## 1 Introduction  
Over the past 10 years, different technologies have boosted data-acquisition, communications, and processing capabilities. This strong development has led to the inauguration of a new concept: the Internet of Things (IoT). Although this concept might have direct applications in the daily life of the public, its successful implementation in industrial environments seems to be a more complex issue, as recent reviews have outlined [1]. This is the case of machining workshops, where many factors limit the range of IoT solutions. First, the integration of new sensors in existing machines is not easy, as durable machine-tools are usually designed for a long life and most existing machines were built before the development of the IoT or the Industry 4.0 paradigms [2]. Therefore, communication capabilities and integrated sensors built into machine-tools are very limited. In those cases, the only way to extract information from them would be through the machine's CNC (Computer Numerical Control). However, the CNC is not often available, given that its primary function is to control the machine-tool. Therefore, the most common solution is to access the PLC (Programmable Logic Controller) of the machine. Reading the PLC parameters is one way of extracting many parametric values from the CNC. This solution has been widely used during the last decade; i.e., for developing adaptive remote controllers for milling machines through an internet connection [3]. The first element for suitable IoT solutions in small workshops should therefore be a Data Acquisition Platform (DAP) connected to the PLC of the machine. The first DAP for manufacturing tasks was demonstrated over 20 years ago [4] for tool condition monitoring. However, the opportunity of setting up a real implementation was never demonstrated, due mainly to the very limited access to commercial CNCs at that time. To overcome this limitation, open architecture CNCs, once very rare in industrial workshops, were used in most studies over the past twenty years. In very recent years, some studies have described the new communication capabilities incorporated in commercial CNCs. Having established reliable solutions for data communication, the research focus moved on towards the definition of the best Key Performance Indicators extracted from the IoT platforms for manufacturing optimization. However, the data-acquisition stage is, however, not the only challenge for machine-workshop IoT solutions. Data processing and analysis are also subject to very restricted conditions and data features. As will be explained, the analysis of workshop machining processes will often have to contend with incomplete and unbalanced datasets. Besides, the data will have too many inputs, lessening its reliability. It will therefore be necessary to reduce the number of inputs and to eliminate repeated instances without losing information. Machine-learning techniques have many capabilities that are especially suitable to overcome these limitations. First, they generalize models to new conditions, thereby reducing the number of expensive experimental tests to be performed. Second, machine-learning techniques can extract useful information for unbalanced datasets. Third, machine-learning techniques reduce the number of features without losing information. Fourth, machine-learning techniques are able to complete missing attributes, due to sensor malfunctions and data-transmission errors. Nevertheless, the studies on machine-learning algorithm applications to predict machining-process performance have been demonstrated in laboratory datasets. Datasets generated under laboratory conditions have some extremely different features to those generated in real workshops.