Accurate prediction of tool life is crucial for reducing production costs and enhancing quality in the machining process. However, such predictions often rely on empirical knowledge, which may limit inexperienced engineers to reliably obtain accurate predictions. This study explores a method to predict the tool life of a cutting machine using servo motor current data collected during the initial stages of tool wear, which is a cost-effective approach. The LightGBM model was identified as suitable for predicting tool life from current data, given the challenges associated with predicting from the average variation of current values. By identifying and utilizing the top 50 features from the current data for prediction, the accuracy of tool life prediction in the early wear stage improved. As this prediction method was developed based on current data obtained during the very early wear stage in experiments with square end-mills, it was tested on extrapolated data using different end-mill diameters. The findings revealed average accuracy rates of 71.2% and 69.4% when using maximum machining time and maximum removal volume as thresholds, respectively.

This study investigates the influence of various cone cavity sizes on the flow characteristics and vibration behavior of the gas film flow field in aerostatic bearings with conical chambers under operational conditions. Utilizing a fluid-structure coupling approach, the vibration performance of the conical cavity shaft under external excitation is analyzed. Experimental tests are conducted to evaluate the vibration characteristics of the conical cavity bearing. To facilitate this, an aerostatic bearing test bench is designed to assess the shaft's vibration response under load impacts. The experimental results are compared with simulation outcomes to validate the reliability of the simulation model. The findings indicate that when the height of the conical cavity is 0.1 mm with a 50° angle, there is no supersonic airflow present in the bearing's internal flow field, and the vortex effect is minimal. Conversely, at a conical cavity height of 0.2 mm and a 50° angle, the amplitude of the shaft's response to external excitation is reduced, and the response time is notably short. Overall, the experimental results align well with the trends observed in the simulation data. This research offers valuable theoretical insights for the high-precision machining of aerostatic bearings.

The disc cutter is a main cutter to break rock in the Tunnel Boring Machine (TBM), but its abnormal state will increase the wear and may even cause the damages to the cutter head. But the cutter wear can only detect the limit of tool use, Therefore, real-time monitoring of the rotational speed of the disc cutter has become an urgent technical problem needed to be solved for TBM. This paper analyzes the disc cutter’s structural feature, and the periodic characteristics of disc cutter presented in the rotation process. Then, a monitoring system of disc cutter rotation speed is proposed based on the wear of the disc cutter detected by the eddy current sensor. The wear signal of disc cutters is sampled at a certain period for a long time. Through Fast Fourier Transform (FFT), the frequency character of wear signal is extracted. The rotational speeds of actual disc cutters under different installation distances and different rotational speeds are verified by similar disc cutters in the laboratory. In an engineering project, the rotational speeds of two edge disc cutter on an atmospheric cutter head with a diameter of 15 meters is monitored to verify the method. Results show that the cutter speed could be detected accurately and effectively when the wear amount is less than 20mm.

To realize a human-centered society, it is essential to develop advanced display technology that can artificially recreate tactile sensations for users. However, because the mechanoreceptors in human skin are microscopic, it is challenging to stimulate these receptors with currently available actuators. Thus, to cover the limitations of current actuators, we focused on the tactile illusion known as the velvet hand illusion (VHI), in which a sensation of smoothness occurs when people rub two parallel wires between the palms of their hands. In addition, it is known that a smooth sensation similar to the VHI can be induced using a dot matrix display when two lines composed of pins move in an apparent reciprocating motion on the palm. It is interesting that this smooth sensation is created by the uneven stimulation of the pins. To apply this sensation, it is necessary to elucidate the mechanism behind it. Therefore, in this study, we conducted psychophysical experiments and brain function measurements to determine whether this sensation produced by a dot matrix display is the VHI, and to clarify how sensations differ between displays. First, in the psychophysical experiments, we used the semantic differential method and factor analysis to construct tactile dimensions to investigate the characteristics of the VHI sensation. As a result, it was found that the stronger the VHI, the greater the sense of smoothness and softness. This characteristic was also confirmed in the VHI induced by the dot matrix display. Next, we used functional near-infrared spectroscopy to investigate brain activation related to VHI generation. The investigation confirmed that the strength of the illusion is related to the activity level of the hand area in the left primary somatosensory cortex for both the wire display and the dot matrix display.

This paper presents an optimization of energy distribution in hydraulic hammer forging based on the three actuals which are (1) actual place, (2) actual product and (3) actual situation. For the automation in the hydraulic hammer forging, “billet jumping” during the forging process is a fatal defect. It is much difficult to simulate the billet jumping through numerical simulation. Thus, it is important to determine the optimal energy distribution without using numerical simulation. To successfully determine the optimal energy distribution in hydraulic hammer forging, first, several experiments based on the latin hypercube design sampling are conducted. Then, multi-objective optimization minimizing total energy and billet thickness is performed. In this process, the billet jumping is numerically evaluated through the experiments and is handled as the design constraint. Surrogate-based optimization using radial basis function network is performed to determine the optimal energy distribution considering the billet jumping. Through the experimental result, the hydraulic hammer forging using the optimal energy distribution is successfully conducted without the billet jumping.

