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In high-speed milling, the machining system is affected by a chatter resulting from the dynamic interaction between the tool and the part, which can cause harmful effects on the tool and the machined surface of the part. Chatter occurs more frequently for the milling of thin-walled parts due to their low stiffness. In addition, the dynamic characteristics of thin-walled parts vary along the tool path. The dynamics of the part are therefore the dominant factor that should be considered in the modeling and the study of the milling process and must be performed in 3D, where the third dimension is the tool position. This paper studies the milling stability of Al 7075-T6 thin-walled parts during high-speed milling considering the variation of dynamic characteristics and develops three-dimensional stability lobe diagrams of the spindle speed, axial depth of cut, and tool position. The dynamic equations of motion are solved numerically using semi-discretization method. Modal parameters of the tool and the part were extracted experimentally by modal tests. Then, cutting tests were conducted to validate the established model by measuring the machined surface roughness which is used as a criterion for detecting instability. The experimentally obtained results correspond well with the predicted stability limits. Moreover, the influence of different cutting parameters on the machining stability along the tool path was investigated. It is found that the variable spindle speed improves significantly the cutting process, and the best selection of feed per tooth impacts positively on the machined surface quality. [ABSTRACT FROM AUTHOR]

We introduce a comprehensive benchmark for local features and robust estimation algorithms, focusing on the downstream task—the accuracy of the reconstructed camera pose—as our primary metric. Our pipeline's modular structure allows easy integration, configuration, and combination of different methods and heuristics. This is demonstrated by embedding dozens of popular algorithms and evaluating them, from seminal works to the cutting edge of machine learning research. We show that with proper settings, classical solutions may still outperform the perceived state of the art. Besides establishing the actual state of the art, the conducted experiments reveal unexpected properties of structure from motion pipelines that can help improve their performance, for both algorithmic and learned methods. Data and code are online (https://github.com/ubc-vision/image-matching-benchmark), providing an easy-to-use and flexible framework for the benchmarking of local features and robust estimation methods, both alongside and against top-performing methods. This work provides a basis for the Image Matching Challenge (https://image-matching-challenge.github.io). [ABSTRACT FROM AUTHOR]

Optimization of cutting parameters in machining operations is a complex task requiring extensive knowledge and experience to reach the maximum potential for cost reductions in manufacturing. Through the work presented in this paper a cutting parameters optimization algorithm for roughing and finishing turning has been realized. The proposed optimization algorithm is based on a combination of linear search and binary search methods, heaving as objective criterion the minimization of the machining time. Examples for roughing and finishing turning have been presented to illustrate the application of the proposed algorithm and analyse the results. [ABSTRACT FROM AUTHOR]

The manufacturing industry faces a new revolution, grounded on the intense digitalization of assets and industrial processes and the increasing computation capabilities imposed by the new data-driven digital architectures. This reality has been promoting the Digital Twin concept and its importance in the industrial companies' business models. However, with these new opportunities, also new threads may rise, mainly related to industrial data protection and sovereignty. Therefore, this research paper will demonstrate the International Data Spaces reference model's application to overcome these limitations. Following a pilot study with a Portuguese machine producer/maintainer enterprise, this paper will demonstrate the development of a cutting and bending machine Digital Twin, leveraged on an International Data Spaces infrastructure for interoperability, for the plastic and metal industry and its importance to introduce this machine manufacturing company in a new business-to-business marketplace from the EU project Market 4.0. [ABSTRACT FROM AUTHOR]

This research paper explores the performance of electrorheological fluids, widely known as smart fluids, which are popular because they experience immediate changes in their characteristics when applied to exterior magnetic or electric field. On application of high voltage, the ER fluid alters its form to semisolid from viscous liquid within a few milliseconds, which is reversible. The reversible nature of ER fluid was employed to absorb vibrations during hard machining. In this research, the consequence of the application of electric field to the ER characteristics of numerous non‐Newtonian fluids with an accumulation of TiO2 and Al2O3 nano elements are investigated. The purpose of this research is to discover the ER sample, which has the highest viscosity, highest breakdown voltage, but less sedimentation. So the selected sample can be used in ER dampers for the purpose of shock absorption. The ER fluid behaves as a mainspring with nonlinear vibration features that are managed by an arrangement of ER fluid, the structure of plunger, and the constraints of applied electric field. In this research, we have employed steel metallic specimens made from EN24T of 302BHN and the machining experiments are conducted to attain the shape parameters that can reduce the tool vibration and help improve the process of cutting at the time of machining with minimum application of fluid utilizing hard metal inserts. In this work, cutting tool parameters were analyzed like cutting force, tool wear, tool vibration amplitude, and surface roughness. It was observed that ER damper has shown its better performance at 5KV. At 5KV, cutting force was the least, tool wear was the least, tool vibration amplitude was the least, and surface roughness was the least. The use of an ER fluid damper decreases tool vibrations and successfully enhances machining performance. The machining industry will be benefited with commercialization of this technique. [ABSTRACT FROM AUTHOR]

