo of the Junta de Castilla y León (Spain) and the Ministry of Science, Innovation, and Universities.

Critical situations such as the current pandemic produced by the Sars-Cov-2 (COVID-19) are leading to the incorporation of new technologies, such as virtual reality (VR), as a part of the teaching and training methods that facilitates the teaching task in contrast with the traditional learning methods that have been used in the academia. In this regard, in this work is proposed the design and development of a VR-based tool that is applied in the teaching and training of condition-based maintenance strategies in induction motors. The design of this tool is performed under Oculus Quest and includes the development of the 3D virtual environments, the creation of the VR learning experience, and finally the necessary adjustments for the VR application to render smoothly on standalone devices. The usability of this experience tool has been experimentally validated with different students by giving to the students practical knowledge but also a learning experience.

The merger of game-based approaches and Virtual Reality (VR) environments that can enhance learning and training methodologies have a very promising future, reinforced by the widespread market-availability of affordable software and hardware tools for VR-environments. Rather than passive observers, users engage in those learning environments as active participants, permitting the development of exploration-based learning paradigms. There are separate reviews of VR technologies and serious games for educational and training purposes with a focus on only one knowledge area. However, this review covers 135 proposals for serious games in immersive VR-environments that are combinations of both VR and serious games and that offer end-user validation. First, an analysis of the forum, nationality, and date of publication of the articles is conducted. Then, the application domains, the target audience, the design of the game and its technological implementation, the performance evaluation procedure, and the results are analyzed. The aim here is to identify the factual standards of the proposed solutions and the differences between training and learning applications. Finally, the study lays the basis for future research lines that will develop serious games in immersive VR-environments, providing recommendations for the improvement of these tools and their successful application for the enhancement of both learning and training tasks.

Two teaching methodologies are presented and compared in this study: on the one hand, semi-guided tours in immersive virtual reality and, on the other, viewing video renderings of 3D environments. The two techniques are contrasted through 3D modeling of a fifteenth-century Spanish town called Briviesca, in an immersive environment, viewed with Oculus Rift. The suitability of virtual reality for teaching is assessed through questions on historical knowledge and urban layout. The understanding of the undergraduate students is evaluated, through questionnaires, after the viewing sessions. The responses of the students underline the effectiveness of the two methodologies: Video screenings received higher scores for historical ideas and the virtual tour was the most effective method at conveying knowledge learnt while viewing. Additionally, two user movements for controlling the virtual reality environment were tested: (1) gamepad locomotion and (2) roomscale movements combined with teleporting. The clear advantage of the second option was the total lack of motion sickness effects. However, the natural tendency using teleporting was to move very quickly through the city areas with no singular buildings and to spend more time in front of these types of buildings. They therefore missed visual information related to the first areas while retaining more information related to those buildings. Finally, the spatial location of singular buildings was clearly better acquired with the virtual tour.

BACKGROUND Depression and anxiety in children and adolescents are major health problems worldwide. In recent years, serious games research has advanced in the development of tools to address these mental health conditions. However, there has not been an extensive analysis of these games, their tendencies, and capacities. OBJECTIVE This review aims to gather the most current serious games, published from 2015 to 2020, with a new approach focusing on their applications: awareness, prevention, detection, and therapy. The purpose is also to analyze the implementation, development, and evaluation of these tools to obtain trends, strengths, and weaknesses for future research lines. METHODS The identification of the serious games through a literature search was conducted on the databases PubMed, Scopus, Wiley, Taylor and Francis, Springer, PsycINFO, PsycArticles, Web of Science, and Science Direct. The identified records were screened to include only the manuscripts meeting these criteria: a serious game for PC, smartphone, or virtual reality; developed by research teams; targeting only depression or anxiety or both; aiming specifically at children or adolescents. RESULTS A total of 34 studies have been found that developed serious games for PC, smartphone, and virtual reality devices and tested them in children and adolescents. Most of the games address both conditions and are applied in prevention and therapy. Nevertheless, there is a trend that anxiety is targeted more in childhood and depression targeted more in adolescence. Regarding design, the game genres arcade minigames, adventure worlds, and social simulations are used, in this order. For implementation, these serious games usually require sessions of 1 hour and are most often played using a PC. Moreover, the common evaluation tools are normalized questionnaires that measure acquisition of skills or reduction of symptoms. Most studies collect and compare these data before and after the participants play. CONCLUSIONS The results show that more awareness and detection games are needed, as well as games that mix the awareness, prevention, detection, and therapy applications. In addition, games for depression and anxiety should equally target all age ranges. For future research, the development and evaluation of serious games should be standardized, so the implementation of serious games as tools would advance. The games should always offer support while playing, in addition to collecting data on participant behavior during the game to better analyze their learning. Furthermore, there is an open line regarding the use of virtual reality for these games due to the capabilities offered by this technology.

