Anna Zawada-Tomkiewicz

The objective of this paper is to structure the role of the human in metal machining by organising operator participation into three functional roles and analysing their evolution across increasing levels of automation. Using drilling operations as a representative case study, the research integrates the Human-centric Manufacturing Model with the ISA-95 architecture to compare human involvement in classical, automated and autonomous production environments. The results indicate a systematic shift of human contribution from direct physical execution toward supervisory, cognitive and organisational functions. Advances in machine learning, digital twins and multi-sensor monitoring - together with increasing material complexity such as composite stacks and additively manufactured components - transform machining into a data-driven process requiring human validation and interpretation rather than manual intervention. Consequently, boundary physical roles diminish, while cognitive-augmented and analytical-organisational roles become central to planning, monitoring and governance of autonomous systems. The findings show that increasing autonomy does not eliminate the human from manufacturing but redefines the operator as a supervisor, interpreter and orchestrator of cyber-physical production systems, supporting safety, reliability and continuous improvement in Industry 5.0 environments.

The article addresses the challenge of reducing machining errors under tight tolerances, which can negatively affect workpiece quality. It highlights the need for modelling and compensating individual error types, particularly those caused by tool wear. Traditionally, tool wear compensation relies on experimentally determined absolute wear values, but nonlinearity in wear introduces discrepancies between modelled and actual machining processes. To address this, the article introduces a novel tool wear model integrated into an Intelligent Tool Change System. The model represents changes in tool edge reduction over time, allowing for tool position correction relative to the workpiece and signalling alarm states. It incorporates a first-order inertial adaptive model, enabling accurate forecasting of tool wear. These predictions are based on real-time geometric measurements collected during cutting by an Automatic Measurement Unit. The measurements are analyzed in the time domain to provide current process corrections and determine the tool lifecycle. A key feature of the model is its self-tuning capability, which adjusts parameters dynamically to handle limited data availability, improving prediction accuracy and reducing the complexity of parameter settings. The model's predictions were validated by comparing predicted wear values against actual measurements. Additionally, the integrated model was compared with a linear prediction model, demonstrating superior accuracy. To evaluate the model's performance, the article uses the normalized root mean square error (NRMSE) as the assessment metric. Results confirm that the first-order inertial adaptive model not only enhances accuracy over adaptive linear model but also provides reliable wear predictions, supporting effective tool change strategies in machining processes. This innovative approach offers significant improvements in managing machining errors and optimizing tool usage.

A study was made of the machinability of NiTi alloy in turning, under conditions resulting in a small cutting layer. The experiment involved cutting with variable feeds ranging from 0.01 to 0.1 mm/rev. The cutting conditions were carefully chosen, considering the rounding radius of the cutting edge. The machined surface was examined and measured in 3D using a confocal microscope and in 2D with a contact profilometer. These measurements were used to estimate h_min, leading to the development of a surface formation model that considers both the lateral material flow due to h_min and the lateral material flow due to altered thermodynamic conditions from the previous blade pass. A method for evaluating the surface and selecting its characteristics was proposed based on analyses derived directly from surface features: PCA (Principal Component Analysis) and EMD (Empirical Mode Decomposition) with the Hilbert transform (Hilbert–Huang transform). PCA analysis facilitated the examination of individual surface component variances, while analysis of the IMF components enabled the assessment of surface component energy combined with instantaneous frequencies.

This article describes a system for measuring and compensating for errors resulting from the cutting process in order to improve the accuracy of the workpiece. Measurements were performed by means of an automatic measurement unit. The diameter of the workpiece was measured at two points, and at the same time, the temperature at the end face of the workpiece was measured. These measurements were used in Statistical Process Control (SPC). Based on the measured values, the process stability was checked and an error correction value was determined for the next item. Moreover, the value of the correction was influenced by the assumed value of tool wear, in accordance with the adopted model, and the possibility of achieving the assumed surface quality. The diameter of the workpiece for SPC purposes was measured under industrial conditions using an automatic measurement unit, which indicates that the temperature of the workpiece during the measurement was significantly higher than the reference temperature. The study focuses on the possibility of identifying a workpiece temperature compensation model in measurements of the workpiece diameter for the purpose of introducing an additional change in the correction value. It was found that a model with a constant correction value and a linear model poorly reflect the nature of the changes. On the other hand, the Autoregressive with Extra Input (ARX) model and the Nonlinear Autoregressive with Extra Input (NLARX) model, with a neural network, are able to map the inertia of the system and map the process with greater accuracy. In this way, measurements performed in industrial conditions can more accurately determine the possibility of achieving the assumed tolerance of the finished product. At the same time, the research shows that the temperature compensation model is nonlinear, and that the maximum possible machining accuracy of the workpiece can be achieved thanks to the repeatable measurement and compensation technique.

The article is devoted to the study of the effect of cryogenic cooling on the tool wear in thread turning tests. The tool wear and its influence on the thread accuracy were investigated. Two different grades of titanium alloys were used for comparative purposes. The excellent performance characteristics of titanium alloys pose machining problems, causing high unit forces at the edge of the tool leading to chipping and premature tool failure. In turn, the low thermal conductivity of pure titanium affects the heat distribution in the cutting zone. The heat is not absorbed by the material being machined but accumulates in the tool, causing an increase in diffusion and chemical wear. The results of cutting tests using liquid nitrogen showed lower values of wear on the major and minor tool flank. The edge reduction of the tool was also significantly less during cryogenic machining. The analysis of the formation of wear marks and the blade wear mechanisms was carried out for the tool rake face. The tests were carried out using the SEM method and confirmed by EDS analyses. In order to compare the course of tool wear over time, a mathematical model was developed, which results from the course of phenomena during cutting. It consists of two complementary equations. The first equation is characteristic for the first cutting phase and results from the loads imposed on the blade and aims at thermodynamic equilibrium. It is a period of stable tool operation and constant wear intensity. The second equation concerns crossing the equilibrium point followed by the process of accumulation of elementary wear phenomena. These phenomena accumulate until the blade is completely worn-out. The use of blade wear development models to determine the expected blade life allowed to confirm the beneficial effect of cryogenic cooling on the course of the blade wear process when cutting threads for two different titanium alloys.