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Our university, Tokyo Institute of Technology, conducted safety and health risk assessments (RA). Previous research indicated that many hazards identified in RA are less likely to be the main causes of actual accidents, while those not identified in RA are more likely to be the main causes of actual accident. Particularly, notable cases not identified as hazards in RA and having more occurrences as actual causes were "glass" and "cutting tool". Therefore, the objective of this study is to find out why these hazards have not been identified and what should be done to ensure fruitful RA implementation. Here, RA reports and accident reports of "glass" and "cutting tool" related work were intensively investigated. The findings demonstrated that in STEP 1, the "hazard identification" stage, the risk-assuming work should be much closer to the actual accident, that is, to increase the awareness of hazards in pre- and postexperimental work such as "force-applied" work, where previously a low proportion of assumptions had been made. In STEP 2, "risk estimation and assessment" stage, the following steps are required: assume the situation of damage caused by the risk-assumed work in a more concrete, rather than vague, manner, estimate and score the degree of damage at a higher level, determine the risk level as III or IV accordingly, and link this to the consideration and implementation of preventive measures. Lastly, in STEP 3, the "consideration of risk reduction measure and implementation" stage, countermeasures were to focus on worker-independent measures, such as the "intrinsic measure" and "engineering measure". [ABSTRACT FROM AUTHOR]

Online condition monitoring and remaining useful life (RUL) prediction play an essential role for cutting tools in the machining process as they directly affect the product's quality and reliability. This work proposes an efficient and reliable method for RUL prediction of the cutting tools using decision-making models such as the squared exponential Gaussian process regression (SE-GPR) method. The work also defines the optimum feature selection approach based on Shannon entropy. Firstly, the vibration and force sensor features are extracted, and their Shannon entropy values are calculated. Then, the features with minimum Shannon entropy values are selected that carry the maximum information about the cutting tool state and effectively reflect the cutting tool's degradation patterns. Finally, the SE-GPR model is utilized to examine the RUL prediction capability of the selected features. The proposed Shannon entropy-based feature selection method effectively reduces the number of features and improves computational and prediction accuracy compared to other feature selection methods, i.e., principal component analysis, Pearson correlation coefficient, and suitability index (monotonicity, trendability, and robustness). The prediction accuracy has been enhanced using selected features and the SE-GPR model, showing its superiority for RUL prediction compared to other approaches such as neural networks and SVM. [ABSTRACT FROM AUTHOR]

The rotors are the key components in the Roots pump, and the rotor manufacture time will decide the pump price. In special cases, such as when one rotor shaft has two stages, respectively, with claw-type and Roots profiles, the traditional forming method is unsuitable for manufacturing the Roots rotor stage. Instead, more suitable manufacturing methods, such as planning or end-mill milling, can be applied. However, these two methods take time and effort. Therefore, this study proposes a general mathematical approach for Roots rotor manufacturing using the conical milling cutter as the key tool, which can reduce manufacturing time. The contact points and meshing angles between the rotor and the conical milling cutter can be calculated based on contact conditions. Then, a 3D conical milling cutter surface is obtained based on the cutter profile as a straight line, and the conical cutter is designed and considered to avoid undercutting in the rotor cutting process. The generated Roots rotor can be obtained by applying cutting simulation from the proposed mathematical model. The normal deviations of the rotor can be found by comparing the generated rotor profile to the datum rotor profile. In addition, the influence of axis errors of the conical milling cutter on the tooth profiles of the Roots pump rotor is investigated and discussed. The experimental results based on the proposed mathematical model are obtained and expressed to confirm the practicability of the proposed method. [ABSTRACT FROM AUTHOR]

Purpose: Artificial Intelligence-based methods have significantly assisted in condition monitoring of cutting tools by using supervised learning algorithms. When a machine learning model or a neural network is used for classification, it yields four different cases: true classifications (includes true positive and true negative), type I error, and type II error. True classifications encompass all data points that were accurately classified as healthy or damaged. The type I error refers to a true negative label misclassified as positive, and the type II error refers to a true positive label misclassified as negative. The type II error goes unnoticed when the model is deployed in real-time, while the other three cases are notified successfully. Notifying the type II error is the genuine concern, i.e. when a faulty label is misclassified as healthy. Methods: In an attempt to address this problem, an enhanced neural network was constructed, and the values derived from the outputs of neurons are used to facilitate notification of type II error by controlling the confidence levels. Another problem arises while resorting to such learning algorithms: the inability to classify data into labels besides those used for training. This type of undefined data is encountered due to various unknown factors stemmed due to a high noise environment. To tackle this, a discriminator is used to evaluate the training data distribution with the incoming data and segregate undefined conditions into a separate undefined category. Results: The vibrations caused by unknown factors are fed into the network as an input, triggering the discriminator to classify these data points into a separate category. The proposed methodology aims to notify type II error caused by AI models in real-time and simultaneously also seeks to establish a distinction between data and noise by segregating data points derived from noise into a separate category in real-time. Conclusion: The proposed provision would allow for better resource management and enhancement of efficiency of the system by mitigating the chances of a misclassification by the AI model. [ABSTRACT FROM AUTHOR]