Low-frequency vibration cutting (VC) is a machining technology wherein a tool periodically vibrates to break cutting chips. However, VC produces tool paths that include air cuts to break up chips, which deteriorates surface roughness and roundness. This study aims to address these limitations. Low-frequency vibrations are applied to both the upper and lower turrets of a multitasking lathe. To improve surface roughness with simultaneous VC, this study presents a theoretical method to calculate the optimal values for frequency and amplitude of VC and the starting positions of the Z-axis of two upper and lower turrets. Simulations and actual machining experiments were conducted after adjusting VC frequency and amplitude and the starting positions of Z-axis of both turrets. Results show that the surface roughness improves with two-turret simultaneous VC compared to one-turret single VC in stainless steel and brass machining experiments, while succeeding in breaking up cutting chips. Furthermore, the surface roughness of simultaneous VC is improved to a level close to that of single and simultaneous conventional cutting (CC) results in stainless steel. Although there is some variation in material, the roundness of simultaneous VC is generally improved compared to single VC and CC. For the machining of brass, the roundness results of the simultaneous VC were better than those of single CC, VC, and simultaneous CC.

In turning processes, effective chip control is crucial for preventing machining issues such as tool damage, workpiece surface defects, and equipment malfunctions caused by cutting chips. This paper introduces a novel chipbreaking method using a triangular tool path to enhance chip management without compromising surface quality. The technique involves creating a triangular motion in the cutting tool, enabling intermittent air cuts that effectively break chips. This approach can be implemented easily by modifying existing CNC programs, without requiring specialized tools or equipment. Experimental results show that the proposed method successfully breaks continuous chips into smaller segments, significantly reducing chip-related risks. Surface roughness remained comparable to conventional turning, demonstrating that this method preserves surface quality. However, a drawback of the triangular tool path is the increased machining time due to extended tool movement. To mitigate this, higher feed rates were applied during rough-cutting stages, reducing machining time without affecting surface roughness or roundness. This study highlights the potential of a triangular tool path as a practical solution for chip control in unmanned and automated machining environments. Although further optimization is needed to improve machining time, this method offers a flexible and cost-effective option for enhanced chip management.

This study presents a state stabilizing mechanism that can maintain consistent axial tension in the ball screw feed drive systems used within machine tools, even when the screw shaft is elongated due to thermal expansion. The proposed mechanism is structured as an attachable unit for the end of the screw shaft in a feed drive. During a machining cycle, the heat generated from the rotation of bearings within the unit is then used as an intrinsic energy source to expand hydraulic fluid sealed within a pressurized chamber. By minimizing the variation in pressure within the chamber, the mechanism autonomously maintains stable axial tension in the feed drive. After modeling pressure within the chamber and comparing calculated results with trends in actual test data, the proposed mechanism was confirmed to maintain stable axial tension even in the presence of shaft elongation. Results in preliminary tests validate the mechanism as a solution that may be considered during the design stage.

This paper addresses the issue of low processing efficiency resulting from the intricate process of an internal joint component. To this end, it proposes an optimization of the processing route for the component through the implementation of an artificial fish swarm algorithm based on the existing process route. In accordance with the characteristics of the part, the processing element is meticulously delineated, a prudent processing element code is formulated, the adjacent processing elements of the same category are consolidated, and the process constraints of the part are scrutinized before and after the merger. A reasonable constraint matrix model is constructed. The objective function is to determine the minimum number of machine tool, cutting tool, and clamping type changes. The design of prey, swarm, and follow behavioral parameters is conducted in a reasonable manner, as is the optimization and adjustment of the algorithm. By comparing and verifying the pre-optimization and post-optimization process solutions, the study shows that the combined optimized solution reduces the total number of machine tool changes, tool changes, and clamping changes by 43.8% and reduces the total machining time by 7 minutes and 16 seconds, which is more efficient and reasonable.

To ensure that electronic components are accurately placed on PCBs, the precision and robustness of the SMT machine's vision system are critical. In light of this, we focus on the challenges associated with detecting the position and orientation of rectangular components, proposing an innovative solution aimed at enhancing detection accuracy and stability. Firstly, to address the impact of varying illumination on edge recognition, we introduce an entropy-based adaptive thresholding method. This approach maintains stable performance under different lighting conditions, ensuring accurate extraction of component edges. Secondly, to tackle the challenge of extracting edge segments against complex backgrounds, we propose a clustering algorithm based on weighted fusion, combined with RANSAC linear fitting techniques. This effectively extracts complete edge segments of the components. Finally, by analyzing these edge segments, we achieve high-precision measurement of the deflection angles of rectangular components. To validate the effectiveness of the proposed method, we conducted experimental tests on eight different types of chip packages and compared the results with those from two state-of-the-art line segment detection algorithms currently in use. The experimental results demonstrate that our method exhibits significant advantages in terms of both precision and robustness. Overall, the proposed scheme not only improves detection accuracy but also enhances the reliability of the system in practical applications, showcasing its potential value in the field of industrial automation. Through comprehensive evaluations of various package types, this study confirms the superior performance of the proposed method in handling complex environments. It provides new insights and technical support for the further optimization of vision systems in SMT machines.