BACKGROUND The design of Virtual Reality Serious Games (VR-SG) is a subject still developing. One of its open developments is the definition of metrics to evaluate the fun and learning result. In this way, weaknesses and strengths in the design of serious games can be found for future works in this research field. OBJECTIVE This paper aims to create a metric that can be used to rate the gameplay of VR-SG. This metric’s novelty allows to evaluate the different fun and learning features and give them a quantitative rating. A study case shows the capability of implementing this evaluation to identify strengths and weaknesses of VR-SGs. METHODS The new VR-SG metric is developed on the basis of the Mechanics, Dynamics and Aesthetic (MDA) framework but including User Experience (UX) elements and adapting them to VR-SG. This metric includes 1) UX aspects: VR-headsets, training tutorials and interactive adaptions to avoid VR inconveniences; and 2) MDA aspects: exclusive VR audiovisual elements and its aesthetics interactions. RESULTS The selected indie serious game is Hellblade, developed to raise awareness about the difficulties of people suffering from psychosis with two versions: one for 2D-screens and the other for VR devices. The comparison of metric´s scores for both versions shows: 1) some VR dynamics increase the gameplay impact and therefore, the educational capacity; and 2) flaws in game design where the scores drop down. Some of these flaws are: reduced number of levels, missions and items, lack of a tutorial to enhance usability and lack of strategies and rewards in the long-term to increase motivation. CONCLUSIONS This metric allows to identify the elements of the gameplay and UX that are necessary to learn in VR experiences. The study case shows this research is useful to evaluate the educational utility of VR-SG. Further works will analyze VR applications to synthetize every game element influencing its intrinsic sensations. CLINICALTRIAL The trials have not been registered, as testing for this metric has not involved people with mental conditions or addressed other medical applications. Hellblade is a commercial video game that anyone can purchase and play. The trials have been carried out to obtain results on the gaming experience of different people in relation to the educational purpose of raising awareness of psychosis.

Serious Games for Virtual Reality (SG-VR) is still a new subject that needs to be explored. Achieving the optimal fun and learning results depends on the application of the most suitable metrics. Virtual Reality environments offer great capabilities but at the same time make difficult to record User Experience (UX) to improve it. Moreover, the continuous evolution of Virtual Reality technologies and video game industry tendencies constantly change these metrics. This paper studies the Mechanics, Dynamics and Aesthetic (MDA) framework and User Experience metrics to develop new ones for SG-VR. These new parameters are focused on the intrinsic motivations the players need so they engage with the game. However, the development team budget must be taken into account, since it limits items and interactions but still have to aim to the learning goals. New VR metrics will be 1) UX features: chosen VR headsets, training interactions tutorials to learn control and interactive adaptions to avoid VR inconveniences; and 2) MDA features: exclusive VR aesthetical elements and its interactions.

The fast development and progressive price reduction of Virtual Reality (VR) technologies promotes their implementation in areas beyond gaming. This makes it increasingly applied in other areas such as the development of educational or training applications. However, the development of these applications from scratch can involve a very high time and research investment. To solve this limitation, this paper presents an immersive virtual reality framework that simplifies the development of applications and allows researchers to focus on the design of the educational or training experience, once the framework already solve the main technical issues of the VR environment’s development. This Virtual Reality framework is based in Unreal Engine, therefore compatible with most virtual reality helmets on the market. The framework includes tools for all the required tasks to create virtual reality experiences: movement of the player, interactions with the scenario and objects and the creation of scene objectives among others. To validate its suitability, two cultural heritage environments have been used, in which it has been applied to create two educational experiences.