In metalworking, cutting fluids play an important role by reducing heat and friction during machining, extending tool life, and improving surface finish. Although the positive effects of cutting fluid have been confirmed in many studies, the relevant cutting fluid parameters such as nozzle cross-section area and supply pressure, as well as their influence on the thermo-mechanical loads of the cutting tool, have been insufficiently investigated. This study investigates the effects of cutting fluid supply conditions on tool loads during continuous and interrupted orthogonal cutting processes. The research specifically addresses the impact of nozzle geometry and fluid jet orientation on the thermo-mechanical loads on the cutting tool, which have been underexplored in previous studies. A prototype tool holder, designed and additively manufactured for this purpose, allows for variations in nozzle geometry and jet orientation. Experiments were conducted under varying cutting parameters, nozzle geometries, and fluid pressures, with tool temperature being monitored through an embedded thermocouple. The results show that nozzle geometry significantly affects chip shape, which directly affects cooling efficiency and, consequently, tool temperature. The study also uses an inversely calibrated analytical model to analyze the tool temperature distribution, which shows that the highest temperatures occur in the tool-chip contact area, while temperatures outside this area decrease rapidly. In addition, the percentage of heat conducted into the tool decreases with increasing Péclet number, which is consistent in both continuous and interrupted cutting scenarios. These findings provide a deeper understanding of how cutting fluid nozzle design affects tool performance and establish a foundation for model-based temperature analysis in machining processes. [ABSTRACT FROM AUTHOR]

Low content polycrystalline cubic boron nitride (PcBN) samples were synthesized by synthesis method with cubic boron nitride (cBN) contents (wt%) of 50%, 60%, and 70%, respectively. Using titanium nitride as the ceramic adhesive and aluminum as the metal adhesive, the synthesis was carried out in a UDS-650 six sided hydraulic press with a pressure of 5.5 GPa, a temperature of 1400-1600 °C and a sintering time of 10 minutes to obtain PcBN with a thickness of 4 millimeters. The phase composition and microstructure of PcBN were confirmed with apparatus of XRD and SEM. Results indicated that with the increasing of cBN content, the Vickers hardness value of PcBN shows same trend. The effect of ball milling time and working power on increasing the hardness of materials is not significantly. PcBN cutting tools with 50% cBN content exhibit abrasive wear and poor wear resistance. The cutting tools with 60% cBN content and 70% cBN content both have lower wear degree, but the PcBN cutting tools with 60% cBN content is significantly better than that of 70% cBN content. The PcBN cutting tools with 60% cBN content shows excellent wear resistance after mid-term turning, then the wear on the back surface slows down rapidly and exhibits a stable micro wear state. [ABSTRACT FROM AUTHOR]

This paper systematically explores the applications of image processing techniques in machined surface analysis, a critical area in industries like manufacturing, aerospace, automotive, and healthcare. It examines the integration of image processing in traditional Computer Numerical Control (CNC) machining and micromachining, focusing on its role in tool wear analysis, workpiece detection, automatic CNC programming, and defect inspection. With AI and machine learning advancements, these technologies enhance defect detection, surface texture analysis, predictive maintenance, and quality optimization. The paper also discusses future advancements in high resolutions, 3D imaging, augmented reality, and Industry 4.0, highlighting their impact on productivity, precision, and challenges such as data privacy. In conclusion, image processing remains vital to improving manufacturing efficiency and quality control. [ABSTRACT FROM AUTHOR]