Omnidirectional mobile robots with conventional wheels avoid the drawbacks (e.g., shock, slippage, and low load capacity) of omnidirectional mobile robots with wheels that have special structures. We previously proposed such a robot, called the slidable-wheel omnidirectional mobile robot (SWOM), as well as its controller for point-to-point movement. However, for practical applications, such as transporting goods in factories and warehouses, SWOM needs to be able to follow a predefined trajectory. In this paper, we present the design of a trajectory tracking controller for SWOM. Given that SWOM is a nonlinear system with constraints on both inputs and outputs, model predictive control (MPC) is adopted. Due to the high computational demands and time consumption associated with nonlinear MPC, linear MPC is used to achieve trajectory tracking. By expanding the previous research, an original method for generating a reference path that includes not only state variables but also inputs is proposed in this paper for the trajectory tracking task. The linearized kinematic model of SWOM is obtained using a first-order Taylor expansion around reference points on the reference path. Simulations considering slippage are conducted and the results show that SWOM can well track the reference path. Experiments conducted on a prototype also validate the effectiveness of the proposed control method.

High-speed directed energy deposition (DED) offers advantages such as efficiency, thin coating layers, and reduced heat-affected zones, making it ideal for repairs and coating on existing parts across various industries. In the DED process, various deposition parameters are included and affect the thermal history. Moreover, a thermal history significantly affects the deposit's mechanical properties by varying metallographic structure. Therefore, thermal simulations of DED are highly demanded due to the time and cost challenges associated with experimenting across different process parameters. However, thermal simulations of DED have difficulties because of the process by which material and heat source are supplied from time to time to the melt pool. Many studies have been conducted on thermal simulations of DED, however, few studies focus on high-speed DED coating. Therefore, this research develops the thermal simulation techniques for high-speed DED coating by exploring five simple methods for element addition and heat input. The geometry of the workpiece model was simplified to reduce the simulation cost. Using the Ansys Parametric Design Language (APDL) software and the simplified workpiece model, we conducted thermal simulations and validated them against experimental data acquired from high-speed imaging and two-color temperature measurements. Through these methods, the study proposed the temperature comparison method of experiment and simulation results and identified the suitable method for element addition and heat input in high-speed DED coating that closely replicates the actual temperature within the coating layer. This method reduces the need for finer mesh, effectively lowering computational costs while maintaining accuracy. The findings provide insights into practical simulation practices for DED and contribute to enhancing parameter validation in additive manufacturing simulations, supporting further development in high-speed DED coating technology.

The stability of computer numerical control machine tool operation directly affects production efficiency and quality. However, there are some uncertain factors concerning with environment in which the control effect of these tools is not ideal. To address these issues a knowledge graph-based real-time optimization intelligent control system for computer numerical control machine tools is used. This study uses the research results from the field of machine tools to establish a knowledge graph. Furthermore, a method based on the graph neural network is employed to relate the knowledge graph data with the functioning of the machine tools. In addition, for further details, the data is classified into different groups by using clustering algorithms resulting in corresponding decision-making outcomes. The results showed that the decision accuracy and workpiece quality qualification rate were 98.74% and 95.47%, respectively. The decision response time of the system was only 1.09. By using a knowledge graph, a real-time optimization intelligent control system for machine tools can guarantee the stability of their performance and provide precise control decision-making, enhancing their performance and thereby improving the quality of the products being produced.

It is significant to design a grinding device with vibration suppression function for robotic constant force grinding. In this paper, a novel magnetorheological damping vibration isolation device (MDVID) for robotic grinding system is proposed. Firstly, the MDVID is designed by integrating magnetorheological damping unit and connecting unit, and the whole prototype of the robotic grinding end effector is developed. Then, damping force output characteristics of the MDVID under different parameters are analyzed based on the mechanical model and Maxwell electromagnetic simulation software. On this basis, mechanical properties experiment of the MDVID is carried out to further reveal and verify damping force output regularity characteristics of the magnetorheological damping unit under different currents, compression speeds and compression displacements. Finally, based on the data of magnetorheological damping force, damping force model of the MDVID is established through data fitting, and experimental verification of different parameter conditions is carried out. The MDVID proposed in this paper effectively integrates magnetorheological damping smart materials and can be used for vibration suppression of the robotic grinding system.