The main productivity constraints of milling operations are self-induced vibrations, especially regenerative chatter vibrations. Two key parameters are linked to these vibrations: the depth of cut achievable without vibrations and the chatter frequency. Both parameters are linked to the dynamics of machine component excitation and the milling operation parameters. Their identification in any cutting direction in milling machine operations requires complex analytical models and mechatronic simulations, usually only applied to identify the worst cutting conditions in operating machines. This work proposes the use of machine learning techniques with no need to calculate the two above-mentioned parameters by means of a 3-step strategy. The strategy combines: 1) experimental frequency responses collected at the tool center point; 2) analytical calculations of both parameters; and, 3) different machine learning techniques. The results of these calculations can then be used to predict chatter under different combinations of milling directions and machine positions. This strategy is validated with real experiments on a bridge milling machine performing concordance roughing operations on AISI 1045 steel with a 125 mm diameter mill fitted with nine cutters at 45°, the results of which have confirmed the high variability of both parameters along the working volume. The following regression techniques are tested: artificial neural networks, regression trees and Random Forest. The results show that Random Forest ensembles provided the highest accuracy with a statistical advantage over the other machine learning models; they achieved a final accuracy of 0.95 mm for the critical depth and 7.3 Hz for the chatter frequency (RMSE) in the whole working volume and in all feed directions, applying a 10 × 10 cross validation scheme. These RMSE values are acceptable from the industrial point of view, taking into account that the critical depth of this range varies between 0.68 mm and 19.20 mm and the chatter frequency between 1.14 Hz and 65.25 Hz. Besides, Random Forest ensembles are more easily optimized than artificial neural networks (1 parameter configuration versus 210 MLPs). Additionally, tools that incorporate regression trees are interesting and highly accurate, providing immediately accessible and useful information in visual formats on critical machine performance for the design engineer., Hidrodamp Project (IDI-20110453) of the Centre for Industrial Technological Development (CDTI)

El desarrollo en los ultimos anos de distintas tecnologias englobadas en el paradigma Industria 4.0 abre la puerta a la monitorizacion intensiva de las maquinas herramienta. En este trabajo se presenta una plataforma de adquisicion y monitorizacion tanto 2D como 3D del funcionamiento de maquinas-herramienta que busca facilitar la toma de decisiones para la optimizacion de la produccion. Esta plataforma esta compuesta por: 1) un sistema de adquisicion de datos que procesa la informacion recopilada por el PLC y el CNC de la maquina y por cualquier otro sensor integrado en la misma, 2) un servidor remoto que guarda los datos recogidos y 3) un conjunto de interfaces 2D y 3D que permiten tanto calcular indicadores claves de rendimiento en tiempo real como analizar un proceso concreto de mecanizado en un entorno virtual 3D mediante Oculus Rift y Oculus Touch para detectar anomalias en el proceso de mecanizado. El funcionamiento de esta plataforma se ha validado en una fresadora de portico que realiza el mecanizado de una pieza de fundicion de grandes dimensiones. El resultado de este estudio muestra como se pueden detectar tres tipos de anomalias en el proceso de mecanizado y como el entorno inmersivo 3D facilita que el ingeniero de proceso detecte estas anomalias, en especial en el caso de ingenieros de proceso junior. Palabras clave: maquina herramienta, realidad virtual, indicadores claves de rendimiento, optimizacion del mecanizado