The turning process is a widely used machining process, and its productivity has a significant impact on the cost and profit in industrial enterprises. Currently, it is difficult to effectively determine the optimum process parameters under complex conditions. To address this issue, a classification-based parameter optimization approach of the turning process is proposed in this paper, which aims to provide feasible optimization suggestions of process parameters and consists of a classification model and several optimization strategies. Specifically, the classification model is used to separate the whole complex process into different substages to reduce difficulties of the further optimization, and it achieves high accuracy and strong anti-interference in the identification of substages by integrating the advantages of an encoder-decoder framework, attention mechanism, and major voting. Additionally, during the optimization process of each substage, Dynamic Time Warping (DTW) and K-Nearest Neighbor (KNN) are utilized to eliminate the negative impact of cutting tool wear status on optimization results at first. Then, the envelope curve strategy and boxplot method succeed in the adaptive calculation of a parameter threshold and the detection of optimizable items. According to these optimization strategies, the proposed approach performs well in the provision of effective optimization suggestions. Ultimately, the proposed approach is verified by a bearing production line. Experimental results demonstrate that the proposed approach achieves a significant productivity improvement of 23.43% in the studied production line. [ABSTRACT FROM AUTHOR]

In centrifugal sugar beet cutters, the cutter is the key component, and its efficiency has a considerable influence on the productivity and the quality of the sugar obtained. Laminar coatings may considerably improve its productivity. Such coatings may be designed so as to increase tool life and increase the cutting efficiency. In addition, their hardness and self-sharpening help prevent wear of the cutting edge. A coating of nickel-clad boron carbide powder is applied to experimental samples of 40G steel, by detonation spraying. That simulates the cutting edge of unribbed blades of 1011-V type. The coating has a dense uniform laminar structure, without cracks or pores containing boron carbide (B4C) powder. The B4C particles measure less than 10 µm and are uniformly distributed in the Ni–NiO metal–ceramic matrix. The mean hardness of the coating is 770 HV, which is more than triple the substrate hardness. These results show that hardening of the tool surface is effective and provide valuable information for improving coatings by enhancing self-sharpening in the course of operation. [ABSTRACT FROM AUTHOR]

Despite significant advancements in both measurement systems and machine learning techniques, the integration of these technologies for real-time tool fault diagnosis in milling processes remains under developed. Existing studies tend to focus on a comprehensive comparative analysis that bridges these two areas machine learning algorithms or the application of specific measurement sys-tems. There is also a gap in evaluating the cost-effectiveness and practicality of different measurement systems when integrated with machine learning models for industrial applications. This study addresses these gaps by conducting a detailed comparative analysis of multiple measurement sys-tems and their performance with machine learning techniques in a real-world milling context, aim-ing to provide practical recommendations for industry adoption. Using both traditional and Artificial Intelligence (AI) to define and exploit sensory systems in the milling process, as well as various (direct and indirect) monitoring approaches, are summarised in this study. Machine learning tech-niques SVM, KNN, DT performs better and provide higher accuracy and in feature extraction clas-sification techniques statistical features, wavelet transform with the Holder Exponent (HE) having higher accuracy for diagnosing the tool faults. [ABSTRACT FROM AUTHOR]

Cutting tool materials are the backbone of machining and play a vital role in the manufacturing industry. Innovation in cutting tools is important for customized and demanding applications. This state-of-the-art review is focused on innovations and future research directions for cutting tools covering i) tool materials/microstructure/property relationships, ii) coatings and their effect on tool performance, iii) cutting edge and functional surface preparation and effect on tool performance, iv) tool geometry for high performance and stable machining considering rapid machining, sustainability, and circularity aspects. The vision is to identify tool material/coating/geometry/functional surface relationships for significant improvement in machining performance. This paper includes perspectives from several research groups with a detailed discussion on current advances, capabilities, and challenges in engineered design of cutting tools, materials, coatings, structures and sets a new agenda for future tooling and research directions. [ABSTRACT FROM AUTHOR]

Resin bond grinding wheels are the most common grinding tools in the industry. Until now, all research on the additive manufacturing of resin bond grinding wheels has focused on commercially available acrylate resin. However, using a phenol-based bond to print resin-bond grinding wheels has been challenging for researchers and industries. In this study, a photo-curable phenol resin bond grinding wheel was introduced for the first time, offering advantages such as lower cost, high thermal resistance, and good mechanical properties. To enhance the grinding performance of the printed wheels, various additives, such as copper, glass fiber, and carbon fiber, were incorporated into the composition. Different on-machine and out-of-machine measurements, such as force, tool wear, dimensional accuracy, and optical microscopy measurements, were conducted to investigate the grinding performance of the printed wheels. The results demonstrate that printed grinding wheels have strong potential in grinding applications, which was more prominent for the bond reinforced by glass fibers, providing improved mechanical properties (up to 50%), wear resistance (up to 75%), and higher dimensional accuracy (up to 11%). [ABSTRACT FROM AUTHOR]