Rock abrasivity influences wear of cutting tools and consequently, performance of mechanized tunneling machines. Several methods have been proposed to evaluate rock abrasivity in recent decades, each one has its own advantages. In this paper, a new method is introduced to estimate wear of disc cutters based on rock cutting tests using scaled down discs (i.e. 54 and 72 mm diameter). The discs are made of H13 steel, which is a common steel type in producing real-scale discs, with hardness of 32 and 54 HRC. The small-scale linear rock cutting machine and a new abrasion test apparatus, namely University of Tehran abrasivity test machine, are utilized to perform the tests. Tip width of the worn discs is monitored and presented as the function of the accumulated test run to classify the rock abrasion. Abrasivity tests show that by increasing the uniaxial compressive strength (UCS) of the rock samples, wear rate is doubled gradually that reveals the sensitivity of the test procedure to the main parameters affecting the abrasivity of hard rocks. For the rocks with the highest UCS, the normal wear stops after performing 5 to 10 rounds of the tests, and then, deformation of the disc tip is detectable. Two abrasivity indices are defined based on the abrasivity tests results and their correlations with Cerchar Abrasivity Index (CAI) and UCS are established. Comparison of the established correlations in this study with previous investigations demonstrates the sensitivity of the indices to the parameters affecting wear of the disc cutters and repeatability of the outputs obtained from abrasivity tests using scaled down discs. Findings of this study can be used to enhance the accuracy of rock abrasivity classifications. [ABSTRACT FROM AUTHOR]

Being a difficult-to-cut material, Fiber Metal Laminates (FML) often pose challenges during conventional drilling and require judicious selection of machining parameters to ensure defect-free laminates that can serve reliably during their service lifetime. Helical milling is a promising technique for producing good-quality holes and is preferred over conventional drilling. The paper compares conventional drilling with the helical milling technique for producing holes in carbon fiber-reinforced aluminum laminates. The effect of machining parameters, such as cutting speed and axial feed, on the magnitude of cutting force and the machining temperature during conventional drilling as well as helical milling is studied. It was observed that the thrust force produced during machining reduces considerably during helical milling in comparison to conventional drilling at a constant axial feed rate. The highest machining temperature recorded for helical milling was much lower in comparison to the highest machining temperature measured during conventional drilling. The machining temperatures recorded during helical milling were well below the glass transition temperature of the epoxy used in carbon fiber prepreg, hence protecting the prepreg from thermal degradation during the hole-making process. The surface roughness of the holes produced by both techniques is measured, and the surface morphology of the drilled holes is analyzed using a scanning electron microscope. The surface roughness of the helical-milled holes was lower than that for holes produced by conventional drilling. Scanning electron microscope images provided insights into the interaction of the hole surface with the chips during the chip evacuation stage under different speeds and feed rates. The microhardness of the aluminum layers increased after processing holes using drilling and helical milling operations. The axial feed/axial pitch had minimal influence on the microhardness increase in comparison to the cutting speed. [ABSTRACT FROM AUTHOR]

Scheduling jobs and tools is a significant problem for manufacturing systems. Inefficient job scheduling and tool loading planning may result in the underutilization of capital-intensive machines and a high machine idle time. Tools are transferred by a tool transporter (TT) between machines. Therefore, efficient scheduling of jobs, TT, and the tools with alternate machines enables a manufacturing system to increase machines' utilization and decrease their idle times. This paper addresses machines, TT, and tools concurrent scheduling with alternate machines and only one copy of every tool variety is made available where the tools are costly, in a multi-machine flexible manufacturing system, taking into account tool transfer times for makespan (MS) minimization. The tools are placed in a central tool magazine, which shares and serves them to many machines to cut down the price of duplicating the tools in each machine. The problem is to assign machines from alternate machines, tools, and corresponding TT trips, including the deadheading trip and loaded trip, to job operations for MS minimization. This paper uses a nonlinear mixed-integer programming framework to present the problem, and a flower pollination algorithm (FPA) is employed to solve it. The results show that FPA outperforms the Jaya algorithm, and the usage of alternate machines for the operations reduces the MS. Reduction in MS indicates an improvement in utilization of resources. [ABSTRACT FROM AUTHOR]