Currently, a key industrial challenge in friction processes is the prediction of surface roughness and loss of mass under different machining processes, such as Electro-Discharge Machining (EDM), and turning and grinding processes. Under industrial conditions, only the sliding distance is easily evaluated in friction processes, while the acquisition of other variables usually implies expensive costs for production centres, such as the integration of sensors in functioning machine-tools. Besides, appropriate datasets are usually very small, because the testing of different friction conditions is also expensive. These two restrictions, small datasets and very few inputs, make it very difficult to use Artificial Intelligence (AI) techniques to model the industrial problem. So, the use of the isotropy level of the surface structure is proposed, as another input that is easily evaluated prior to the friction process. In this example, the friction processes of a cubic sample of 102Cr6 (40 HRC) steel and a further element made of X210Cr12 (60 HRC) steel are considered. Different artificial intelligence techniques, such as artificial regression trees, multilayer perceptrons (MLPs), radial basis networks (RBFs), and Random Forest, were tested considering the isotropy level as either a nominal or a numeric attribute, to evaluate improvements in the accuracy of surface roughness and loss-of-mass predictions. The results obtained with real datasets showed that RBFs and MLPs provided the most accurate models for loss of mass and surface roughness prediction, respectively. MLPs have slightly higher surface prediction accuracy than Random Forest, although MLP models are very sensitive to the tuning of their parameters (a small mismatch between the learning rate and the momentum in the MLP will drastically reduce the accuracy of the model). In contrast, Random Forest has no parameter to be tuned and its prediction is almost as good as MLPs for surface roughness, so Random Forest will be more suitable for industrial use where no expert in AI model tuning is available. Moreover, the inclusion of the isotropy level in the dataset, especially as a numeric attribute, greatly improved the accuracy of the models, in some cases, by up to 52% for MLPs, and by a smaller proportion of 16% in the Random Forest models in terms of Root Mean Square Error. Finally, Random Forest ensembles only trained with low and very high isotropy level experimental datasets generated reliable models for medium levels of isotropy, thereby offering a solution to reduce the size of training datasets.

Form and friction drilling techniques are now promising alternatives in light and medium boilermaking that will very probably supersede conventional drilling techniques, as rapid and economic solutions for producing nutless bolted joints. Nonetheless, given the number of cutting parameters involved, optimization of the process requires calibration of the main input parameters in relation to the desired output values. Among these values, the gap between plates determines the service life of the joint. In this paper, a suitable smart manufacturing strategy for real industrial conditions is proposed, where it is necessary to identify the most accurate machine-learning technique to process experimental datasets of a small size. The strategy is first to generate a small-size dataset under real industrial conditions, then the gap is discretized taking into account the specific industrial needs of this quality indicator for each product. Finally, the different machine learning models are tested and fine-tuned to ascertain the most accurate model at the lowest cost. The strategy is validated with a 48 condition-dataset where only feed-rate and rotation speed are used as inputs and the gap as the output. The results on this dataset showed that the Adaboost ensembles provided the highest accuracy and were more easily optimized than artificial neural networks.

A two-step method is presented for the automatic prediction of tool life in turning operations. First, experimental data are collected for three cutting edges under the same constant processing conditions. In these experiments, the parameter of tool wear, VB, is measured with conventional methods and the same parameter is estimated using Neural Wear, a customized software package that combines flank wear image recognition and Artificial Neural Networks (ANNs). Second, an ANN model of tool life is trained with the data collected from the first two cutting edges and the subsequent model is evaluated on two different subsets for the third cutting edge: the first subset is obtained from the direct measurement of tool wear and the second is obtained from the Neural Wear software that estimates tool wear using edge images. Although the complete-automated solution, Neural Wear software for tool wear recognition plus the ANN model of tool life prediction, presented a slightly higher error than the direct measurements, it was within the same range and can meet all industrial requirements. These results confirm that the combination of image recognition software and ANN modelling could potentially be developed into a useful industrial tool for low-cost estimation of tool life in turning operations.

