## 1 Introduction
Under laboratory conditions, a very small number of inputs are varied from one experiment to the next, there is almost no experimental repetition, the experimental conditions are carefully selected, mainly by factorial or Taguchi experimental design, and all inputs and outputs are carefully measured, and validated before the next experiment is performed, as outlined by the most exemplary reviews [5,6]. However, as [PERSON] et al. demonstrated [7], under industrial conditions, most of the experiments refer to the same cutting conditions, a very broad range of parameters are at the same time varied, and the data present many empty values and values of limited confidence. Most of these features can be joined in one dataset property: artificially balanced datasets. Balance means that there is a similar proportion of all the tested conditions and the values of the outputs in the dataset. This condition is however far from the industrial reality, where most of the instances in the acquired dataset will show a normal behavior and very few will show an abnormal behavior, independent of the considered machining process or output that is to be predicted. The starting point of this study is completely unlike previous works: the aim is to design a reliable data-acquisition system and to connect it to a machining center in a workshop, acquiring data almost under blind conditions. The machine will be monitored over a sufficient period of time to gain a general overview of its operation, in this case 3 months. The machine has a commercial, non-open architecture CNC that will ensure a suitable IoT solution for all other machine-tools. Then, different machine-learning algorithms will be used to model continuous features, such as motor temperatures, and some discretized features, such as machine state and working mode. The novelty of this approach is not that it develops a breakthrough approach for data acquisition and IoT implementation, nor is it in the design of new machine-learning algorithms. It refers to both the design and the validation of a reliable IoT solution for its immediate implementation in real workshops. The solution is based on the most suitable machine-learning algorithms for each of the proposed industrial tasks (identification of temperature patterns and machine working states). This suitability will be proven with a real and extensive dataset, where different machining processes are mixed with no clear identification: milling, drilling, etc. Our solution, as previously outlined, is different to the previously presented proposals, because it is not built up from laboratory datasets and specifically designed machining tests (i.e., only face-milling tests at different cutting speeds), but from real datasets with many repetitions, different cutting conditions and processes, etc. Besides, no pre-processing techniques are applied to the dataset, to assure that the selected machine-learning algorithms will be ready to process real information later on without the intervention of a human expert. The existing bibliography, summarized in Section 2, proposes solutions for certain industrial problems (e.g., tool breakage or wear monitoring in drilling, milling or turning, surface quality or dimensional accuracy prediction in machined workpieces, etc.). However, in this case, the solution is open to extract any useful information, merely by changing the inputs and outputs of the trained model and without any previous classification of the cutting process that took place. This feature is assured through continuous and discretized outputs of industrial interest. Therefore, the novelty of this research, rather than a solution for a specific manufacturing task, proves a more generic solution and a suitable approach to extract useful information from real manufacturing data. The paper will be organized as follows. A brief state of the art of IoT platforms for machining workshops and some examples of the most common machine-learning algorithms used on those platforms will be included in Section 2.