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Understanding surface roughness generation in machining is critical to estimate the final quality of the part, optimize cutting conditions, reduce costs and improve manufacturing sustainability in industry. This work presents a review of the factors that affect surface roughness generation in machining (turning/milling) processes. Up to twenty-five different factors were identified, which were classified as setup factors (cutting tool, machine tool/fixturing and workpiece factors), operational factors (cutting and process parameters) and processing factors, which are related to the resulting cutting processes, such as built-up edge, chatter or tool wear. The importance of understanding these factors to improve machining sustainability is highlighted through three case studies, ranging from a simple change in the cutting insert to a more complex case where a controlled surface roughness leads to the elimination of a grinding stage. A case study illustrating the potential benefit of MQL in the sustainability of the machining process is also reported from the mold manufacturing industry. In all of the cases, the improvement in sustainability in terms of the reduction in kg of CO2 equivalent is notable, especially when grinding operations are reduced or eliminated from the manufacturing process. This paper can be of interest to practitioners in finishing operations at milling and turning operations that want to increase machining sustainability through a deep understanding of surface roughness generation.

Exact observing and forecasting tool conditions fundamentally affect cutting execution, bringing further developed workpiece machining accuracy and lower machining costs. Because of the unpredictability and time-differing nature of the cutting system, existing methodologies cannot achieve ideal oversight progressively. A technique dependent on Digital Twins (DT) is proposed to accomplish extraordinary accuracy in checking and anticipating tool conditions. This technique builds up a balanced virtual instrument framework that matches entirely with the physical system. Collecting data from the physical system (Milling Machine) is initialized, and sensory data collection is carried out. The National Instruments data acquisition system captures vibration data through a uni-axial accelerometer, and a USB-based microphone sensor acquires the sound signals. The data are trained with different Machine Learning (ML) classification-based algorithms. The prediction accuracy is calculated with the help of a confusion matrix with the highest accuracy of 91% through a Probabilistic Neural Network (PNN). This result has been mapped by extracting the statistical features of the vibrational data. Testing has been performed with the trained model to validate the model’s accuracy. Later, the modeling of the DT is initiated using MATLAB-Simulink. This model has been created under the data-driven approach. The physical–virtual balance of the DT model is acknowledged utilizing the advances, taking into consideration the detailed planning of the constant state of the tool’s condition. The tool condition monitoring system through the DT model is deployed through the machine learning technique. The DT model can predict the different tool conditions based on sensory data.

This study proposes a machine tool vibration prediction method based on multi‐sensor fusion and a long short‐term memory (LSTM) network. Machine tool vibration significantly impacts machining quality, surface roughness, dimensional accuracy, and tool wear. By combining deep learning with industrial applications, this method achieves high‐precision vibration prediction through multi‐sensor data fusion. Data is input into the LSTM model to predict the next moment's vibration. Experimental results demonstrate strong prediction capability for periodic vibrations and machining‐specific vibration errors, effectively enhancing machining accuracy. A machine tool operating vibration prediction method based on multi‐sensor fusion (Kalman) and long short‐term memory network is proposed

This paper presents a hybrid fault-diagnosis framework for milling cutting tools designed to address three persistent challenges in industrial monitoring: noisy vibration signals, limited fault labels, and variability across operating conditions. The framework begins by removing baseline drift from raw signals to improve the signal-to-noise ratio. Logarithmic continuous wavelet scalograms are then constructed to provide precise time-frequency localization and reveal fault-related harmonics. To enhance feature clarity, a Canny edge operator is applied, suppressing minor artifacts and reducing intra-class variation so that key diagnostic structures are emphasized. Feature representation is obtained through a dual-branch encoder, where one pathway captures localized patterns while the other preserves long-range dependencies, resulting in compact and discriminative fault descriptors. These descriptors are integrated by an ensemble decision mechanism that assigns validation-guided weights to individual learners, ensuring reliable fault identification, improved robustness under noise, and stable performance across diverse operating conditions. Experimental validation on real-world cutting tool data demonstrates an accuracy of 99.78%, strong resilience to environmental noise, and consistent diagnostic performance under variable conditions. The framework remains lightweight, scalable, and readily deployable, providing a practical solution for high-precision tool fault diagnosis in data-constrained industrial environments.

The intelligent monitoring of cutting tools used in the manufacturing industry is steadily becoming more convenient. To accurately predict the state of tools and tool breakages, this study proposes a tool wear prediction technique based on multi-sensor information fusion. First, the vibrational, current, and cutting force signals transmitted during the machining process were collected, and the features were extracted. Next, the Kalman filtering algorithm was used for feature fusion, and a predictive model for tool wear was constructed by combining the ResNet and long short-term memory (LSTM) models (called ResNet-LSTM). Experimental data for thin-walled parts obtained under various machining conditions were utilized to monitor the changes in tool conditions. A comparison between the ResNet and LSTM tool wear prediction models indicated that the proposed ResNet-LSTM model significantly improved the prediction accuracy compared to the individual LSTM and ResNet models. Moreover, ResNet-LSTM exhibited adaptive noise reduction capabilities at the front end of the network for signal feature extraction, thereby enhancing the signal feature extraction capability. The ResNet-LSTM model yielded an average prediction error of 0.0085 mm and a tool wear prediction accuracy of 98.25%. These results validate the feasibility of the tool wear prediction method proposed in this study.

