This data descriptor introduces a data set from a CNC machining process which includes vibration and current data recorded for 14 cutting tools used from their initial state until failure. Cuboidal samples made of 42CrMo4 material were used and milled clockwise. The research setup (Haas VF-1 and Beckhoff PAC system), experimental procedures and data were described. The data set has been made publicly available for further research and development and contains all raw and aggregated data with metadata from 968 milling cycles. Data can be used for the classification of tool condition or fault prediction, which is widely used in intelligent prediction maintenance systems.

IoE components are becoming an integral part of our lives and support the operation of systems such as smart homes, smart cities, or Industry 4.0. The large number and variety of IoE components force the creation of flexible systems for data acquisition, processing, and analysis. The work presents a proposal for a new flexible architecture model and technology stack designed for the diagnostics and monitoring of industrial components and processes in an IoE device environment. The proposed solutions allow creating custom flexible systems for managing a distributed IoT environment, including the implementation of innovative mechanisms like, for example: predictive maintenance, anomaly detection, business intelligence, optimization of energy consumption, or supervision of the manufacturing process. In the present study, two detailed system architectures are proposed, and one of them was implemented. The developed system was tested in near-production conditions using a real IoT device infrastructure including industrial systems, drones, and sensor networks. The results showed that the proposed model of a central data-acquisition and -processing system allows the flexible integration of various IoE solutions and has a very high implementation potential wherever there is a need to integrate data from different sources and systems.

The article presents an attempt to identify an appropriate regression model for the estimation of cutting tool lifespan in the milling process based on the analysis of the R² parameters of these models. The work is based on our own experiments and the accumulated database (which we make available for further use). The study uses a Haas VF-1 milling machine equipped with vibration sensors and based on a Beckhoff PLC data collector. As the acquired sensor data are continuous, and in order to account for dependencies between them, regression models were used. Support Vector Regression (SVR), decision trees and neural networks were tested during the work. The results obtained show that the best prediction results with the lowest error values were obtained for two-dimensional neural networks using the LBFGS solver (93.9%). Very similar results were also obtained for SVR (93.4%). The research carried out is related to the realisation of intelligent manufacturing dedicated to Industry 4.0 in the field of monitoring production processes, planning service downtime and reducing the level of losses resulting from damage to materials, semi-finished products and tools.

Arc welding used at automated workstations in large-scale production systems requires continuous assessment of welded joints quality. There are known classical methods and diagnostic systems based on the observation of welding current or arc voltage, while along with the development of deep learning methods, the interest in diagnostics by the use of images is increasing. The article presents results of research conducted for the process of joining two stainless steel materials (AISI 304 and AISI 316L) of various thicknesses by means of a fillet weld, aimed at developing a method of diagnosing the welding process using a convolutional neural network. Infrared images recorded using two thermovision cameras mounted on a test stand were used to diagnose the process. EWM Tetrix 351 welding machine operating in TIG technology was used as an executive element. Welds were made at different currents and arc welding voltages, as well as at different welding speeds, which had a direct impact on its quality. The solution for binary classification of welded joints (correct or incorrect) with accuracy above 98% was achieved.

The article presents an extensive analysis of the literature related to the diagnosis of the extrusion process and proposes a new, unique method. This method is based on the observation of the punch displacement signal in relation to the die, and then approximation of this signal using a polynomial. It is difficult to find in the literature even an attempt to solve the problem of diagnosing the extrusion process by means of a simple distance measurement. The dominant feature is the use of strain gauges, force sensors or even accelerometers. However, the authors managed to use the displacement signal, and it was considered a key element of the method presented in the article. The aim of the authors was to propose an effective method, simple to implement and not requiring high computing power, with the possibility of acting and making decisions in real time. At the input of the classifier, authors provided the determined polynomial coefficients and the SSE (Sum of Squared Errors) value. Based on the SSE values only, the decision tree algorithm performed anomaly detection with an accuracy of 98.36%. With regard to the duration of the experiment (single extrusion process), the decision was made after 0.44 s, which is on average 26.7% of the extrusion experiment duration. The article describes in detail the method and the results achieved.