In order to solve the problems of tool wear measurement in actual production, such as manual operation and shutdown detection, a machining tool wear measurement system based on machine vision is developed in this paper. First, the Otsu segmentation algorithm based on Laplacian edge information is proposed to binarize the images. Then, through rough positioning by morphology-based Canny operator edge detection and image registration, the tool wear area is extracted effectively. Finally, sub-pixel edge detection based on Zernike moment is used to improve the measurement accuracy while the principal curve method is used to fit sub-pixel edge points so as to obtain the smooth edge curve and realize the online measurement of tool wear. In real machining process, the tool wear test results show that the system has a high degree of automation and a rapid running speed. Moreover, its measurement accuracy can reach micron level. This system can be effectively applied to real-time monitoring of tool wear in industry.
Alloy elements can influence the microstructure evolution of aluminum alloys in the state of solid solute atoms or nano-precipitated silicon particles, but it is still a controversial subject which form has a more significant effect on the microstructure of aluminum alloys. Therefore, taking the Al-1%Si alloy as the research object, the ratio of precipitation state and the solid solution state of the silicon atoms was changed before deformation and a multi-pass accumulative roll-bonding method was used to achieve large deformation. In order to compare the influence of solid solute atoms and nano-precipitated silicon particles on the structure and properties of aluminum alloy during deformation, a comparative study was conducted on the evolution of nano-precipitated silicon particles, grain size, and dislocation density in the process of reaching the saturation state of the microstructure and mechanical properties. The results show that the initial samples with less nano-precipitated silicon particles and more solute silicon atoms have a higher saturated dislocation density and a smaller saturated grain size after deformation, corresponding to a higher saturated yield strength. Solid solute silicon atoms dispersed in the Al-1%Si alloy have a better overall effect than nano-precipitated silicon particles of the same volume in preventing the dynamic recovery of dislocations, which is consistent with the theoretical analysis of dislocations. The dislocation recovery ability in the material affects its saturated grain size. The stronger the dislocation recovery capacity, the larger the saturated grain size.
Aimed at the problems of low processing accuracy and surface quality of die-casting aluminum alloy under the influence of cutting force, a method of using surface micro-textured milling cutter with groove and V-shaped arrays on the rake face was proposed. The variations of milling forces were monitored and analyzed by using tri-direction-force transducer, and the spectrum characteristics of the y-direction milling force (Fy) was emphatically analyzed. The spectrum analysis diagrams of Fy of three different milling cutters were obtained by fast Fourier transform. The milling performance of the micro-textured milling cutters was evaluated by surface topography and surface roughness. The results show that compared with the average values in the x, y, and z directions of the conventional milling cutter, the values of the groove micro-structured milling cutter are reduced by 3.8%, 0.29%, and 11.7%, while those of the V-shaped micro-structured milling cutter are reduced by 8.5%, 14.3%, and 12.4%. The relationship between high frequency amplitudes of Fy at 6 times spindle frequency are proposed as conventional milling cutter>groove arrays milling cutter>V-shaped arrays milling cutter. Compared with the conventional milling cutter, the surface roughness is decreased when using micro-textured milling cutters, and the surface quality of the workpiece processed by the V-array milling cutter is the best. This paper will provide a theoretical basis for precision milling of die-cast aluminum alloys.
A self-developed strip-type high-temperature friction and wear test device was used to simulate the high-temperature friction process of uncoated 22MnB5 boron steel under actual hot stamping conditions. The mold was preheated to simulate the temperature increase of the die in the hot stamping process. The effects of mold temperature on the friction behavior and mechanism of uncoated boron steel were studied by using the friction coefficient test, surface wear morphology observation, and cross-section and matrix structure chart of hot stamping boron steel. The results show that the friction coefficient between the uncoated boron steel and the H13 steel is basically stable at 0.5 when the temperature of the mold is low, and the wear mechanism is mainly classified to abrasive wear and adhesive wear. Besides, when the mold temperature exceeds 100 ℃,the friction coefficient of the uncoated boron steel decreases from 0.474 to 0.414 with an increase of temperature from 150 ℃ to 200 ℃,inferring that the adhesive wear is weakened. The Vickers hardness of the boron steel matrix is approximately close to 430 from room temperature to 100 ℃. Moreover, with the temperature further rising to 150 ℃ and 200 ℃,the hardness decreases to 413.5 and 399.7 respectively, which indicates that the mold temperature has a significant effect on the mechanical behavior of formed parts.
Flow spinning process is beneficial to realizing the integral forming of the ribbed members, but the height of the inner rib that can be formed is limited. Therefore, an ultrasonic assisted method was introduced into the flow spinning process to increase the height of the inner rib. Uniaxial tensile and compression tests with ultrasonic vibration were conducted to establish the hardening equation of the 2219-O aluminum alloy considering the acoustic softening effect. The friction reduction effect after ultrasonic loading was analyzed. A simulation model of aluminum alloy ribbed member spinning with ultrasonic vibration was established using the Abaqus software. The simulation results show that the ultrasonic vibration can reduce the deformation resistance of the material, improve the material flow of the ribs in different directions, guide the material flowing into the rib grooves, and thereby improve the filling height of the ribs. When the amplitude reaches 12μm, the rib height can be increased by 1/3.
There is a need for the effective weld seam profile extraction method to realize the automatic and intelligent welding process with thick steel plates based on laser vision sensing. In this paper, a method was proposed to identify the variable weld seam profiles from the strong arc background based on intensity mutation and density feature detection for the thick plate welding process with T-joints. First, an improved Canny algorithm was used to magnify the weld seam profile and restrain interference. Next, an intensity mutation detection method was proposed to further strengthen the weld seam profile because there exists intensity mutation in the local region surrounding the weld seam profile. Finally, an algorithm based on the band-width and density feature scanning method was proposed to further eliminate the interference data after the strengthened image was binarized with the Otsu algorithm. The weld seam profile was identified as clusters with their spatial scale features after the nearest neighbor clustering was dealt with. The results show that this method can identify over 95% of the weld seam profiles from the arc interference background whose area is about 20% of the image. It provides valuable reference for promoting the automatic and intelligent welding process with different joints.
The mechanical behavior of the quenching and partitioning 980 (QP980) steel is affected by martensitic transformation during deformation. The microstructure of QP980 steel before and after deformation is characterized by using the electron backscattered diffraction (EBSD) method. An elastic visco-plastic self-consistent (EVPSC) polycrystalline model considering phase transformation is established based on the phenomenological theory of martensite crystallography (PTMC). The macroscopic flow stress as well as texture evolution of QP980 steel during uniaxial tension process is reproduced by the model. The material consists of ferrite (F), martensite, and retained austenite (RA) with rolling texture in the initial state. After deformation, the content of RA decreases and the <111> fiber of the RA, the <110> fiber of the ferrite and martensite along the tensile direction are enhanced. Phase transformation enhances the strength and work hardening rate but has little effect on the texture evolution. According to the distribution of stress and strain during the calculation of deformation, ferrite and tempered martensite (TM) contribute most to the deformation, and the new martensite (NM) is the most probable nucleation sites of fracture due to its highest average stress.
