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The purpose of the work was to investigate the impact of the severe plastic deformation (SPD) realized by hydrostatic extrusion (HE) process of AA5083 aluminium alloy on improving its machinability by turning. The article presents the results of measurements of cutting force components and roughness parameters at turning of the ultrafine grained (UFG) AA5083 aluminium alloy obtained in the process of severe plastic deformation HE. An analysis of the deformation process was carried out, covering the three-pass cold HE with cumulative true strain, e = 2.95. As a result of the deformation, the AA5083 alloy microstructure was strongly defective and significantly refined, which resulted in a significant increase in mechanical properties, UTS strength by 35 %, YS yield strength by 135 %, and hardness by 50 %. The average grain size was 150 nm. AA5083 alloy in the initial state and after HE was subjected to cutting analysis with selected parameters: constant depth of cut ap =0.5 mm, variable cutting speed Vc from 20 to 250 m/min and variable feed f from 0.08 to 0.17 mm/rev. After HE the material was characterized by much better machinability compared to the material at the initial state manifested by lower cutting force components Fc, Ff and Fp, especially in the low cutting speed zone 20–50 m/min, lower by 10, 70 and 5%, respectively, more than twice lower surface roughness Ra at lower cutting speeds, and significantly lower wear of the cutting tool by minimizing the effect of the workpiece adhesion to the cutter's rake surface.

There are many items in the literature indicating that certain signal features (SFs) of cutting forces, vibrations or acoustic emission are useful for the diagnosis of tool wear in certain single experiments. There is no answer to whether these SFs are universal. The novelty of this article is an attempt to answer these questions and propose a large set of SFs related to tool wear, but without including superfluous SFs. The analysis of the usefulness of the signal properties for the state of the cutting tool in turning was carried out on a large experiment. A number of various SFs obtained for various signal analysis methods were selected for the study. It is found that no SF is always related to the tool wear, so we define many different signal characteristics that can be related to the tool wear (basic set) and automatically select those associated with it in a given machining case. To this end, the relationship between the measures and the wear of the tool was analyzed. Interrelated measures were excluded from it. The obtained results can be used to build a new generation of more effective tool wear diagnostics systems. One of the goals of the tool wear diagnosis system is to save the energy used. The results can also enable the refinement of existing algorithms that predict the energy consumption of a machine.

In this paper, we describe the needs and specific requirements of the aerospace industry in the field of metal machining, specifically, the concept of an edge-computing-based production supervision system for the aerospace industry using a tool and cutting process condition monitoring system. The new concept was developed based on experience gained during the implementation of research projects in Poland’s Aviation Valley at aerospace plants such as Pratt &amp, Whitney and Lockheed Martin. Commercial tool condition monitoring (TCM) and production monitoring systems do not effectively meet the requirements and specificity of the aerospace industry. The main objective of the system is real-time diagnostics and sharing of data, knowledge, and system configurations among technologists, line bosses, machine tool operators, and quality control. The concept presented in this paper is a special tool condition monitoring system comprising a three-stage (natural wear, accelerated wear, and catastrophic tool failure) set of diagnostic algorithms designed for short-run machining and aimed at protecting the workpiece from damage by a damaged or worn tool.

There are many algorithms for the identification of gradual tool wear (GTW) which usually counts in minutes and the detection of catastrophic tool failure (CTF) which counts in milliseconds. However, the tool may lose its cutting ability by accelerated tool wear (ATW), which may last several seconds and cannot be sensed either by GTW or CTF detection algorithms. The paper presents an innovative algorithm for early detection of ATW and CTF. It consists in comparing the waveforms of the cutting force sensor signal in hierarchical time windows. The compared waveforms are independent of the absolute value of the signal. This allows the detection of ATWs of different intensity and duration. Tests proved that successful detection of ATW allows for prevention of CTF.

Presented is a comparison of different methods of estimating tool wear – obtained for group of RBF neural networks, hierarchical methods and the standard time counting. The analysis of the signals from the machining process carried out for three different experiments, clearly demonstrating the effect of presented methods. The results obtained for group of RBF neural networks are similar to results obtained for hierarchical methods.

One of the biggest challenges facing the factory of the future today is to reduce the time-to-market access and increase through the improvement of competitiveness and efficiency. In order to achieve this target, data analytics in Industrial Cyber-Physical System becomes a feasible option. In this paper, a visual analytics framework for condition monitoring of the machine tool is presented with the aim to manage events and alarms at factory level. The framework is assessed in a particular use case that consists in a multi-threaded cloud-based solution for the global analysis of the behaviour of variables acquired from PLC, CNC and robot manipulator. A human-machine interface is also designed for the real-time visualization of the key performance indicators according to the user’s criteria. This tool implemented is a great solution for condition monitoring and decision-making process based on data analytics from simple statistics to complex machine learning methods. The results achieved are part of the vision and implementation of the industrial test bed of “Industry and Society 5.0” platform.

