International Journal of Precision Engineering and Manufacturing最新文献

Iterative learning control (ILC) enhances control specifications for display panel transfer robots used continuously in industrial settings, offering significant cost-effective improvements in sites requiring higher control standards. This study focuses on improving the path accuracy of an 8G display panel transfer robot control system, as defined in ISO 9283, by implementing a path error compensation ILC system. To mitigate the time-delay characteristic of path error compensation inputs in the robot system, an ILC algorithm was developed. It incorporates time-scaling and time-shifting algorithms in addition to the switching-gain, proportional, derivative (SPD) offline ILC method. Furthermore, a compensation system was designed to ensure the stability of the compensation input, integrating a low-pass filter into the proposed ILC algorithm. Experimental validation of the ILC compensation system was conducted using an 8G display panel transfer robot, demonstrating its functionality. Additionally, the ILC path error compensation system parameters were optimized through various experiments and detailed characteristic analyses. Iterative learning resulted in a reduction of RMS path error data by more than 90%, significantly enhancing control performance.

Fused deposition modeling (FDM) 3D printing with polymeric materials has the advantage of producing products of various shapes; however, it has limitations in the mechanical properties of the output. Therefore, post-processing processes must be applied to the output, and research must be conducted to improve the mechanical properties. The first objective of this study was to compare the mechanical properties of FDM 3D printed parts made of polylactic acid (PLA) with and without ultrasonic post-processing. The mechanical properties of the PLA prints were compared using tensile tests before and after ultrasonic treatment, and the mechanical properties of the PLA prints were compared with ultrasonic treatment at the glass transition temperature. Consequently, the tensile strength of the ultrasonically treated PLA output improved by approximately 38.8%. The second objective of this study was to apply a machine learning algorithm based on convolutional neural networks to extract the image pattern observed in the output before and after ultrasonic treatment and to predict the mechanical properties. A machine learning algorithm, consisting of feature extraction and classification, was applied to develop a pretrained model to detect whether the output was sonicated and to predict the mechanical properties accordingly. Furthermore, the PLA output, whose reliability was verified by the pretrained model, was expected to be used as a structural material element in various industrial fields.

High-quality welded structure products can be obtained by formulating a reasonable welding process. However, serious corrosion problems can still occur in the process of use, especially in the use of thin stainless steel lap structures. In this paper, the influence of different lap angles on the corrosion resistance of the joints was analyzed. The experimental results revealed that the heat input during the welding process of different lap joints was basically the same, so the difference in microstructure was not significant. The distribution pattern of microhardness was also consistent, and the hardness of the lap joint was high in the weld zone, low in the heat-affected zone and fusion zone. The effect of joint angle on corrosion resistance was obvious. With the increase of the lap angle, the overall corrosion resistance presented a tendency to first increase and then decrease.The corrosion resistance of the joint was excellent when the lap angle was 18°, the self-corrosion current density was the minimum of 2.528 × 10⁻⁶ A cm⁻² and the charge transfer resistance R_p was the maximum of 3516 Ω cm². The corrosion resistance of the welded joint was poor when the lap angle was 27°, the self-corrosion current density was the maximum of 1.151 × 10^–5 A cm⁻² and the charge transfer resistance R_p was the minimum of 840.7 Ω cm².

42CrMo high-strength steel is a material that is difficult to machine and has difficulties controlling the quality of the machined surface. To ensure the stability of surface quality during cutting, lead the adjustment of cutting parameters to accurately predict the 42CrMo steel's machined surface roughness (Ra). Single factor cutting, orthogonal cutting, and response surface cutting experiments were conducted based on the experimental platform, and single factor, range, and grey correlation analyses were performed on the surface roughness measurement results. It can be concluded that within a given range, the feed per tooth has the greatest impact on surface roughness, and the cutting depth has the least impact on surface roughness. The PSO-SVM surface roughness prediction model was developed and compared with other widely used surface roughness prediction models (BP, SVM, GA-BP, PSO-BP) by using experimental data on the machined surface roughness of cutting 42CrMo steel. It can be concluded that the PSO-SVM training set prediction model has an average relative prediction error of 4.76% and a goodness of fit R² = 0.87198, which is quite near to 1. The PSO-SVM testing set prediction model has an average relative prediction error of 12.65% and a goodness of fit of R² = 0.86406, which is quite near to 1. Since it can effectively guide the selection and adjustment of cutting parameters, the PSO-SVM surface roughness prediction model has high prediction accuracy, good fitting degree, and stability. It also has a specific reference value for the study of the cutting process and surface quality of 42CrMo steel.

Joining dissimilar metals poses critical challenges due to differences in their properties, leading to the formation of brittle intermetallic compounds (IMCs). Aluminum (Al) and copper (Cu) are widely used in electrical applications for their advantageous characteristics. In lithium-ion batteries, joints of these metals aim to harness their physical and electrical beneficial features. However, aluminum and copper’s high reflectivity and heat sensitivity present significant challenges in the joining processes. In the present study, a pulsed fiber laser, tailored for heat-sensitive components, was employed to weld Al/Cu and Cu/Al overlap configurations separately. Varied laser power ranges were applied to each welding configuration. The quality of the welds was assessed based on microstructure and mechanical properties. The results revealed the growth of dendritic IMCs towards the Al side in both welding configurations. Notably, the Al/Cu weld exhibited superior connection strength and fewer imperfections compared to the Cu/Al weld.