Experimental data sets that include tool settings, tool and machine-tool behavior, and surface roughness data for milling processes are usually of limited size, due mainly to the high costs of machining tests. This fact restricts the application of machine-learning techniques for surface roughness prediction in industrial settings. The primary objective of this work is to investigate the way data streams that are missing product features (i.e. unlabeled data streams) can contribute to the development of prediction models. The investigation is followed by a proposal for a semi-supervised approach to the development of roughness prediction models that can use partly unlabeled data to improve the accuracy of roughness prediction. Following this strategy, records collected during the milling process, which miss roughness measurements, but contain vibration data are used to increase the accuracy of the prediction models. The method proposed in this work is based on the selective use of such unlabelled instances, collected at tool settings that are not represented in the labeled data. This strategy, when applied properly, yields both extended training data sets and higher accuracy in the roughness prediction models that are derived from them. The scale of accuracy improvement and its statistical significance are shown in the study case of high-torque face milling of F114 steel. The semi-supervised approach proposed in this work has been used in combination with supervised k Nearest Neighbours and random forest techniques. Furthermore, the study of both continuous and discretized roughness prediction, showed higher gains in accuracy in the second.

Undoubtedly mobile devices are gaining more and more popularity. However, the breakthrough in mobile applications is yet to be followed by a breakthrough in manufacturing industry. The paper presents a new methodology for application development on the Android platform in MIT App Inventor bookmarks. The research method consists in an algorithm and application design. Also it was presented an example of an elaborate program SpeedCalc for the lathe spindle speed selection for the determined value of cutting speed in relation to the diameter of the work piece. The program can be used with a mobile phone or tablet.

Nowadays, face milling is one of the most widely used machining processes for the generation of flat surfaces. Following international standards, the quality of a machined surface is measured in terms of surface roughness, Ra, a parameter that will decrease with increased tool wear. So, cutting inserts of the milling tool have to be changed before a given surface quality threshold is exceeded. The use of artificial intelligence methods is suggested in this paper for real-time prediction of surface roughness deviations, depending on the main drive power, and taking tool wear, $$V_{B}$$ into account. This method ensures comprehensive use of the potential of modern CNC machines that are able to monitor the main drive power, N, in real-time. It can likewise estimate the three parameters -maximum tool wear, machining time, and cutting power- that are required to generate a given surface roughness, thereby making the most efficient use of the cutting tool. A series of artificial intelligence methods are tested: random forest (RF), standard Multilayer perceptrons (MLP), Regression Trees, and radial-based functions. Random forest was shown to have the highest model accuracy, followed by regression trees, displaying higher accuracy than the standard MLP and the radial-basis function. Moreover, RF techniques are easily tuned and generate visual information for direct use by the process engineer, such as the linear relationships between process parameters and roughness, and thresholds for avoiding rapid tool wear. All of this information can be directly extracted from the tree structure or by drawing 3D charts plotting two process inputs and the predicted roughness depending on workshop requirements.

Graphical abstract, Highlights • Dataset of plates lifetime were obtained by 3 casting methods and chemical composition. • A two-steps model for prediction of the full lifetime of plates of Hadfield steel was proposed. • The prediction model combines regression trees with multilayer perceptron (MLP) • MLP provides accurate weaŕs models considering the chemical composition. • Regression trees provide visual information about dataset structure to build MLP., Highly tensile manganese steel is in great demand owing to its high tensile strength under shock loads. All workpieces are produced through casting, because it is highly difficult to machine. The probabilistic aspects of its casting, its variable composition, and the different casting techniques must all be considered for the optimisation of its mechanical properties. A hybrid strategy is therefore proposed which combines decision trees and artificial neural networks (ANNs) for accurate and reliable prediction models for ore crushing plate lifetimes. The strategic blend of these two high-accuracy prediction models is used to generate simple decision trees which can reveal the main dataset features, thereby facilitating decision-making. Following a complexity analysis of a dataset with 450 different plates, the best model consisted of 9 different multilayer perceptrons, the inputs of which were only the Fe and Mn plate compositions. The model recorded a low root mean square error (RMSE) of only 0.0614 h for the lifetime of the plate: a very accurate result considering their varied lifetimes of between 746 and 6902 h in the dataset. Finally, the use of these models under real industrial conditions is presented in a heat map, namely a 2D representation of the main manufacturing process inputs with a colour scale which shows the predicted output, i.e. the expected lifetime of the manufactured plates. Thus, the hybrid strategy extracts core training dataset information in high-accuracy prediction models. This novel strategy merges the different capabilities of two families of machine-learning algorithms. It provides a high-accuracy industrial tool for the prediction of the full lifetime of highly tensile manganese steel plates. The results yielded a precision prediction of (RMSE of 0.061 h) for the full lifetime of (light, medium, and heavy) crusher plates manufactured with the three (experimental, classic, and highly efficient (new)) casting methods.