Currently, the most important challenge in any assessment of state-of-the-art sensor technology and its reliability is to achieve road traffic safety targets. The research reported in this paper is focused on the design of a procedure for evaluating the reliability of Internet-of-Things (IoT) sensors and the use of a Cyber-Physical System (CPS) for the implementation of that evaluation procedure to gauge reliability. An important requirement for the generation of real critical situations under safety conditions is the capability of managing a co-simulation environment, in which both real and virtual data sensory information can be processed. An IoT case study that consists of a LiDAR-based collaborative map is then proposed, in which both real and virtual computing nodes with their corresponding sensors exchange information. Specifically, the sensor chosen for this study is a Ibeo Lux 4-layer LiDAR sensor with IoT added capabilities. Implementation is through an artificial-intelligence-based modeling library for sensor data-prediction error, at a local level, and a self-learning-based decision-making model supported on a Q-learning method, at a global level. Its aim is to determine the best model behavior and to trigger the updating procedure, if required. Finally, an experimental evaluation of this framework is also performed using simulated and real data

Nowadays, reliability of sensors is one of the most important challenges for widespread application of Internet-of-things data in key emerging fields such as the automotive and manufacturing sectors. This paper presents a brief review of the main research and innovation actions at the European level, as well as some on-going research related to sensor reliability in cyber-physical systems (CPS). The research reported in this paper is also focused on the design of a procedure for evaluating the reliability of Internet-of-Things sensors in a cyber-physical system. The results of a case study of sensor reliability assessment in an autonomous driving scenario for the automotive sector are also shown. A co-simulation framework is designed in order to enable real-time interaction between virtual and real sensors. The case study consists of an IoT LiDAR-based collaborative map in order to assess the CPS-based co-simulation framework. Specifically, the sensor chosen is the Ibeo Lux 4-layer LiDAR sensor with IoT added capabilities. The modeling library for predicting error with machine learning methods is implemented at a local level, and a self-learning-procedure for decision-making based on Q-learning runs at a global level. The study supporting the experimental evaluation of the co-simulation framework is presented using simulated and real data. The results demonstrate the effectiveness of the proposed method for increasing sensor reliability in cyber-physical systems using Internet-of-Things data., This work was partially supported by the project Power2Power: Providing next-generation silicon-based power solutions in transport and machinery for significant decarbonisation in the next decade, funded by the Electronic Component Systems for European Leadership (ECSEL-JU) Joint Undertaking and the Ministry of Science, Innovation and Universities (MICINN), under grant agreement No 826417. In addition, this work was also funded by the Spanish Ministry of Science, Innovation and Universities through the project COGDRIVE (DPI2017-86915-C3-1-R). Preparation of this publication was also partially co-financed by the Polish National Agency for Academic Exchange (NAWA) through the project: “Industry 4.0 in Production and Aeronautical Engineering (IPAE)”.

The paper presents some aspects of turning of difficult to machine materials with self-propelled rotary tools (SPRT). The main criterion of the rotary tools performance was the attainable surface quality, waviness and adhered chips. The dependence of the insert rotational speed on cutting parameters was also investigated. Original method of measurement of this speed was developed. Obtained results showed, that the speed is lower than theoretical, reported in literature. Stiffness and run out of the tools were measured. Cutting force measurements revealed substantial run-out of the rotating insert. Some important drawbacks and limitations of SPRT application were revealed.

This paper presents a tool wear monitoring strategy based on a large number of signal features in the rough turning of Inconel 625. Signal features (SFs) were extracted from time domain signals as well as from frequency domain transforms and their wavelet coefficients (time–frequency domain). All of them were automatically evaluated regarding their relevancy for tool wear monitoring based on a determination coefficient between the feature and its low-pass-filtered course as well as the repeatability. The selected SFs were used for tool wear estimation. The accuracy of this estimation was then used to evaluate the sensor and signal usability.

Nickel-based superalloys are widely used in the aircraft industry since they are exceptionally thermal resistant, retaining their mechanical properties at temperatures of up to 700 °C. On the other hand, since they are very difficult to machine, tool life is typically short and can finish abruptly. As catastrophic tool failure can destroy an expensive workpiece, automatic tool condition monitoring (TCM) has become particularly critical. This paper presents an application of the wavelet packet transform (WPT) for extracting useful TCM features from the cutting forces and acoustic emission (AE) signals during rough turning of Inconel 625. New, improved methods of signal feature (SF) relevancy evaluation were proposed based on determination and correlation coefficients. Out of several SFs calculated from bandpass signals, the most useful for TCM were automatically selected. The selected features were used for tool condition monitoring.

This paper draws on the activities of the CIRP Collaborative Work on “Round Robin on Chip Form Monitoring” carried out within the Scientific-Technical Committee Cutting (STC-C). This collaborative work involved the following main round robin activities: (a) generation, detection, storage and exchange of cutting force sensor signals obtained at different Laboratories during sensor-based monitoring of machining processes with variable cutting conditions yielding diverse chip forms, and (b) cutting force signal (CFS) characterization and feature extraction through advanced processing methodologies, both aimed at comparing chip form monitoring results achieved on the basis of innovative analysis paradigms.

Publisher Summary This chapter discusses the activities of a joint research project work carried out by two laboratories at the Warsaw University of Technology, Poland, and the University of Naples Federico II, Italy. The main activities of the joint research work comprised: (a) generation, detection, and storage of cutting force sensor signals obtained during sensor-based monitoring of machining processes with variable cutting conditions generating different chip forms and (b) cutting force signal (CFS) characterization and feature extraction through advanced processing methodologies, aimed at comparing chip form monitoring results achieved on the basis of innovative signal analysis and processing.