In recent years, the process monitoring based on optical radiation detection widely applied in laser welding monitoring process, such as visual cameras, spectrometers and photoelectric sensors. This study proposes a low-cost monitoring model based on a CNN module with the combination of convolution and depth-wise separable convolution (DSC) applying the industrial photoelectric sensors. This model aims to generate more effective features from the primitive signals captured by the visible light photoelectric sensor and the reflective laser photoelectric sensor, without pre-processing in advance. The DSC is applied to generate features to reveal the inherent features of welding statuses, and especially reduce the computing costs during monitoring process. The proposed model in this study acquired high accuracy with low space complexity and time complexity compared with the traditional model. The model also performs well under the limited and unbalanced welding data, indicating its good robustness. This study provides a low-cost method for real-time monitoring of laser welding process.

In this work, an algorithmic technique is used to minimize the excess parts and maximize the success rate of selective assembly. In this study, a unique method known as Equal Area-Equal Width-Equal Bin Numbers is introduced to group the parts of a ball bearing assembly by taking into account their range of tolerance. A full factorial design is used to conduct the experiments, and the salp swarm optimization (SSO) algorithm is employed to evaluate the best bin combinations and identify the possibility of making the maximum number of assemblies. Computational results showed a 13.16 percent increase in success rate when compared to prior research when employing the proposed method. Comparing the computational outcomes versus those obtained by the Antlion optimization and Genetic algorithms validates the adoption of the SSO algorithm. A paired T-test is performed to assess the statistical significance of the findings. The convergence plot further supports the superiority of the SSO algorithm.

High-speed machining is a practical way to attain high productivity with lower costs. Under this condition, the tool geometry needs to be optimized to sustain high cutting forces and temperatures. The sharpness of the cutting edge and the coating thickness (CT) are two key parameters that affect the tool’s performance. While a sharp edge eases the cutting process, it causes a high stress concentration, which increases the wear rate and eventual edge fracture. In this study, we use a combination of finite element simulations and experimental testing to evaluate the effects of CT ( 1–3 μm), edge radius ((r_{beta }) , 6–15 μm), and coefficient of friction ((upmu = 0 - 0.2)) on the stress distribution at the cutting edge. Our simulations showed that the larger the CT, the higher the stress magnitude inside the coating, but the lower the maximum stress depth percentile. Considering an industrial case of cutting steel workpieces using AlCrN-coated tungsten carbide tools under given cutting parameters, our simulations suggested an optimum CT of 3 μm. By manufacturing a series of milling tools with different CTs and edge radii, we validated the simulation results using a set of well-controlled milling experiments. Finally, the edge radius should be selected considering the size of rake/flank angle mainly to control stress distribution over the cutting edge.

In order to investigate the effect of passenger load on the vibration of rail vehicles, a modal experiment was conducted on a certain type of high-speed train under empty-load, rice counterweight, and passenger-carrying conditions. The influence of different load configurations on the modal vibration of the vehicle was examined and analyzed using multi-point excitation and sine sweep frequency methods. The differences between the impact of passenger and rice loads on vehicle vibration were elucidated. Subsequently, the human–vehicle coupling mathematical model was modified based on the experimental results, and the differences in the effect of different loads on vehicle vibrations were simulated and analyzed. Finally, the attenuation mechanism of passenger loads on vehicle vibration was analyzed. The research has established that the modal parameters of the vehicle are greatly influenced by different loads. Numerical calculations demonstrate that the vertical bending mode of the car body in the middle experiences a maximum vibration attenuation of 60% when carrying passengers compared to the same mass of mass loaded. The natural frequencies and damping ratio of passengers can significantly affect the vertical bending mode vibration. The findings of this research can serve as a theoretical foundation and technical assistance for implementing vehicle vibration reduction techniques.

This paper presents a synchronous measurement methodology aimed at identifying four position-independent geometric errors (PIGEs) and six position-dependent geometric errors (PDGEs) of the rotary axis in five-axis machine tools. Previous studies and literature have emphasized the challenge of simultaneously measuring and identifying PIGEs and PDGEs of the rotary axis in five-axis machine tools. Therefore, the primary objective of this paper is to propose a measurement methodology that can identify these errors simultaneously through a single measuring process. Compared to commercially available measuring instruments, this measurement system offers several advantages: it is easy to install, cost-effective, and can be applied to various types of five-axis machine tools. These benefits enable the establishment of a fast on-machine error measurement. The initial phase of the research involves establishing a mathematical model and computing the geometric error equations based on the specific type of machine tools in use. Subsequently, the difference between the ideal and actual center positions of the calibration sphere is determined by utilizing a touch-trigger probe while positioning the machine's rotary table at various angles. Finally, the experimental data is inputted into the mathematical algorithm to obtain the PIGEs and PDGEs of the rotary table. Post-experimentation, the PIGEs and PDGEs obtained through the proposed measurement method are incorporated into the controller as compensations. The feasibility of this approach is evaluated by measuring the volumetric errors of the machine tools both with and without compensation. The results demonstrate a significant reduction in the deviation of the volumetric errors, decreasing from 11.97 to 2.31 µm after compensation. This outcome underscores the potential of the proposed method for simultaneous measurement of geometric errors in the rotary axis of machine tools across various types and scenarios.