This study was intended to investigate the mechanical property at high temperature and cutting performance of cemented carbide for machining of exotic materials, such as nickel alloys, cobalt alloys, and titanium alloys. Exotic materials are widely used for equipment and parts in the aerospace and automotive industries due to their superior heat and corrosion resistance. When machining exotic materials, its low thermal conductivity causes the cutting edge temperature to be higher than when cutting steel, and the hardness of the tool material decreases, resulting in extremely low tool life. However, because cemented carbide in particular contains metals in its composition, plastic deformation of the cemented carbide cannot be prevented even when the coating has sufficient heat resistance. The WC-Co-NiCr alloy, in which NiCr partially replaces Co as the binder phase of cemented carbide, has improved high-temperature properties while maintaining room temperature properties compared with conventional WC-Co alloys, and tool life has been improved in machining exotic alloys. [ABSTRACT FROM AUTHOR]

This study was intended to investigate the mechanical property and cutting performance of AlTiBN coating for machining of exotic materials such as heat-resistant alloys and Titanium alloys, which has been increasing for aerospace, energy and medical industries. In conventional PVD coatings for cutting tools, Cr and Si has often been added to AlTiN in order to increase hardness and heat resistance, achieving high wear resistance. However, these methods increase hardness as well as Young's modulus, which reduces toughness of coating. In exotic materials milling, coatings with a high Young's modulus tends to develop internal cracks, leading to early destruction of the coatings. In this research, optimizing B content of AlTiBN coating, we realized low Young's modulus while maintaining high hardness. This is probably because the pure AlTiN changed to a mixed crystal structure of cubic and hexagonal by the addition of B. When the cutting performance of this AlTiBN coating was evaluated in milling for Inconel 718, it was found that internal cracks were suppressed at the initial stage of processing. As a result, cemented carbide tool with the AlTiBN coating achieved approximately 1.6 times longer tool life than that with B-free AlTiN coating. [ABSTRACT FROM AUTHOR]

The Ti-5Al-5V-5Mo-3Cr (Ti-5553) alloy is a relatively novel difficult-to-cut material with limited machinability and tool life analysis available in the literature, and hence requires further investigation. This study focuses on the machining and tribological performance of Ti-5553 under high-speed finish turning (150 m/min, 175 m/min, and 200 m/min) via novel mono/bi-layered PVD-coated WC tools. A base AlTiN coating is used as the reference monolayer coating, with AlCrN, diamond-like ta-C, and TiAlSiN coatings each deposited on top of a base AlTiN coating, totaling four separate coated tools (one monolayer and three bi-layer). Tool life, cutting forces, workpiece surface quality, and tribological chip analysis are among the subjects of investigation in this study. Overall, the AlTiN/AlCrN coated tool outperformed all the other combinations: an improvement of ~19% in terms of tool life in reference to the base AlTiN coating when averaging across the three speeds; lowest surface roughness values: ~0.30, 0.33, and 0.64 µm; as well as the lowest chip back surface roughness values: ~0.80, 0.68, and 0.81 µm at 150, 175, and 200 m/min, respectively. These results indicate that the AlTiN/AlCrN coating is an excellent candidate for industrial applications involving high-speed machining of Ti-5553. [ABSTRACT FROM AUTHOR]

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Nomex honeycomb composites are widely recognized for their advanced structural applications in the aerospace, automotive and defense industries. These materials are distinguished by exceptional characteristics such as thin cell walls and a hexagonal structure, as well as layers made of phenolic resins and aramid fibers. However, complex machining and the maintenance of high quality at a large scale presents considerable challenges. This study provides a comprehensive review of the literature on the processing of Nomex composites, highlighting the design challenges related to processing technologies, the impact of conventional and ultrasonic processing methods, and the associated mechanical properties and microstructural topographies. Moreover, it reviews research advances in machining techniques, current challenges, and future perspectives, thereby providing valuable guidance to ensure the optimal cutting of Nomex honeycomb composite structures (NHCs). [ABSTRACT FROM AUTHOR]