In laser beam processing, the angle or offset between the auxiliary gas and the laser beam axis have been proved to be two new process optimization parameters for improving cutting speed and quality. However, a traditional electromechanical actuator cannot achieve high-speed and high-precision motion control with a compact structure. This paper proposes a magnetic levitation actuator which could realize the 5-DOF motion control of a lens using six groups of differential electromagnets. At first, the nonlinear characteristic of a magnetic driving force was analyzed by establishing an analytical model and finite element calculation. Then, the dynamic model of the magnetic levitation actuator was established using the Taylor series. And the mathematical relationship between the detected distance and five-degree-of-freedom was determined. Next, the centralized control system based on PID control was designed. Finally, a driving test was carried out to verify the five-degrees-of-freedom motion of the proposed electromagnetic levitation actuator. The results show it can achieve a stable levitation and precision positioning with a desired command motion. It also proves that the proposed magnetic levitation actuator has the potential application in an off-axis laser cutting machine tool. [ABSTRACT FROM AUTHOR]

The low loading rate of the thin coal seam shearer drum is a severe obstacle to the efficient mining of thin seam resources, and the auxiliary drum loading through the cowl is an effective measure to alleviate this situation. However, the working mechanism of the coal cowl still remains unclear. In this paper, with the help of the discrete element method and the modeling experiment method, the effects of coal cowl's offset distance, tilt angle and wrap angle on the coal loading rate under different loading modes of the drum are investigated; and the significance of various factors and their interactions to the drum coal loading rate is explored by designing response surface experiments. The findings show that a monotonous negative correlation between the offset distance of the coal cowl and the coal loading rate is identified, and that a smaller offset distance can effectively improve the coal loading rate of the drum. The conveying torque is significantly increased, easily inducing the drum choking, coal recycling coal over-crushing. Along with the increasing tilt angle, the rate of ejection loading decreases monotonically, and the rate of pushing loading increases first and then decreases. Coal loading rate is weakly affected by changes in coal cowl's wrap angle. The results of response surface analysis reveal that the most significant factors affecting the drum's coal loading rate are tilt angle and offset distance in ejection and pushing loading modes, respectively. The conclusions drawn here offer implications for improving the coal loading performance of the thin coal seam shearer drum, as well as certain guidance on the optimal design of coal cowl parameters. [ABSTRACT FROM AUTHOR]

This paper refers to the topic of determining the kerf value and axis accuracy of a CNC plasma cutter for the cutting of 2, 4 and 6 mm thick plates of construction steel, aluminum and stainless steel. After the thorough description of the researched materials, a detailed experiment plan was created in the Design-Expert software package. After cutting the test samples the data were measured. Upon completion of the cutting, all test samples were measured, and the data regarding their dimensional deviations on x and y-axis and bore diameter deviations was tabularly shown and analysed with the Design – Expert software. Statistical analysis of the measured data was made so that the optimal kerf values and the equations which describe dimensional deviations for each material and thickness could be made, based on the dimensional deviation of the test samples. Along with the kerf values, analysis has also given insight in the accuracy of the x and y-axis of the machine. Finally, an algorithm for optimizing multiple criteria was utilized to determine the ideal kerf value for each material and thickness. The objective was to identify the precise kerf value that results in the highest possible accuracy for the dimensions of the workpiece in both the x and y directions, as well as for the bore diameter. [ABSTRACT FROM AUTHOR]

Conveying and baling are two important links in the mechanized harvesting of ramie, in the face of ramie cutting and baling harvesting technology research gaps, low stalk conveying rate, high breaking rate and other problems. In this paper, according to the technical requirements of ramie harvesting, we designed a conveying and baling device, the hand-held ramie cutter. First, the key mechanism of the conveying and baling device of the equipment was designed. Then, we analyzed the location of stem clogging and the reasons for the breaking problem during the conveying and baling process. The field harvesting experiments were carried out according to the principles of Box–Behnken experimental design. Taking the machine travelling speed, conveying speed and ramie raking frequency as the test factors and using the Design-Expert V8.0.6.1 to process the data, we established a regression model for each experimental factor on the conveying rate and breaking rate. The order of influence of several factors on the breaking rate is: X2 > X1 > X3; and the effects of the three factors on the conveying rate were X3 > X2 > X1. Through response surface analysis (RSA), the effects of the factors on the indicators were explained, as was the impact of the factors on the indicators. Finally, the parameter optimization was carried out with the delivery rate as the core index. The best combination of motion parameters was obtained as follows: the travelling speed was 0.37 m/s, the chain conveying speed was 1.1 m/s, and the raking frequency was 144 times/min. With the combination of parameters under the field test verification, the results show that compared with the original work quality, the stalk delivery rate increased from 85.2% to 93% (an increase of 7.8%), the stalk breaking rate fell from 31.1% to 20.4% (a decrease of 10.7%). The performance of ramie harvesting and baling was greatly improved, and we achieved relatively satisfactory results. [ABSTRACT FROM AUTHOR]

