International Journal of Precision Engineering and Manufacturing最新文献

The precision of machine tools is crucial for ensuring the accuracy of final products, directly impacting manufacturing quality and efficiency. Axial offset error, a significant factor in tool holder-spindle interfaces, mainly affects high-speed machining processes. In previous research, the axial offset error of the taper contacts BT40 tool holder was modeled by linear interpolation of the maximum and the residual error polynomial curves of maximum spindle speed. This study aims to develop a more accurate axial offset error modeling using the natural neighbor interpolations of the measured errors in the spindle speed domain. Experiments were conducted on BT40 and BBT40-type tool holders, and the key findings are as follows. The maximum axial offset error for the BT40 tool holder was 7.95 μm at 10,000 rpm, with a residual error of 4.45 μm. The maximum error for the BBT40 tool holder was 3.80 μm at 12,000 rpm, with errors decreasing and becoming negative at higher speeds, reaching −5.36 μm at 20,000 rpm. The new model reduced the prediction accuracy by 43% compared to the previous model, demonstrating improved robustness against various error tendencies. The proposed model enhances prediction accuracy and offers potential applications in offline simulation and real-time error compensation, contributing to higher manufacturing quality without requiring hardware changes.

The flat surface of a thin LiTaO₃ substrate, exhibiting excellent electro-optical and piezoelectric properties, is required to enhance surface acoustic wave energy. A high-quality surface of a thin LiTaO₃ substrate can be obtained through ultra-thinning processes, such as grinding and chemical mechanical polishing. However, during the ultra-thinning process, the grinding wheel gradually wears, leading to an uneven pressure distribution on its surface, which results in machining errors, such as cracks, subsurface damage, and chatter. Hence, the uneven pressure distribution must be examined to maintain and improve machining accuracy. In this study, reciprocating tests and simulations were performed on the grinding wheel of a LiTaO₃ wafer using Archard’s wear model in the commercial software ANSYS Transient Structural. In addition, a grinding simulation was performed, considering the grinding conditions and wear rate, to examine the pressure distribution on the surface of the grinding wheel. In the grinding simulations, the periodic pressure distribution changed at a high frequency of 12,987 Hz on the surface of the grinding wheel, with a maximum pressure of 1.7 MPa. Additionally, modal analysis was conducted to examine the occurrence of resonance, thereby confirming the risk of resonance.

This study designs a system to precisely detect the angle of wafers on an ion implanter's electrostatic chuck (ESC). In specific ion implantation processes, ions may penetrate deeper than intended because of the channeling effect, compromising the device performance. To address this issue, the system adjusts the tilt of the ESC and the twist angles of the wafer to control the ion beam direction. Utilizing a camera-based machine learning system, the system identifies the wafer notch to ensure an accurate alignment of the ESC. However, factors such as insufficient lighting and vibrations affect notch detection, which can degrade image quality. To overcome these issues, this study explored various image-enhancement techniques and evaluated the performance of object detection algorithms on enhanced images.

Aiming at the problem that the rotor of vertical magnetic suspension system falls to the protective bearing with different structural parameters and causes its failure, in this paper, the dynamic model of the inner structure of the protective bearing, the mathematical model of axial and radial collision between the rotor, the inner ring of the protective bearing are established. The author changed the two structural parameters of protective bearing with and without cage and the ball filling number, and the impact characteristics of WNCPB (with no cage protective bearing) and WCPB (with cage protective bearing) on the rotor were compared and analyzed. Finally, the protective bearing performance testing machine of the magnetic bearing system was built to simulate and calculate the impact force of the outer ring, and the indirect measurement experiment of the axial and radial impact force of the protective bearing with different structural parameters was carried out. The empirical results show that: the impact resistance of WCPB improved with the ball filling number increasing from 26 to 28. The number of WNCPB filled balls should be slightly less than the number of full balls to obtain optimal impact resistance. With the ball filling number increasing from 26 to 28 and then to 30, WNCPB with the ball filling number 28 has the best impact resistance. Compared to WNCPB, the overall performance of WCPB is improved.

Micropumps have found wide applications in biomedicine, micro-electro-mechanical systems, and microfluidic systems. This study presents a novel nozzle/diffuser micropump with two parallel chambers fabricated using the stereolithography (SLA) 3D printing method from FLGPCL04-Fe₃O₄ magnetic nanocomposite. The proposed valveless micropump is an attractive alternative for drug delivery applications due to its effective controllability, cost-effectiveness, and mass production capability. The dual chamber structure is able to overcome the disadvantages of the single chamber micropumps like providing higher flow rates. In this micropump, a maximum membrane displacement of 65 μm has been achieved using 5 wt% magnetic nanoparticles concentration for a 30-turn microcoil and applied current of 1000 mA. The fluid flow was evaluated through the membrane displacement using numerical simulations in COMSOL Multiphysics 5. Based on the experimental results, a maximum flow rate of 82 nL/s has been achieved under dual-chamber loading while loading one of the chambers leading to a maximum flow rate of 62.5 nL/s.