This paper addresses the optimization of CNC (Computer Numerical Control) working time through the strategic positioning and grouping of cutting tools. Our analysis primarily focused on evaluating the previous arrangement of tools in the magazine and exploring the potential for repositioning them to enhance CNC operational efficiency. The work methodology is based on the collection of direct data from CNC as well as through CAD/CAM simulations. The collected data are analyzed with the Python programming language, which is one of the most used and efficient languages now. By grouping the tools according to the same function, tools were repositioned next to each other, which we obtained. After collecting the data and processing them in the Python programming language, we made a visualization of the data. Working time is reduced from 27.44 s to 26.46 s and results are analyzed in the Python Programming language. The working time is reduced by 0.98 s, which is approximately a 3.57% reduction in the working time. This indicates a significant improvement in efficiency due to the optimization of the cutting tool positions in the magazine. [ABSTRACT FROM AUTHOR]

The paper deals with a novel method for the edge preparation of cutting tools using an advanced approach. Cemented carbide cutting inserts were used as a sample in the experiment, and were prepared using plasma discharges in electrolyte. This technique is an environmentally-friendlier method in contrast to electropolishing treatment. The experiment aimed to determine the influence of process parameters of plasma treatment on the material removal rate and cutting edge radius size. In the experiment, concentration of electrolyte, voltage between electrodes, temperature of electrolyte, and operating time were varied. The material removal rate and cutting edge radius size increased faster with the decreasing concentration of electrolyte, decreasing voltage, and decreasing temperature of electrolyte. The material removal percentage increase was 155 percent, and 57 percent for cutting edge radius size during the 10 seconds of operation time in the specified range of process parameters. This change can be achieved by setting various process parameters. The results of the experiment proved that plasma treatment can control the forming of cutting edge radii. Cutting edge preparation using plasma discharges in electrolyte takes only a few seconds, depending on the cutting edge radius sizes. [ABSTRACT FROM AUTHOR]

Production of pipes with internal longitudinal grooves is an urgent problem in the conditions of existing production technologies. The use of new types of materials in industry requires the study of machining processes intensification based on the use of the latest cutting methods. The article provides an analysis of the positive effects of ultrasonic cutting and methods of increasing the efficiency of processing internal grooves. During ultrasonic cutting with the imposition of forced vibrations, the roughness of the treated surface is significantly reduced. Under the conditions of cutting with the imposition of forced oscillations, the influence of lubrication and cooling of the friction surfaces increases significantly. This is especially true for the intermittent cutting process. It should be noted that the main and perpendicular components of the cutting force will appear in the oscillating system. Longitudinal oscillations overlap bending oscillations and the cutting edge performs circular oscillatory movements. This leads to shock loads on the back surface of the cutter, which accelerates tool wear. In order to eliminate this shortcoming, it is proposed to place the central axis of oscillation normal to the direction of action of the uniform cutting forces. In addition, the direction of the central axis of oscillation must coincide with the direction of forced oscillations of the cutter. In this way, it is possible to significantly reduce the amount of non-periodic oscillations of the cutting blade caused by the dynamic characteristics of the technological system. The design of a multi-cutting special head for processing internal grooves is suggested. The use of a special head will make it possible to significantly increase the quality, accuracy and efficiency of groove processing due to the reduction of the influence of force and temperature factors on the tool material. [ABSTRACT FROM AUTHOR]

This paper presents the results of the experimental research on diamond-reinforced composites with WC–Co matrices enhanced with a ZrO2 additive. The samples were prepared using a modified spark plasma sintering method with a directly applied alternating current. The structure and performance of the basic composite 94 wt.%WC–6 wt.%Co was compared with the ones with ZrO2 added in proportions up to 10 wt.%. It was demonstrated that an increase in zirconia content contributed to the intense refinement of the phase components. The composite 25 wt.%Cdiamond–70.5 wt.%WC–4.5 wt.%Co consisted of a hexagonal WC phase with lattice parameters a = 0.2906 nm and c = 0.2837 nm, a cubic phase (a = 1.1112 nm), hexagonal graphite phase (a = 0.2464 nm, c = 0.6711 nm), as well as diamond grits. After the addition of zirconia nanopowder, the sintered composite contained structural WC and Co3W3C phases, amorphous carbon, tetragonal phase t-ZrO2 (a = 0.36019 nm, c = 0.5174 nm), and diamond grits—these structural changes, after an addition of 6 wt.% ZrO2 contributed to an increase in the fracture toughness by more than 20%, up to KIc = 16.9 ± 0.76 MPa·m0.5, with a negligible decrease in the hardness. Moreover, the composite exhibited an alteration of the destruction mechanism after the addition of zirconia, as well as enhanced forces holding the diamond grits in the matrix. [ABSTRACT FROM AUTHOR]