Predictive maintenance (PdM) has tremendous potential for reducing total operational costs and turnaround time in industrial manufacturing and maintenance services. In recent years, many deep learning based prediction methods have emerged and advanced the state-of-the-art in the PdM area. This paper proposes a holistic tool wear prediction framework that orchestrates convolutional neural networks for multisensor sequence learning, synthetic features to augment small training datasets, and a meticulously designed training regulation strategy. The effectiveness of the proposed framework is evaluated with computer numerical control (CNC) machine milling datasets available in the public domain. The results show that our method, sCNN-Ex, outperforms state-of-the-art tool wear prediction methods. sCNN-Ex reduces the prediction mean absolute percentage error from existing 32% to 21.7% for steel cases in the NASA milling dataset. [ABSTRACT FROM AUTHOR]

DSS has low machinability characteristics due to its high strength, machining is complicated, and careful attention is required when selecting machining parameters. The main criteria discussed in this paper concern the turning optimization parameters and machining time reduction of DSS 2205 as the work material. The input parameters are cutting velocity, feeds, cutting depth, and tooltip nose radius of the cutting tool. The design of experiments methodology is employed to design the experiments using Design-Expert V12 software. The second-order mathematical model was developed, and analysis of variance was performed to analyze the performance characteristics to recognize the critical variables influencing the output parameter. An artificial neural network (ANN) backpropagation algorithm using MATLAB software was used to develop the mathematical model and optimize the output. The model was developed, and the results were optimized using MATLAB software's ANN back propagation method to find the best possible solutions. The generated models were significant based on the analysis of variance and the R-squared value, and these results indicate that the cutting velocity is the most critical factor. For a low machining time, the cutting velocity should be between 100 and 140 m/min, and the tooltip nose radius should be 2.8 mm. The optimal parameter settings are validated by performing a lower is better confirmation test using gray relational analysis (GRA). The GRA exposed the lower machining time at a cutting velocity of 140 m/min, rate of feed of 0.5 mm/rev, cutting depth of 0.5 mm, and tooltip nose radius of 2.4 mm. The predicted values were close to the experimental values, and the result indicates the optimal level of the highest GRA grade of the machining variable. [ABSTRACT FROM AUTHOR]

This research paper looks at the surface quality acquired during the laser cutting of metallic materials, with an emphasis on S235JR. We conducted studies on a FiberMak Momentum Gen-2 laser cutting machine to determine the effect of process settings on surface quality. We varied laser beam power and feed rate while keeping other parameters constant, such as nozzle diameter, CO2 pressure, and focal length. Results showed that surface quality is significantly influenced by these parameters. We identified two distinct zones on the cut surface: one with favorable roughness and another with less favorable roughness. Overall, increasing laser beam power led to improved surface quality, while increasing the feed rate resulted in a degradation of the surface. By analyzing the experimental data, we identified an optimal parameter combination for achieving high surface quality. These discoveries have practical applications in business and help to a theoretical knowledge of the laser cutting process. [ABSTRACT FROM AUTHOR]

Corner cleaning is the most important manufacturing step of window framing to ensure aesthetic quality. After the welding process, the current methods to clean the welding seams lack quality control and adaptability. This increases rework, cost, and the waste produced in manufacturing and is largely due to the use of CNC cutting machines, as well as the reliance on manual inspection and weld seam cleaning. Dealing with manufacturing imperfections becomes a challenging task, as CNC machines rely on predetermined cleaning paths and frame information. To tackle such challenges using Industry 4.0 approaches and automation technology, such as robots and sensors, in this paper, a novel intelligent system is proposed to increase the process capacity to adapt to variability in weld cleaning conditions while ensuring quality through a combined approach of robot arms and machine vision that replaces the existing manual-based methods. Using edge detection to identify the window position and its orientation, artificial intelligence image processing techniques (Mask R-CNN model) are used to detect the window weld seam and to guide the robot manipulator in its cleaning process. The framework is divided into several modules, beginning with the estimation of a rough position for the purpose of guiding the robot toward the window target, followed by an image processing and detection module used in conjunction with instance segmentation techniques to segment the target area of the weld seam, and, finally, the generation of cleaning paths for further robot manipulation. The proposed robotic system is validated two-fold: first, in a simulated environment and then, in a real-world scenario, with the results obtained demonstrating the effectiveness and adaptability of the proposed system. The evaluation of the proposed framework shows that the trained Mask R-CNN can locate and quantify weld seams with 95% mean average precision (less than 1 cm). [ABSTRACT FROM AUTHOR]

