There is an increasing trend in the industry of knowing in real-time the condition of their assets. In particular, tool wear is a critical aspect, which requires real-time monitoring to reduce costs and scrap in machining processes. Traditionally, for the purpose of predicting tool wear conditions in machining, mathematical models have been developed to extract the information from the signal of sensors attached to the machines. To reduce the complexity of developing physical models, where an in-depth knowledge of the system being modelled is required, the current trend is to use machine-learning (ML) models based on data from the tool wear. The acoustic emission (AE) technique has been widely used to capture data from and understand the real-time condition of industrial assets such as cutting tools. However, AE signal interpretation and processing is rather complex. One of the most common features extracted from AE signals to predict the tool wear is the counts parameter, defined as the number of times that the amplitude of the signal exceeds a predefined threshold. A recurrent problem of this feature is to define the adequate threshold to obtain consistent wear prediction. Additionally, AE signal bandwidth is rather wide, and the selection of the optimum frequencies band for feature extraction has been pointed out as critical and complex by many authors. To overcome these problems, this paper proposes a methodology that applies multi-threshold count feature extraction at multiresolution level using wavelet packet transform, which extracts a redundant and non-optimal feature map from the AE signal. Next, recursive feature elimination is performed to reduce and optimize the vast number of predicting features generated in the previous step, and random forests regression provides the estimated tool wear. The methodology presented was tested using data captured when turning 19NiMoCr6 steel under pre-established cutting conditions. The results obtained were compared with several ML algorithms such as k-nearest neighbors, support vector machines, artificial neural networks and decision trees. Experimental results show that the proposed method can reduce the predicted root mean squared error by 36.53%.

Purpose: The purpose of this paper is twofold: to present gamified mobile experiences as valid tools for DMOs to enrich the experience of tourists, and to present the benefits provided to DMOs by analytics tools integrated on gamified mobile experiences.Design/methodology/approach: Staff from three DMOs have generated a gamified mobile experience using a custom authoring tool designed and developed to fulfil their requirements. This gamified experience has targeted families with children visiting Basque Country during off-peak season. The experience has been validated over a period of seven weeks within a pilot project promoted by the local tourist information offices of the DMOs. Data directly provided by tourists and data gathered from analytic tools integrated on the gamified mobile experience have been analysed to fulfil the research objectives presented on the paper.Findings: Both DMOs and tourists can benefit from gamified mobile experiences. The integration of analytics tools to gain insights into the behaviour of tourists can be a relevant information source for DMOs.Research limitations/implications: The pilot project has targeted a niche tourism market, families with children visiting Basque Country, and has been running during off-peak season. Further studies focusing on other tourist types and different tourism season and destination types will be required to strengthen the validation of the research objectives presented on this paper.Practical implications: The paper promotes both the development of gamified mobile experiences and the inclusion of analytics tools for DMOs to obtain relevant information about tourists and the mobile experiences.Originality/value: A gamified mobile experience is generated by DMOs, validated on the basis of experience of real tourists. The analytics tools inside the gamified mobile experience provide DMOs with relevant information.

Nowadays, new challenges around increasing production quality and productivity, and decreasing energy consumption, are growing in the manufacturing industry. In order to tackle these challenges, it is of vital importance to monitor the health of critical components. In the machine tool sector, one of the main aspects is to monitor the wear of the cutting tools, as it affects directly to the fulfillment of tolerances, production of scrap, energy consumption, etc. Besides, the prediction of the remaining useful life (RUL) of the cutting tools, which is related to their wear level, is gaining more importance in the field of predictive maintenance, being that prediction is a crucial point for an improvement of the quality of the cutting process. Unlike monitoring the current health of the cutting tools in real time, as tool wear diagnosis does, RUL prediction allows to know when the tool will end its useful life. This is a key factor since it allows optimizing the planning of maintenance strategies. Moreover, a substantial number of signals can be captured from machine tools, but not all of them perform as optimum predictors for tool RUL. Thus, this paper focuses on RUL and has two main objectives. First, to evaluate the optimum signals for RUL prediction, a substantial number of them were captured in a turning process and investigated by using recursive feature elimination (RFE). Second, the use of bidirectional recurrent neural networks (BRNN) as regressive models to predict the RUL of cutting tools in machining operations using the investigated optimum signals is investigated. The results are compared to traditional machine learning (ML) models and convolutional neural networks (CNN). The results show that among all the signals captured, the root mean squared (RMS) parameter of the forward force ( $${F}_{y}$$F y ) is the optimum for RUL prediction. As well, the bidirectional long-short term memory (BiLSTM) and bidirectional gated recurrent units (BiGRU), which are two types of BRNN, along with the RMS of $${F}_{y}$$F y signal, achieved the lowest root mean squared error (RMSE) for tool RUL, being also computationally the most demanding ones.