The amount of steel waste produced is on the increase due to improvements in steel manufacturing industries. The increase in such wastes causes significant environmental problems and, furthermore, a large area is also required to store these waste products. Instead of disposing of these wastes, the reuse of them in different industries is an important success in terms of both reducing environmental pollution and providing low-cost products. From this motivation, the effect of lathe scrap fibers generated from Computer Numerical Control (CNC) lathe machine tools on concrete performance was investigated in this study. Pursuant to this aim and considering different fiber content, an experimental study was conducted on some test specimens. Workability and slump values of concrete produced with different lathe scrap fibers were determined, and these properties were compared with those of plain concrete. For the hardened concrete, 150 mm × 150 mm × 150 mm cubic specimens and cylindrical specimens with a diameter of 100 mm and a height of 200 mm were tested to identify compressive strength and splitting tensile strength of the concrete produced with different volume fracture of lathe waste scrap (0%, 1%, 2% and 3%). With the addition of the lathe scrap, the compressive and splitting tensile strength of fiber-reinforced concrete increases, but after a certain value of steel fiber content, there is a decrease in workability. Next, a three-point bending test was carried out on samples with dimensions of 100 × 100 × 400 mm and a span length of 300 mm to obtain the flexure behavior of different mixtures. It has been shown that the flexural strength of fiber-reinforced concrete increases with an increasing content of waste lathe. Furthermore, microstructural analysis was performed to observe the interaction between lathe scrap fiber and concrete. Good adhesion was observed between the steel fiber and cementitious concrete. According to the results obtained, waste lathe scrap fiber also worked as a good crack arrestor. Lastly, practical empirical equations were developed to calculate the compressive strength and splitting tensile strength of fiber-reinforced concrete produced with waste lathe scrap.

The milling machine serves an important role in manufacturing because of its versatility in machining. The cutting tool is a critical component of machining because it is responsible for machining accuracy and surface finishing, impacting industrial productivity. Monitoring the cutting tool’s life is essential to avoid machining downtime caused due to tool wear. To prevent the unplanned downtime of the machine and to utilize the maximum life of the cutting tool, the accurate prediction of the remaining useful life (RUL) cutting tool is essential. Different artificial intelligence (AI) techniques estimate the RUL of cutting tools in milling operations with improved prediction accuracy. The IEEE NUAA Ideahouse dataset has been used in this paper for the RUL estimation of the milling cutter. The accuracy of the prediction is based on the quality of feature engineering performed on the unprocessed data. Feature extraction is a crucial phase in RUL prediction. In this work, the authors considers the time–frequency domain (TFD) features such as short-time Fourier-transform (STFT) and different wavelet transforms (WT) along with deep learning (DL) models such as long short-term memory (LSTM), different variants of LSTN, convolutional neural network (CNN), and hybrid models that are a combination of CCN with LSTM variants for RUL estimation. The TFD feature extraction with LSTM variants and hybrid models performs well for the milling cutting tool RUL estimation.

In this study, the impacts of different proportions of tension reinforcement and waste lathe scraps on the failure and bending behavior of reinforced concrete beams (RCBs) are clearly detected considering empirical tests. Firstly, material strength and consistency test and then ½ scaled beam test have been carried out. For this purpose, a total of 12 specimens were produced in the laboratory and then tested to examine the failure mechanism under flexure. Two variables have been selected in creating text matrix. These are the longitudinal tension reinforcement ratio in beams (three different level) and volumetric ratio of waste lathe scraps (four different level: 0%, 1%, 2% and 3%). The produced simply supported beams were subjected to a two-point bending test. To prevent shear failure, sufficient stirrups have been used. Thus, a change in the bending behavior was observed during each test. With the addition of 1%, 2% and 3% waste lathe scraps, compressive strength escalated by 11.2%, 21.7% and 32.5%, respectively, compared to concrete without waste. According to slump test results, as the waste lathe scraps proportion in the concrete mixture is increased, the concrete consistency diminishes. Apart from the material tests, the following results were obtained from the tests performed on the beams. It is detected that with the addition of lathe waste, the mechanical features of beams improved. It is observed that different proportions of tension reinforcement and waste lathe scraps had different failure and bending impacts on the RCBs. While there was no significant change in stiffness and strength, ductility increased considerably with the addition of lathe waste.

In the domain of modern manufacturing, computer numerical control (CNC) milling machines have emerged as instrumental assets. However, the data they generate is of vast amount, but usually contains redundancies and displays consistent patterns, making it inefficient for deep learning training. This paper proposes a novel sampling algorithm tailored for CNC milling machine data, emphasizing both diversity and efficiency. The proposed method leverages the entropy concept from the information‐theoretic perspective to evaluate and enhance data diversity, aiming to achieve efficient learning with high accuracy. This in turn enables to not only facilitates a deeper understanding of CNC data characteristics but also contributes significantly to the optimization of deep learning training processes in the context of CNC milling data. This paper proposes a novel sampling algorithm tailored for computer numerical control milling machine data, emphasizing both diversity and efficiency. The proposed method leverages the entropy concept from the information‐theoretic perspective to evaluate and enhance data diversity, aiming to achieve efficient learning with high accuracy.

This work aims to develop technical solutions that allow providing the specified parameters of the accuracy of the shape of parts in the cross-section during processing on a CNC machine. Experimental studies were performed on a screw-cutting lathe. An acoustic signal in the range from 6 to 12 kHz was used as a diagnostic sign to assess the wear of the cutting tool, since during preliminary studies, it was found that this range is most sensitive to changes in processing modes. Studies were performed at different values of wear of the cutting tool (estimated by the width of the wear chamfer). For estimating the life of a cutting tool, a neuro-fuzzy model has been developed. Using models of this class allows adjusting to specific conditions (machine, tool), and correctly evaluating the tool life. The model error for the test sample does not exceed 10%. The test results showed that using the proposed solutions makes it possible to increase the accuracy of the manufacturing of shut-off valve parts by 20-30%.