Precision geometry measurement of complex cutting tools has a significant impact on the machinability of components in the industry. However, acquiring high-quality images in machine vision is a challenging problem due to the large slope and complex geometry. In view of the vital function of illumination, this paper proposed a method of optimal design for the LED array. First, the calculation model of irradiance distribution on the measurement plane is established, and the properties of the reflected light from the surface of cutting tools are analyzed based on the BRDF theory. Then, the optimal parameters are solved through the specific algorithm flow for the LED array design. Finally, a flexible LED light source is fabricated with the optimal parameters for different features of the cutting tools and used in the measurement. The measurement results show that the error of the optimized light source is less than 1%, and compared with the off-the-shelf light, the measurement accuracy is improved by 9.5% on average. Moreover, this method also presents the potential applied to other complex objects. [ABSTRACT FROM AUTHOR]

A major problem in the high-speed cutting process of machine tools is tool wear. Tool wear directly affects the surface quality and machining accuracy of the workpiece. However, the limits of fusing multiple sensing signals to indirectly monitor tool wear are rarely concerned in real manufacturing environments. In this paper, a tool wear identification method based on a single sensor signal is proposed. To solve the limits of less obtained information and poor anti-interference ability of single sensor, multi-domain feature fusion strategy is established. By establishing a hybrid model of deep convolutional neural network and stacked long short-term memory network, the complex mapping relationship between fusion features and tool wear is constructed. Specifically, the spatial features of the input data set are extracted by the convolution kernel of the deep convolutional neural network. Then, a stacked double-layer long short-term memory neural network is established to capture sequence features with long-term dependence, thereby identifying tool wear. Finally, the superiority of the developed method is verified by tool wear experiments. The results show that the method can be effectively applied to tool wear identification from single sensor signals, and the mean RMSE and MAE of the identification results are 9.43 and 7.15, respectively. Compared with four other traditional multiple regression methods, RMSE and MAE are reduced by 73.0% and 78.7% on average. This study provides a reference value for the industrial implementation of tool wear monitoring system. [ABSTRACT FROM AUTHOR]

Vibrations are limiting the productivity and the process quality of cutting machine tools. For the monitoring of these vibrations, often external sensors, such as acceleration sensors, are used. These external systems require additional cost and maintenance effort. This paper presents a virtual sensor, which is capable of detecting vibrations at the tool center point, based on internal machine data. External sensors are only necessary once for model identification. This reduces the overall cost of the system significantly. The virtual sensor uses the high-quality data of the linear position encoder near the ball screw nut and calculates the vibrations at the tool tip by using transmissibility functions. This paper explains the theory behind the used transmissibility functions and describes how they are measured, by comparing different experimental approaches to identify the modal parameters of cutting machine tools. After the identification of the sensor, a dynamical test cycle is used to prove the physical correctness. [ABSTRACT FROM AUTHOR]

The grafting machine cutting step is the core step of the grafting process. The existing grafting machine cutting mechanism adopts fixed angle cutting and manually adjusts the cutting angle based on experience, and the cutting angle is not definite for rootstock cutting in different growth periods. In this paper, we proposed a method to guide the precise cutting mechanism according to internal and external parameters of melon rootstock at a specific period. First, we constructed a cutting model based on internal and external characteristics of rootstock cutting in the growth period of "two leaves and one core" and clarified the safe cutting area. Second, we designed a rotary precision cutting mechanism for rootstock cutting with automatic angle adjustment and constructed the displacement equation of the cutting trajectory of the cutter according to the cutting model. Last, we examined the cutting effect of the precision cutting mechanism and determined the precise cutting angle of the rootstock cutting mechanism in the growth period. Finally, the cutting effect of the precision cutting mechanism was examined, and the precision cutting angle of the rootstock during the growth period was determined. A comparison test between the precision cutting mechanism and the traditional cutting mechanism was carried out, and visual images of the incision were captured and analyzed. The results show that under the five horizontal cutting angles in the safe cutting area of rootstock, the length of the cut surface is inversely proportional to the cutting angle, and the flatness of the cut surface is directly proportional to the cutting angle. Comprehensive evaluation of the length of the cut surface, the cutting success rate, and the quality of cutting revealed that the average cutting angle of the precision cutting mechanism in the safe cutting area (26°) is better than that of the traditional cutting mechanism. It meets the technical requirements of the cutting technology for mechanically grafted rootstocks. The results provide a reference for studying new rootstock precision cutting mechanisms and cutting angle adaptive control models. [ABSTRACT FROM AUTHOR]