The authors assess abrasiveness of sand with continuous acoustic emission recording during LCPC test with crushing of a 500 g sample by a straight impeller in a metal barrel. The informative parameters are the acoustic emission spectra averaged over a number of instances at different times of crushing, and over six frequency bands from 2 to 500 kHz. The obtained coefficients of multiple regression coefficients allow calculating wear of a cutting tool and the LCPC abrasion coefficient LAC for different crushing times by the averaged amplitudes of the AE spectrum components. This method is applicable in tunnel boring, hole drilling and other practices involved in real-time control of ground abrasiveness and wear of cutting tools to be replaced in a timely manner. [ABSTRACT FROM AUTHOR]

The degradation of the cutting tool and its optimal replacement is a major problem in machining given the variability in this degradation even under constant cutting conditions. Therefore, monitoring the degradation of cutting tools is an important part of the process in order to replace the tool at the optimal time and thus reduce operating costs. In this paper, a cutting tool degradation monitoring technique is proposed using bootstrap-based artificial neural networks. Different indicators from the turning operation are used as input to the approach: the RMS value of the cutting force and torque, the machining duration, and the total machined length. They are used by the approach to estimate the size of the flank wear (VB). Different neural networks are tested but the best results are achieved with an architecture containing two hidden layers: the first one containing six neurons with a Tanh activation function and the second one containing six neurons with an ReLu activation function. The novelty of the approach makes it possible, by using the bootstrap approach, to determine a confidence interval around the prediction. The results show that the networks are able to accurately track the degradation and detect the end of life of the cutting tools in a timely manner, but also that the confidence interval allows an estimate of the possible variation of the prediction to be made, thus helping in the decision for optimal tool replacement policies. [ABSTRACT FROM AUTHOR]

Abstract—Comparative durability tests of carbide cutting tools with (Ti, Al)N–Cu and (Ti, Al)N–Ni coatings applied to its working surfaces were carried out under conditions of continuous and interrupted cutting of steels 09G2S and EP302-Sh, respectively. Under milling conditions of EP302-Sh steel, using VK6NST inserts with (Ti, Al)N–Cu and (Ti,Al)N–Ni coatings, the tool life increases by 3.1 and 1.7 times, respectively. When turning the 09G2S steel using VK6NST inserts coated with (Ti, Al)N–Cu and (Ti, Al)N–Ni, the tool life increases by 7.6 and 10.8 times, respectively, and the cutting forces Fz, Fx, and Fy are reduced by ~20%. It is shown that this effect is determined by the presence of a nanostructure in the formed coatings and the presence of a highly consistent ceramic component and ductile metal, which determine their hardness (more than 35 GPa) and fracture toughness (relative work of plastic deformation during indentation over ~60%) and increased tribological characteristics (friction coefficient of ~0.5 compared to uncoated carbide of ~0.7). [ABSTRACT FROM AUTHOR]

The good development of the chipping processing processes is greatly influenced by the rigidity of the technological system, but also by the size and variation of the forces that appear during the chipping processing process. The force required to detach the chip must be known both for the design of the machine tool, the devices and the cutting tools, and for establishing some limit values of its size, depending on the strength or, in most cases, the rigidity of the processed semi-finished product. The paper presents the variation of the values of the cutting force for the transverse turning operation due to the variation of the functional geometry of the cutting tool during the cutting process in order to optimize the geometry of the active part of the cutting tool. Thus, in the work, the recommended optimal constructive geometry of a lathe knife that can be used for turning a part with the diameter 150mm , as well as the parameters of the cutting regime, was established. Under these conditions, a correlation was identified between the values of the functional geometric parameters of the cutting tool used for transverse turning of C45 steel (1.0503): EN 10277-2-2008-  150mm and the cutting forces that occur during cutting

This study examines coatings based on Ti-Al system intermetallics deposited in a nitrogen environment. The research investigates the structure, phase composition, chemical composition, and mechanical properties of coatings with varying ratios of Ti-Al to TiAlN layer thicknesses. Multiple analytical techniques, including nanoindentation, sclerometry, microhardness measurements, electron microscopy, X-ray diffractometry, and transmission electron microscopy, were employed. The results demonstrate that the coating architecture significantly influences its physical and mechanical properties. Notably, coatings with a variable thickness gradient structure exhibit the best properties and are the most promising for practical applications, offering enhanced hardness, wear resistance, and adhesion strength. Furthermore, the findings indicate that a carefully selected combination of layers can be used to control coating properties across a wide range, making these coatings highly suitable for demanding industrial applications.