In face milling one of the most important parameters of the process quality is the roughness of the machined surface. In many articles, the influence of cutting regimes on the roughness and cutting forces of face milling is considered. However, during flat face milling with the milling width B lower than the cutter&rsquo, s diameter D, the influence of such an important parameter as the relative position of the face mill towards the workpiece and the milling kinematics (Up or Down milling) on the cutting force components and the roughness of the machined surface has not been sufficiently studied. At the same time, the values of the cutting force components can vary significantly depending on the relative position of the face mill towards the workpiece, and thus have a different effect on the power expended on the milling process. Having studied this influence, it is possible to formulate useful recommendations for a technologist who creates a technological process using face milling operations. It is possible to choose such a relative position of the face mill and workpiece that will provide the smallest value of the surface roughness obtained by face milling. This paper shows the influence of the relative position of the face mill towards the workpiece and milling kinematics on the components of the cutting forces, the acceleration of the machine spindle in the process of face milling (considering the rotation of the mill for a full revolution), and on the surface roughness obtained by face milling. Practical recommendations on the assignment of the relative position of the face mill towards the workpiece and the milling kinematics are given.

Being able to travel in public transportation autonomously is one daily task that doesn’t imply any special effort or learning process for most of the human population. But, for people with intellectual disabilities, it requires an extensive learning process where many possible situations should be considered before the person can perform successfully this task. Currently these people are taught by specialists following supervised learning methodologies in real environments. But, through the use of immersive virtual reality and interaction within a virtual world, they could be trained in a safe environment, increasing their self-confidence in a saver and quicker way. This paper describes the creation of a customized simulator that prepares subjects to use the bus as a means of transportation in a medium-size city. The design of the virtual reality environment and the interaction mechanics is thought to be highly customizable for each subject with a very realistic environment, thanks to the use of photogrammetric digitalization technologies. The environment has been developed in Unreal engine and uses the head mounted display Oculus rift, as well as Oculus touch for interaction with the different assets.

Industrial threading processes that use cutting taps are in high demand. However, industrial conditions differ markedly from laboratory conditions. In this study, a machine-learning solution is presented for the correct classification of threads, based on industrial requirements, to avoid expensive manual measurement of quality indicators. First, quality states are categorized. Second, process inputs are extracted from the torque signals including statistical parameters. Third, different machine-learning algorithms are tested: from base classifiers, such as decision trees and multilayer perceptrons, to complex ensembles of classifiers especially designed for imbalanced datasets, such as boosting and bagging decision-tree ensembles combined with SMOTE and under-sampling balancing techniques. Ensembles demonstrated the lowest sensitivity to window sizes, the highest accuracy for smaller window sizes, and the greatest learning ability with small datasets. Fourth, the combination of models with both high Recall and high Precision resulted in a reliable industrial tool, tested on an extensive experimental dataset., Projects TIN2015-67534-P (MINECO/FEDER, UE) of the Ministerio de Economía Competitividad of the Spanish Governmentand project BU085P17 (JCyL/FEDER, UE) of the Junta de Castilla y Le ́on, both co-financed through European-Union FEDER funds and project IT-2005/00201 of the INNOTEK Program of the Basque Government

The recent development of new laser machine tools for the manufacture of micro-scale metallic components has boosted demand in the field of medical applications. However, the optimization of this process encounters a major problem: a knowledge gap concerning the relation between the controllable parameters of these machine tools and the quality of the machined components. Our research proposes a two-step strategy to approach this problem for the manufacture of stents. First, a screening test identifies good and bad performance conditions for the laser process and generates useful information on cutting performance; then, a stent is manufactured under different cutting conditions and the most accurate machine learning technique to model this process is identified. This strategy is validated with the performance of experiments that vary pulse duration, laser power, and cutting speed, and measure two geometrical characteristics of the stent geometry. The results showed that linear Support Vector Machines can identify good and bad cutting conditions, while Random Forest ensembles of regression trees can predict with high accuracy the two characteristics of the stent geometry under study. Besides, this technique can extract useful information from the screening test that improves its final accuracy. In view of the small dataset size, an alternative based on the leave-one-out technique was used, instead of standard cross validation, so as to assure the generalization capability of the models.