With the aging of our society, there has been a surge in the prevalence of degenerative arthritis among individuals in their 50 s, leading to an elevated demand for total knee arthroplasty. Consequently, there is a growing need for a surgical simulation system that can enhance surgical satisfaction and assist surgeons improving their proficiency with patient-specific surgical plans. However, there are currently limited methods available to evaluate whether the knee joint amputation performed after surgical simulation aligns with the surgical plan. In this study, we propose a system that can instantly calculate the knee joint's cutting angle and evaluate outcomes in the surgical simulation using a depth camera. In order to reduce the inherent measurement errors of the depth camera, we investigated error levels associated with specimen color, object distance, and illumination conditions. Subsequently, we devised a measurement environment that would effectively mitigate these errors. Following this, we produced specimens with varying areas and shapes to evaluate the accuracy of the angle measurement algorithm through error comparison by angle. Finally, we conducted angle measurements on the mimetic bone that was cut, replicating the surgical simulation procedure, and verified that the angle of the cutting surface could be measured with an error margin of around one degree.

Tool condition monitoring (TCM) is crucial for smart manufacturing and cutting vibration signal is proven to be highly related to tool wear state. In this paper, a wireless smart tool holder is designed for online vibration signal sensing for TCM with accelerometer embedded close to vibration source and signal processing circuits integrated, showing good performance of vibration sensing ability compared with traditional wired ways. Cutting experiments are designed with cutting parameters of great varied range to guarantee the generalization ability of TCM algorithm for different machining conditions and vibration signal of whole tool life cycle is collected by smart handle. Then feature extraction and selection are studied to provide valuable information and artificial neural network algorithm is realized. Results show the algorithm has an accuracy of 85.0% with poor performance in distinguishing some wear states. To solve this problem, an optimized method based on two ANNs in series with new feature sets is proposed. The optimized algorithm has an accuracy of 90.0% with an accuracy increase of 16.8% and the average predicted probability increase of 15.0% in initial wear samples. In spite of speed sacrifice, the optimized algorithm makes progress in recognition accuracy and data confidence level.

Chemical Mechanical Planarization (CMP) is the most well-known process for global planarization of wafer surfaces. The importance of interposers has been growing due to ultra-micronization and densification of semiconductors. Through-Glass-Via used in interposers has over-deposited copper layer after via filling. This copper bulk layer needs to be planarized by CMP for post-processing. At the heterogeneous material interface, defects such as dishing occur due to different material removal selectivities. In addition, the chemical reaction of copper with chemical additives is very sensitive to temperature. Therefore, temperature is an essential consideration for an efficient CMP process. In this study, we compared the effect of slurry additive properties that change with temperature on material removal. For the BTA-based slurry, the initial dishing at high temperature was 95 nm and increased by 25 nm per minute. It shows an increase of more than twice compared to the results at low temperatures. Conversely, for TTA-based slurry used in this study, the initial dishing at high temperature was 70 nm and increased by 15 nm per minute. It shows decrease of more than twice compared to the results at low temperatures. Therefore, we aim to achieve low dishing by utilizing the increasing process temperatures, on the contrary.

The high hardness and brittleness of engineering ceramics make it difficult to ensure surface quality during conventional grinding. Compressive pre-stress assisted machining, as a new processing technology, can effectively improve the surface/subsurface damage of engineering ceramics. In this study, compressive pre-stress assisted scratching experiment was conducted on 95% Al₂O₃ ceramics with diamond indenter under three pre-stresses of 0 MPa, 200 MPa and 400 MPa. The influence of compressive pre-stress on the scratch morphology of 95% Al₂O₃ ceramics, as well as the changes in scratch force and vibration signal during the wear of indenter were comprehensively analyzed. The experimental results show that when the compressive pre-stress increases to 400 MPa, the scratch depth is reduced by 5–15%, the width is reduced by 10–30%, and the depth of scratch subsurface damage is also reduced, avoiding the occurrence of obvious cracks. Wavelet decomposition of the collected vibration signals shows that as the increase of the compressive pre-stress, the fluctuation value of singulars in high-frequency signals gradually decreases, and the percentage of energy gradually increases. Combined with wavelet analysis and the surface wear morphology of indenter, it was found that although the large compressive pre-stress aggravates the tool wear, the surface machining quality of the material is also significantly improved.

Developed countries are distinguished by their large-scale infrastructure, including bridges, towers, and power plants, most of which are constructed using various types of steel, such as mild and stainless steel. Strength and durability of steel in low budget make it an ultimate choice. In the construction of these structures, welding plays a crucial role, utilizing various joint configurations such as butt, T, and lap joints. This study examines the effect of multiple weld repairs on mild steel, using a welding speed of 150 mm/min and a current of 100 amperes for 3 mm thick sheets. Initially, the weld’s microstructure exhibited several cracks within the Weld Zone due to inadequate weld material filling. After the first repair, significant changes were observed, with elongated and distorted grains and an increase in hardness due to pearlite formation and Sulfur segregation. A second repair further highlighted the effects of repeated thermal cycles, causing increased brittleness and Sulfur segregation. The hardness of the weld joints increased by 16% and 24% after the first and second repairs, respectively, when compared to the base mild steel material. However, the Ultimate Tensile Strength (UTS) decreased to 48%, and the Yield Strength (YS) fell to approximately 54% after the second repair. Interestingly, the weld joint showed improved tensile properties after the first repair, attributed to the effective filling of cracks that appeared after the initial welding pass. This resulted in a slight increase in UTS and YS. However, the percent elongation of the material decreased due to the repeated thermal cycles involved in the welding repairs, with reductions of 44.3% and 63.6% after the first and second repairs, respectively. This increase in hardness and decrease in ductility after repairs suggest that the weld joints became more brittle.