Cr-Co-Mo-Ni high-hardness steel is widely used in critical mechanical transmission components. It has the difficult-to-cut characteristic because of its high hardness and high strength. In the cutting process, the machining cutting force is large, the tool is worn quickly, and the machining accuracy is poorly maintained. It has become one of the crux of restricting the improvement of quality and production efficiency of innovative products. In order to solve the above problems, this paper carried out research on the electrochemical machining process to achieve high-quality and efficient machining of high-hardness gear steel splines. Firstly, the process characteristics of ECM of internal spline are analyzed. The electrochemical field model is established by the multi-physical field coupling simulation platform, and the effects of the main electrolytic process parameters on the forming accuracy were numerically simulated and analyzed. Meanwhile, the three-dimensional simulation model of electrolyte flow field in the inner cavity of the electrolytic fixture is established, and the influence of electrolyte flow direction and electrolyte back pressure on the flow field distribution is explored. The platform of electrochemical machining system is set up. An experimental study on electrochemical machining of involute internal splines is carried out to further optimize the electrochemical machining process parameters. The test results showed that the electrolysis accuracy and efficiency of the 41-tooth involute spline workpiece are optimized when the electrolyte is 8% NaNO3, the back pressure of the electrolyte is 0.2 MPa, the feed speed is 1.8 mm/min, and the processing voltage is 16.0 V. The machining accuracy of the method proposed in this paper meets the actual requirements of actual gear application. Compared with the traditional slotting process, the machining efficiency is improved by 62.5%, the tool wear is reduced, and the machining accuracy retention is improved. It has important application value for advanced machining technology of high-performance materials. [ABSTRACT FROM AUTHOR]

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Manufacturing parts made of thin steel in small batches is a challenging task in terms of reaching the proper balance between the productivity, the cost, and the dimensional precision. This paper presents the results of experimental research about manufacturing electrical steel thin parts using abrasive waterjet cutting. For a certain increase of productivity and a more efficient process, the parts were cut using multilayer packages of steel strips. The main objective was to analyze the influence of the number of layers on the dimensional precision of parts. Preliminary tests were performed, followed by a full factorial experiment using two independent parameters, the number of layers and the traverse speed. The parts were measured on a noncontact vision measurement machine and mathematical models were determined to predict the parts deviations depending on the independent parameters used. A practical validation of the models was performed. The main conclusion is that the number of layers has a certain influence on the accuracy of dimensions, but this influence can be predicted with a satisfactory level of confidence using mathematical models. [ABSTRACT FROM AUTHOR]

With the continuous development of the manufacturing industry, the proportion of deep hole machining in mechanical processing is increasing. In 304 stainless steel, as a kind of difficult machining material, its deep hole machining demand is also increasing year by year. Due to the physical characteristics of 304 stainless steel and the uncontrollability of gun drilling, the quality of deep hole processing is relatively poor. In this paper, some experimental studies are carried out to solve this engineering problem. Firstly, by analyzing the structure of the gun head, this paper establishes the stress model of the gun head in the processing process. Then, the accuracy of the theory is verified by experiments. Secondly, this research carried out multiple single-factor drilling experiments to change the front angle of the internal and external edges of the gun drill to control the cutting force in the machining process. Based on the analysis of the test results, the influence of the change of the gun drill's internal and external edge angle on the surface quality of hole wall, chip forming, and hole straightness offset is explored. The results show that the increase in the internal angle of a gun drill can promote the surface roughness of the hole wall and chip forming. And properly increasing the external angle can slow down the straightness offset of the hole. Finally, for the deep hole processing of 304 stainless steel, this paper gives the best edge angle selection of gun drill. [ABSTRACT FROM AUTHOR]