In order to optimize the design of the tool edge, an intelligent method was used for modeling and optimization. The tool edge design method based on support vector regression (SVR) and particle swarm optimization (PSO) was proposed. By combining tool edge parameters and processing condition parameters, and learning from empirical data, a functional model was established between tool life and edge parameters and processing condition parameters. Taking the tool life as the objective function, the optimal edge profile design parameters were solved under different processing condition parameters. The T-shape tool was taken as a case for verification. The SVR-PSO function model was established and solved based on the processing condition parameters, and the optimized edge design parameters and predicted tool life were obtained. The results showed that the deviation between the calculated and actual tool life was less than 6.4%. This method was feasible and practical and has been applied in the design department of tool manufacturing companies. [ABSTRACT FROM AUTHOR]

In this study, the 5S method was applied in the tool room of a company operating in the aviation field and engaged in machining. Preparations for the 5S study were carried out in June 2020, and after this month, production was started according to the 5S method. Outputs such as defective parts rates due to cutting tools, costs due to tool room, machine waiting due to tool supply and tool preparation time were determined as performance evaluation criteria. Within the scope of preparation, first of all, lean production and 5S trainings were given to company employees. Personnel for the area where 5S will be applied have been determined and necessary planning has been made. Sorting, organizing, cleaning, standardization and discipline steps related to 5S have been successfully implemented, respectively. For the recovery data, the outputs of the first half of 2020 and the last half were compared and evaluated. As a result of this study, 66% improvements were observed in the cost of the tool room, 76% in the downtime rate of the machines due to the tool room, 78% in the scrap rate due to the faulty cutting tool, and 85% in the unnecessary movement load rates in the tool room. As a result of these improvement works, the efficiency of production has increased. With the gray relational analysis, where all outputs are evaluated jointly, it has been revealed that the most productive month in terms of production is November, when production is made by applying the 5S approach. It was observed that the most inefficient month was April, when production was carried out without applying the 5S approach. [ABSTRACT FROM AUTHOR]

High-pressure cutting fluid (CF) supply in milling allows higher productivities and results in extended tool life and higher process reliability. On the other hand, the use of CF results in high energy consumption of the CF supply units. By using the additive manufacturing (AM) technique Laser Powder Bed Fusion (PBF-LB), steel-based basic bodies for indexable cutting tools can be manufactured including complex internal structures with new geometrical possibilities. Thus, the internal CF supply can be redesigned, lowering fluid losses within the supply channels and focusing the fluid for a more efficient and effective cooling and lubrication of the contact zone. This study focuses on the ecological evaluation of PBF-LB manufactured indexable tangential milling tools with adapted CF supply for roughing of Ti-6Al-4V according to DIN EN ISO 14040/44. The ecological competitiveness of the newly developed tools against a conventionally manufactured reference tool was evaluated. Two system boundaries were analyzed, considering the cradle-to-gate as well as the cradle-to-grave life cycle. Even though the calculated environmental impact for the manufacturing of the AM tool exceeded the conventional tool, the improved functionality, increased tool life and reduced energy consumption during service life of the tool led to a reduction in the overall ecological costs. [ABSTRACT FROM AUTHOR]

Multiple international organizations and many governments around the world have declared climate emergency. There is a pressing need to minimize the environmental impacts of human activities including manufacturing processes. Machining by mechanical cutting is a fundamental manufacturing process and minimizing and eliminating its environmental impacts is vital. In this keynote paper, sustainability in the context of machining is identified. The resources used in machining have been categorized and the social and environmental impacts and potential methods to reduce them have been identified. The findings are critically discussed and the gaps and challenges for future research direction are highlighted. [ABSTRACT FROM AUTHOR]

