In this paper, an adaptive edge detection method (Canny operator and Otsu threshold selection based adaptive edge detection method - COAED) is proposed. The COAED method combines a new hybrid filter with Canny operator to avoid the conflict of Canny operator between noise removing and edge locating, and uses Otsu threshold selection method to determine Dual-threshold of Canny operator adaptively. The new hybrid filter firstly judges whether the pixel is polluted by impulse noise, and then uses a corresponding filter to process the current pixel. A median filter is used if the pixel is thought to be impulse noise; otherwise an improved mean filter is selected to weaken the Gaussian noise. After the image is smoothed by the hybrid filter, a Canny operator with small Gaussian variance is used to extract edge. Because only part of Gaussian noise remains, Canny operator with small Gaussian variance can suppress the noise and preserve the edge effectively. Using the gauge image polluted by hybrid noise as experiment object, the performance of COAED method is evaluated qualitatively and quantitatively. Experimental results show that the COAED method is superior to Canny operator.
The grinding process requires a high energy expenditure per unit volume of material removed. The high temperature generated in abrasive processes is the main factor responsible for thermal damage to a ground surface. An investigation was undertaken to explore the temperature characteristics in high speed grinding (HSG) of silicon carbide (SiC) with a vitrified diamond wheel. A grindable thermocouople technique including a NI-DAQ device will be used to measure the grinding temperature. This paper will discuss the temperature characteristics in high speed grinding of SiC in detail and give an experiment-based temperature distribution model for SiC. A molecular dynamics simulation will be used to illustrate the effect of a high loading rate on SiC material’s mechanical property, which will further elaborate its unique HSG temperature characteristics. The experimental investigation will provide more practical application support in utilizing HSG technology in a high quality ceramic grinding.
The yarn production is a complex industrial process, and the relation between the spinning variables and the yarn properties has not been established conclusively so far. The SVM regression algorithms are briefly introduced in this study, and then SVM models for predicting yarn properties have been presented. Model selection which amounts to search in hyper-parameter space is performed for study of suitable parameters with Genetic Algorithms. The yarn experimental results indicate that GA- SVM models are capable of remaining the stability of predictive accuracy, and more suitable for noisy and dynamic industrial process.
High-speed permanent magnet synchronous motorized spindle plays a crucial role in high speed grinding which could greatly improve the efficiency of cutting and reduce manufacturing cost. However, for high-speed machining, the spindle system usually generates excessive heat due to its complex structure and the high-speed rotation, resulting in the loss of grinding precision. To control and optimize the heat generation of the spindle, this paper develops a thermal model for the high-speed spindle system, and further analyzes the steady-state thermal property at different grinding speeds. Based on the result of analysis, the temperature of the spindle system is finally optimized with modified cooling system, providing a theoretical basis for the optimization of high-speed permanent magnet synchronous spindle system. After optimization, the temperature of stator reduce significantly to 33°C, which was 53°C before, accordingly the temperature of the rotor is decreased by 19°C.
This paper presents an analytical approach to predict the machining force, temperature and residual stress under minimum quantity lubrication (MQL) condition. Both the lubrication and cooling effects are considered to change the tribological and thermal properties in the modified Oxleys model, which is capable to predict the cutting force and temperature in MQL machining directly from cutting conditions. The machining-induced residual stress is predicted by modified McDowell hybrid algorithm. The predicted cutting forces and residual stresses are verified by orthogonal cutting tests for C45 steel and TC4 alloy steel.
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