Machining is an indispensable manufacturing process for a wide range of engineering materials, such as metals, ceramics, and composite materials, in which the tool wear is a serious problem, which affects not only the costs and productivity but also the quality of the machined components. Thus, the modification of the cutting tool surface by application of textures on their surfaces is proposed as a very promising method for improving tool life. Surface texturing is a relatively new surface engineering technology, where microscale or nanoscale surface textures are generated on the cutting tool through a variety of techniques in order to improve tribological properties of cutting tool surfaces by reducing the coefficient of friction and increasing wear resistance. In this paper, the studies carried out to date on the texturing of ceramic and superhard cutting tools have been reviewed. Furthermore, the most common methods for creating textures on the surfaces of different materials have been summarized. Moreover, the parameters that are generally used in surface texturing, which should be indicated in all future studies of textured cutting tools in order to have a better understanding of its effects in the cutting process, are described. In addition, this paper proposes a way in which to classify the texture surfaces used in the cutting tools according to their geometric parameters. This paper highlights the effect of ceramic and superhard textured cutting tools in improving the machining performance of difficult-to-cut materials, such as coefficient of friction, tool wear, cutting forces, cutting temperature, and machined workpiece roughness. Finally, a conclusion of the analyzed papers is given. [ABSTRACT FROM AUTHOR]

Machining-induced residual stress is an important measure of surface integrity. The residual stress affects the quality and performance of the workpiece. In the process of cutting, it often encounters the situation of multi-pass cutting. The formation of residual stress in the process of single- or multi-pass cutting has been widely studied, but few researchers consider the same total radial cut depth, divided into single-pass cutting and multi-pass cutting, and the last cutting in the process of multi-pass cutting is less studied. The difference between single-pass cutting and multi-pass cutting is that the former machining of cutting is used as the initial state of the latter machining. The stress/strain and temperature generated on the surface of the former machining can affect the subsequent cutting force, temperature, plastic deformation, and the final residual stress. This paper studies the distribution of cutting force and residual stress generated by single- and multi-pass cutting of Al-7075-T6, and focuses on the analysis of the multi-pass cutting strategy. The three-dimensional finite element method is used to simulate, and the experimental results are in good agreement with the simulation results. The results show that compared with single-pass cutting, multi-pass cutting can reduce the surface tensile stress and the magnitude and depth of underground residual compressive stress, and the effect of decreasing method is more obvious when multi-pass cutting. The last machining is affected by the previous cutting. When the next radial cutting depth is greater than the plastic deformation depth produced by the previous cutting, the next radial cutting depth is the same, and the trend of residual stress is approximately the same. The results can guide the optimization of residual stress in the multi-pass cutting strategy. [ABSTRACT FROM AUTHOR]

This paper presents the results of an assessment of the morphological characteristics of semi-finished wood-fibre products obtained from waste fibreboard using a rotary cutting machine by dry grinding. The work has established the influence of machine design parameters, such as the gap between the rotor and stator cutters and the angle of the stator cutter contact with the raw materials, on the mass fraction of small fibres and fines in wood-fibre pulp. These parameters determine the main structural characteristics of boards and ensure fibre bonding. The paper describes the collision of single secondary wood fibres that leads to the development of primary cracks, contributing to external and internal fibrillation in the absence of high temperatures and pressure without using chemical additives or water and steam. [ABSTRACT FROM AUTHOR]

This paper presents a novel cutting fluid monitoring sensor system and a description of an algorithm framework to monitor the state of the cutting emulsion in the machine tool sump. One of the most frequently used coolants in metal machining is cutting emulsion. Contamination and gradual degradation of the fluid is a common occurrence, and unless certain maintenance steps are undertaken, the fluid needs to be completely replaced, which is both un-economical and non-ecological. Increasing the effective service life of the cutting emulsion is therefore desired, which can be achieved by monitoring the parameters of the fluid and taking corrective measures to ensure the correct levels of selected parameters. For this purpose, a multi-sensor monitoring probe was developed and a prototype device was subsequently created by additive manufacturing. The sensor-carrying probe was then placed in the machine tool sump and tested in operation. The probe automatically takes measurements of the selected cutting emulsion properties (temperature, concentration, pH, level height) in set intervals and logs them in the system. During the trial run of the probe, sensor accuracy was tracked and compared to reference measurements, achieving sufficiently low deviations for the purpose of continuous operation. [ABSTRACT FROM AUTHOR]

Titanium alloy is an indispensable material in many industrial fields owing to its excellent strength, corrosion resistance, and heat resistance. Cutting is an important process in the manufacturing of titanium alloy; thus, it is necessary to monitor titanium alloy cutting, especially the monitoring of cutting tools. Since traditional monitoring is realized through operators, it is affected by the operator's technology and cannot keep up with the modern-day precision manufacturing of titanium alloy. With recent technological developments, more and more attention is being paid to online monitoring, particularly by utilizing the field of machine learning to predict tool life. Accordingly, this paper first describes the challenges encountered in titanium alloy cutting. Then, it introduces the characteristics of several machine learning models and their application in monitoring titanium alloy cutting, along with a discussion on the advantages and disadvantages of such algorithms. Finally, an outlook on the future prospects of machine-learning-enabled monitoring of titanium alloy cutting is provided. [ABSTRACT FROM AUTHOR]

