Research on integral separation control of warp tension based on fuzzy parameter optimization

The hybrid machining method called electrochemical mill-grinding is an advanced machining technology, which can achieve high-quality processing of various difficult-to-cut materials. However, achieving rapid transportation and removal of processed products within a small machining gap is a key challenge, which directly affects the final machined surface quality. In this paper, the product transport under different flushing modes was studied through numerical simulation of the flow field. By using the particle tracking method, the time required for the removal of machining products from the machining gap was dynamically simulated. The analysis results indicated that the removal speed of machining products could be significantly improved when the flushing pressure was 0.8 MPa and the electrolyte jet angle was 30°. In addition, machining experiments were conducted. The machining experiment results showed that the machined surface had a metallic luster with clear edge contours. The grinding marks on the machined surface indicated the grinding effect. The continuous machining of complex patterns demonstrated the reliability of this hybrid machining process.

Improving the machined surface in electrochemical mill-grinding by particle tracking fluid simulation and experimental research

Non-abrasive iron-based grinding diamond wheels, lacking abrasive particles, negate the concern of detached passivated abrasives scratching the polished surface. During the Electrolytic In-Process Dressing (ELID) polishing process, α-Fe2O3 particles form on the oxide film surface of the iron-based grinding diamond wheel devoid of abrasives. These particles assume the role of the grinding diamond wheel for polishing. Therefore, the ELID electrolytic performance and the oxide film formation performance of the non-abrasive iron-based grinding diamond wheel significantly influence the formation of α-Fe2O3 particles and the subsequent ELID polishing performance. Our studies investigating the ELID dressing behavior of the non-abrasive iron-based grinding diamond wheel have analyzed the influence mechanism of the electric field, flow field, and pulse energy on its ELID dressing. Moreover, the roundness of the ELID dressing grinding diamond wheel, oxide film properties, and α-Fe2O3 particle content were measured, and plate glass was polished using the ELID non-abrasive iron-based grinding diamond wheel. The results illustrate that the non-abrasive iron-based grinding diamond wheel exhibits excellent ELID dressing performance. The diamond wheel’s roundness is less than 0.001 mm, the oxide film uniformly covers the surface, the content of α-Fe2O3 particles is evenly distributed, and the surface accuracy of the polished plate glass is approximately 0.5 nm. These findings provide a compelling case for the continued use and development of grainless iron-based grinding diamond wheels in precision grinding operations.

ELID Dressing Behaviors of Non-Abrasive Iron-Based Grinding Wheels

A novel constant tension yarn feed control system for automatic winder is presented in this paper which is based on analyzing the principle of the generation of yarn tension and the factors which make the tension fluctuate DSP28035 made by TI (Texas Instruments) is adopted as the main control unit of the system to acquire the real-time yarn tension which is transmitted from a yarn tension sensor Intelligent PID (proportional-integral-derivative) algorithm is also adopted to achieve a high-dynamic performance and an ultra-low inertia servo motor speed digital control to realize constant yarn tension feed The system not only have the function of tension setting, over current protection, error detection and alarm, but also possess the features of simple structure, low cost, real-time control and high accuracy

The Design of Yarn Constant Tension Feeder System

This study aims to develop an artificial intelligence module for recognizing abnormal tension in textile weaving, The module can be used to address the time-consuming and inaccurate issues associated with traditional manual methods. Long short-term memory (LSTM) recurrent neural networks as the algorithm for identifying different types of abnormal tension are employed in this module. This study focuses on training and validating the model using five common patterns. Additionally, an approach involving the integration of plug-in modules and edge computing in deep learning is employed to achieve the research objectives without altering the original system architecture. Multiple experiments were conducted to search for the optimal model parameters. According to the experimental results, the average recognition rate for abnormal tension is 97.12%, with an average computation time of 46.2 milliseconds per sample. The results indicate that the recognition accuracy and computation time meet the practical performance requirements of the system. 

pdf/development-of-the-abnormal-tension-pattern-recognition-3t73q5v80c.pdf

Development of the Abnormal Tension Pattern Recognition Module for Twisted Yarn Based on Deep Learning Edge Computing

The paper introduced a high speed measurement system called virtual dynamic signal test system for the yarn tension of weaving, winding and warping based on Direct Memory Access (DMA) and which was applied on loom and winding machine to test the performance of the system. The working mode of DMA in the process of signal acquisition was analyzed. Moreover, according to the measurement of the yarn tension, the favorable property of the system was proved. A series of the experiments showed that the system had good performance during measuring yarn tension and had achieved expected results. In addition, it had excellent accuracy, good expansibility as well as great universality and high sampling frequency (400KHz).

A high speed measuring system of yarn tension based on direct memory access

Oxide film thickness and composite mechanical properties of an ultrasonic-assisted ELID cast-iron-bonded grinding wheel

Investigation of the plastic deformation mechanism of polycrystalline M50 aviation bearing steel during electrolytic in-process dressing scratching using molecular dynamics simulation

Since installing speed sensors on the grinding head for high-speed precision roll grinders is not feasible, it is necessary to use direct torque control (DTC) without speed sensors, and its control system requires high estimation accuracy. In this paper, speed estimation methods using extended Kalman filter (EKF) and model reference adaptive system (MRAS) have been used to realise speed estimation in the DTC system of the spindle motor of the grinding head for a high-speed precision roll grinder. The simulation results showed that the EKF method has relatively high accuracy and a fast convergence speed for the estimation speed of the spindle motor of the grinding head for a high-speed precision roll grinder by comparison with the MRAS method. Moreover, the EKF method was also able to overcome the nonlinear and parameter uncertainty of the operating system of the motor and strongly immune to the noise interference of the system. Finally, the speed estimation accuracy of the motor using the EKF method had better than the MRAS method. Therefore, this work provides a general approach to applying the DTC without speed sensors for the motor and further lays a solid foundation for developing high-speed precision roll grinders. 

Speed estimation of the spindle motor of the grinding head for a high-speed precision roll grinder based on the extended Kalman filter

Selection of a suitable electrolyte for electrochemical grinding of high-speed steel roll material based on electrochemical techniques and uniform design machining experiments

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