In the highly competitive modern-day industrial landscape, characterized by globalization and resource scarcity, manufacturers are striving to improve economic and environmental performance. Innovation that enables self-adjustment, control and optimization of the energy consumption of individual machines continues. However, more research is needed if such systems are to be deployed successfully, especially considering the complex characteristics of the energy flows in the factory. In this paper we propose a novel approach to the coordination of information, processing and sensing systems for energy and resource efficient production systems. By leveraging on a recently-developed framework focusing on physical flows of energy, materials and waste we propose a solution based on specific energy efficiency KPIs and an online data acquisition/processing system, that enables real-time monitoring of the current status of the machining process and lagging assessment of system energy efficiency. The proposed solution allows the identification of abnormal energy consumption during the operational machine cycle, caused by incorrect part dimensioning or erroneous cutting conditions programmed by the process engineer, enabling identification of potential disruptions with different gravity levels, and delivery of meaningful alarms for the operator. Adaptive control of the machine cutting conditions or even trajectory re-programming is then possible, by correlating the energy-consumption data with other data, such as head temperature. Furthermore, by analysing the energy consumption of value and non value adding activities over complete production cycles (such as a shift or day), it is possible to monitor the progress of production systems toward achieving energy efficiency targets and to conduct root-cause analysis of inefficient energy usage for continuous improvement programs. We tested the proposed solution, modeling, index system ad online data acquisition/processing platform, through an industrial case study by deploying the developed hardware and software modules on a Nicolas Correa S.A. VERSA milling machine.

The selection of the right cutting tool in manufacturing process design is always an open question, especially when different tools are available on the market with similar characteristics, but marked differences in price, ranging from low-cost to high-performance cutting tools. The ultimate decision of the engineer will depend on previous experience with the life cycle of the tool and its performance, but without the support of a systematic knowledge base. This research presents a decision-making system based on soft-computing techniques. First, several experiments were carried out with four different cutting tools: two flat-milling low-cost tools without any surface treatment or coating and two high-performance, high-cost cutting tools in both cases with four cutting edges, similar geometrical features and diameters. Three different measures of tool wear are considered in the context of real workshop conditions: on-line power consumption, cutting length and volume of cut material. Finally, decision trees have been selected as the most suitable technique for building a decision-making system for two reasons: these trees show higher accuracy for the prediction of roughness in terms of tool wear and tool type. They also provide useful visual feedback on the information that is extracted from the real data, which can be directly used by the process engineer.

Machine-learning techniques frequently predict the results of machining processes, based on pre-determined cutting tool settings. By doing so, key parameters of a machined product can be predicted before production begins. Nevertheless, a prediction model cannot capture all the features of interest under real-life industrial conditions. Moreover, careful assessment of prediction credibility is necessary for accurate calibration; aspects that should be addressed through appropriate modeling and visualization techniques. A machine process test problem is proposed to analyze data-visualization techniques, in which a real data set is analyzed that describes deep-drilling under different cutting and cooling conditions. The main objective is the efficient fusion of visualization techniques with the knowledge of industrial engineers. Common modeling and visualization techniques were first surveyed, to contrast standard practice with our novel approach. A hybrid technique combining conditional inference trees with dimensionality reduction was then examined. The results show that a process engineer will be able to estimate overall model accuracy and to verify the extent to which accuracy depends on industrial process settings and the statistical significance of model predictions. Moreover, evaluation of the data set in terms of its sufficiency for modeling purposes will help assess the credibility of these decisions.