The evolution of materials of cutting tools and their geometry, is currently leading to a technological development of many other sectors linked to machining. It is, therefore, a progress in the construction of machine tools, the machining of new materials, improvement of cutting fluids (lower toxicity and costs related to the maintenance and treatment of used cutting fluids, chemical formulation). Regarding the cutting fluids, international regulations are moving towards eco-elimination or limiting the use of certain molecules for ecological reasons. In addition, the formulation must be adapted as best as possible to the industrial demand, both in terms of performance and costs. The characteristics of nanofluids meet all these requirements and seem to be a promising solution for major problems encountered when using conventional cutting fluids. The present paper reports a literature review emphasizing essentially the investigations performed during the last 2 years. This literature focuses on the use of nano-lubricant in machining processes such as turning, milling and grinding processes. Analyzing the current review, it has been found that the use of nanofluids especially hybrid ones reduces the tool wear, the surface roughness, cutting forces, and heat generation. However, the optimal choice of nanoparticles and their concentrations that suites more the most severe cutting conditions, needs more attention in future works. [ABSTRACT FROM AUTHOR]

Wear-resistant coatings improve the machining capability of cutting tools and extend their useful life. However, when a tool needs to be reused, it is mandatory to remove the existing coating to facilitate resharpening and recoating. The existing technique uses electrochemical stripping, which is hazardous to the environment. The environmentally friendly pulsed laser stripping causes the melting and mixing of tools and coating materials, which makes it difficult to separate and remove the coating. This paper presents the results of coating stripping via a beam of fast argon atoms. Due to the twentyfold compression of the beam, a 3 µm thick AlTiN coating was removed from a rotating solid carbide end mill within 25 min. A subsequent one-hour-long irradiation of the cleaned tool with the same beam led to a decrease in the radius of the tool's cutting edges from 10.5 to 3.5 µm. This allowed us to redeposit a 3.5 µm thick AlTiN coating and obtain a coated end mill with a cutting-edge radius of 7 µm. [ABSTRACT FROM AUTHOR]

Gob‐side entry formation by roof cutting is a new technology for no pillar coal mining, which can maximize coal resources and reduce roadway drivage ratio. However, the mechanical behavior of the formed entry is complex while it is crucial to ensure the stability of the entry for mining safety. This paper proposed a machine learning‐based method for predicting the stability of the formed entry, which combined artificial neural network (ANN) with particle swarm optimization (PSO) algorithm or genetic optimization (GO) algorithm. The data set from 75 coal mining faces from 2009 to 2022 was employed to train and test the models. A descriptive variable of dynamic unstable distance was introduced to evaluate the stability state of the formed entry and six other parameters were chosen as influence parameters. The two intelligent models were compared with each other to have a comprehensive assessment. Model assessment indices such as R2, mean absolute error, mean absolute percentage error, and root mean square error were used to evaluate the accuracy of the models. The results of both developed models are promising, and the predictive accuracy of the PSO‐ANN model is higher than that of the GO‐ANN model. Through sensitivity analyses, it has been found that the coal seam thickness and roof rock hardness are the most important parameters for influencing entry stability. The developed method provides a practical tool for the prediction of entry stability and the optimization of entry design. [ABSTRACT FROM AUTHOR]

Dynamic reliability evaluation on foundation of stone cutting machine is usually affected by ambient background noise. In this paper, a nested evaluation approach of dynamic reliability is conducted for handling this complex situation. After getting the detected data, a fast identified process is available for fastly identifying the dynamic parameter foundation of stone cutting machine by using the peak-picking method, in which the natural frequency of foundation is able to be first identified by the equivalent frequency response function for the self-power spectrum of the foundation; subsequently, the damping ratio of the foundation, on the basis of the previous result of natural frequency, is identified by the half-power broadband technology; eventually, after obtaining these dynamic parameters identified, the failure probability of the foundation and its corresponding dynamic reliability index, under ambient excitation, is capable to be calculated based on the first-passage probability of the maximum response. An example of stone cutting machine foundation with engineering background is investigated in detail, and its result diagrams calculated by the proposed approach can be demonstrated that the foundation of stone cutting machine is safe and reliable in the current environment. The proposed method, in this paper, provides an effective path for evaluating the foundation reliability of stone cutting machine under ambient excitation. [ABSTRACT FROM AUTHOR]