In the modern world, composite materials have become more in demand and this is connected to the rapid development of mechanical engineering and aircraft construction. Most companies aim to improve the performance of their product and reduce the cost of manufacturing time, financing and production waste. The technology of manufacturing parts, assemblies and assembly units from composite materials is rapidly developing, but there are still a number of unresolved problems arising during their processing. The effect of delamination on the quality of the hole being processed is one of them. The quality of the opening is one of the main parameters of any design, how exactly this element can be used as a connecting element, which is responsible for the entire future assembly unit. When processing holes with a drill, a defect often occurs - delamination, which subsequently affects the quality and ability of the part to perform its functions. The problem of delamination in parts made of fibrous polymer composite materials is poorly studied. Therefore, the purpose of this article is to review existing problems and methods of solving them. Stratifications that occur during the processing of holes are mostly formed as a result of incorrectly selected cutting modes, namely feed (S) and tool rotation speed (V), which in turn leads to a number of defects: cracks, chips, pulling out fibers, loosening) and with the subsequent formation of delamination. This article examined how important and correct it is to assess the size and area of damage during delamination in order to understand how to prevent this process. Therefore, after analyzing scientific works and understanding the process of formation of delamination, namely its assessment and its distribution in the treated area, it is possible to understand that the assessment of the size of delamination should take into account the following factors: the maximum diameter of the damaged area, the number and density of interlayer cracks, the area of delamination and cutting modes . Optimizing cutting modes and improving the geometric shape of the cutting tool makes it possible to minimize or even eliminate the delamination process for parts made of SPCM. [ABSTRACT FROM AUTHOR]

The turning process is a widely used machining process, and its productivity has a significant impact on the cost and profit in industrial enterprises. Currently, it is difficult to effectively determine the optimum process parameters under complex conditions. To address this issue, a classification-based parameter optimization approach of the turning process is proposed in this paper, which aims to provide feasible optimization suggestions of process parameters and consists of a classification model and several optimization strategies. Specifically, the classification model is used to separate the whole complex process into different substages to reduce difficulties of the further optimization, and it achieves high accuracy and strong anti-interference in the identification of substages by integrating the advantages of an encoder-decoder framework, attention mechanism, and major voting. Additionally, during the optimization process of each substage, Dynamic Time Warping (DTW) and K-Nearest Neighbor (KNN) are utilized to eliminate the negative impact of cutting tool wear status on optimization results at first. Then, the envelope curve strategy and boxplot method succeed in the adaptive calculation of a parameter threshold and the detection of optimizable items. According to these optimization strategies, the proposed approach performs well in the provision of effective optimization suggestions. Ultimately, the proposed approach is verified by a bearing production line. Experimental results demonstrate that the proposed approach achieves a significant productivity improvement of 23.43% in the studied production line.

This paper systematically explores the applications of image processing techniques in machined surface analysis, a critical area in industries like manufacturing, aerospace, automotive, and healthcare. It examines the integration of image processing in traditional Computer Numerical Control (CNC) machining and micromachining, focusing on its role in tool wear analysis, workpiece detection, automatic CNC programming, and defect inspection. With AI and machine learning advancements, these technologies enhance defect detection, surface texture analysis, predictive maintenance, and quality optimization. The paper also discusses future advancements in high resolutions, 3D imaging, augmented reality, and Industry 4.0, highlighting their impact on productivity, precision, and challenges such as data privacy. In conclusion, image processing remains vital to improving manufacturing efficiency and quality control.

Featured Application: The results of the following design investigation can be used in the field of machining, particularly when developing new types of cutting tools and tooling. This paper describes the process of the design and verification of a milling tool and tooling that may contribute to the renouncement of the flood cooling method when mineral oils and oil-in-water emulsions are used as coolants. The proposed solutions are based on the idea of coolant supply in internal channels created inside of a cutting tool. As an alternative to the aforementioned mineral oil-based coolants, liquids with higher cooling efficiency and environmental friendliness (green coolants) were considered. Given coolants' possible lack of lubricating properties and negative (corrosive, etc.) influence on a machine tool's units, tooling delivers these coolants to the cutting tool and bypasses the standard machine tool's supply system. The geometry of the milling tool (a cutting insert with an internal channel) was tested in the framework of a stress simulation. To perform it, cutting force components F z , F y , and F x were determined empirically and then applied to the simulated area of contact between the tool and the workpiece. Based on the obtained principal stress values P 1 , P 2 , and P 3 , the factor of safety was calculated with the Mohr–Coulomb, P 1   m a x , and P 3   m i n failure criteria. The proposed milling tool, equipped with a novel type of labyrinth seal with no friction between its components, was experimentally tested to confirm its ability to maintain leak-tightness at different values of spindle speed (200~2000 rpm) and coolant supply volume (1.0~10.0 L/min). Based on the results of the stress simulation and the leak-tightness experiment, conclusions were drawn about further modernization and utilization prospects of the proposed milling tool and tooling design. [ABSTRACT FROM AUTHOR]