Industrial demand for models and simulation tools that can predict dimensional errors in manufacturing processes is vigorous. One example of these processes is ball-end finishing of inclined surfaces, which is a very complex task, due to the high number of variables that may influence dimensional errors during a cutting process and their different nature. This work firstly analyses the potential of semiempirical models to address the ball-end milling finishing, to conclude that these models are unable to process and to replicate the full range of milling strategies and slope combinations. Secondly, it goes on to analyse the possibilities of artificial neural networks as a means of overcoming this limitation. Two types of neural networks, multilayer perceptron (MLP) and radial basis functions (RBF), are tested. The results show that RBFs predict better than MLPs in all cases, achieving a precision of 1.83 μm in root mean squared error (RMSE) and a correlation coefficient of 0.897 with a 10 × 10 cross-validation scheme. Their training and tuning times are also 2.5 times shorter in all cases. Finally, the use of 3D figures, generated from the best RBF model, yields interesting industrial results in the field of process engineering.

The virtual visualization of historical objects opens the door to a variety of new teaching applications in the classroom. In this study, we present a flexible platform for the creation of semi-immersive 3D environments. First, we describe the software and hardware tools that generate the 3D models and the Virtual Reality Environments. We then present an optimized design methodology, adapted to the generation of light 3D models of sufficient visual quality for teaching purposes. The most suitable option for such purposes proved to be CAD tools coupled with extensive use of image textures on low-resolution 3D meshes. Finally, we report an ad-hoc teaching method to test the platform during a short teaching session on Cultural Heritage and Computer Graphics for high-school and undergraduate students. The evaluation of their experiences, based on post-session surveys, points to the effectiveness of this approach to communicate different types of knowledge and to stimulate student learning.

20th century industrial heritage, hardly a mainstream area of study, is rarely presented in exhibitions to the general public in Spain. Many abandoned industrial heritage sites are simply left to deteriorate with no attempt to learn from their past. In this paper, the construction and validation of three different Virtual Reality environments are reported. These Virtual Reality environments will be included in an exhibition on some of the first factories established in Burgos (Spain) in the 1960s. The environments were generated using different 3D head-mounted displays - Oculus Rift™ and card-boards, and 2D Displays running under different game engines such as Unreal Engine and Unity. Three environments were created: a 2D-virtual tour game in a small factory manufacturing steel sheets; a 3D-mobile virtual reality window to follow the expansion of a small car-component manufacturer into a leading TIER1 automotive sector company; and, a final general overview of the effect of the new companies on the development of the city using Oculus Rift displays. This research focuses on the advantages of each technology for a different objective and the analysis of the effort required to run it properly in a final application, concluding that the game engine Unreal Engine provides more photorealistic results in 3D-environments with high user-interaction capabilities, while Unity is better in 360o environments that show time evolution running on devices such as mobile phones, with low-computing power.

The continuous innovation of new affordable hardware and software over recent years is leading to a surfeit of Virtual Reality (VR) applications in the entertainment industry. However, the abundance of VR applications is unfortunately not matched by case studies and evaluation methods related to low-cost Virtual Reality experiences. A gap in the literature exists, where the utility of VR to display narrative stories could be studied, due to the sensations of amazement, astonishment, and excitement that it awakens. This research reports the steps taken to create a low-cost VR experience designed to transmit a single concept. The key aspects of the experience may be summarized as follows: a short duration, the selection of an evocative scenario limited to a single storyline, short animation of 3D-models selected to produce intense visual impacts with minimal modelling and animation and the collaborative participation of undergraduate students in the development of the VR environment. Members of the public were invited to trials of this VR experience portraying a Nativity scene over the 2016/17 Christmas Season in Burgos (Spain). The user experience was evaluated through surveys administered to numerous final-users immediately after the 3D-experience. The results showed very high levels of satisfaction, even though the 3D-experience for around half of the viewers was not their first one. In a few rare cases, sickness effects were reported. Viewers identified the movement of light as one of the most impressive aspects and considered the duration reasonable, with few or no suggestions for improvement.