2445 results about "Surface roughness" patented technology

The invention relates to the technical field of industrial machine vision detection and image processing, in particular to an automobile part production mold surface smoothness detection system based on image enhancement, which comprises a data acquisition module used for acquiring an original grayscale image of the surface of an automobile part mold; performing low-pass filtering processing on the original grayscale image to eliminate imaging thermal noise; the manifold reconstruction module is used for constructing a structure tensor field; reversely deducing a pseudo-curvature field of the mold surface; the adaptive enhancement module is used for generating a corrected image; constructing a texture orthotropic diffusion model; generating a texture reconstruction reference image; the surface metering module is used for calculating the difference between the corrected image and the texture reconstruction reference image and generating a defect saliency image; calculating the surface roughness value of the mold surface; according to the method, the problem that design textures and abnormal scratches are difficult to distinguish in the prior art is effectively solved, and the technical bottleneck that micro defects are easily missed in a complex geometric structure in traditional visual detection is overcome.

The invention provides an intelligent control method for a floodlight illumination system, and the method comprises the steps: obtaining an environment surface roughness coefficient, spectral reflection distribution and spatial geometric change data, determining the wall surface material absorptivity and shielding region coordinates, and obtaining environment reflection characteristics and shielding distribution data; analyzing shielding boundary sharpness and shielding angle distribution according to the spatial geometric change and the coordinates of the shielding area, calculating a luminous flux distribution curve of a local dark area, and judging whether the influence range of the dark area exceeds a preset threshold range or not; calculating the illumination coverage integrity according to the updated light field distribution and polarization reflection characteristics, determining the boundary sharpness of a light beam, and obtaining a light field uniformity parameter; and according to the light field uniformity parameter and the shielding thermal radiation effect, calculating and optimizing light field parameters.

The invention provides a robot milling surface roughness prediction method and system based on parallel convolution and coordinate attention mechanism feature fusion. According to the method, firstly, multi-source heterogeneous high-frequency signals in the milling process of a robot are synchronously collected, empty slice segments are eliminated and environmental noise is filtered out by utilizing a self-adaptive threshold value and a robust statistical method, and a standardized time sequence sample set is constructed; secondly, establishing a multi-source heterogeneous feature fusion roughness prediction model, and independently extracting deep features of cutting force and vibration signals by adopting a double-branch parallel convolution architecture; a coordinate attention mechanism is introduced, heterogeneous modal features are mapped into a virtual two-dimensional topological structure, the dynamic relative contribution degree of cutting force and vibration signals changing along with the machining state is captured through pooling aggregation along the modal dimension, and an attention weight map is generated to conduct dynamic weighting on the features. And finally, outputting a surface roughness predicted value through a regression network. According to the method, the problem that an existing fusion method neglects the dynamic dependency relationship between physical quantities is effectively solved, the anti-interference capability of the robot in the weak rigidity machining process is enhanced, and the precision and robustness of surface roughness prediction are remarkably improved. The method can be widely applied to robot complex component machining quality analysis and intelligent optimization, and has high efficiency and reliability.

The invention belongs to the field of metal materials, and particularly relates to a high-strength hot-rolled pickled steel plate for an automobile chassis and a production method thereof. The steel plate is mainly suitable for manufacturing automobile chassis structural parts. The steel comprises the following chemical components: C, Si, Mn, Al, V, Ni, Cu, Cr, Pt and Y, P is limited to be less than or equal to 0.015%, S is limited to be less than or equal to 0.005%, N is limited to be less than or equal to 0.006%, and the balance is Fe and inevitable impurities. The yield strength of the steel plate ranges from 498 MPa to 552 MPa, the tensile strength ranges from 608 MPa to 643 MPa, the transverse elongation A ranges from 33% to 38%, the steel plate is qualified after transverse cold bending by 180 degrees D = a, the surface quality is good, oxide scale stripes and chromatic aberration defects are avoided, the surface roughness Ra ranges from 1.10 micrometers to 1.35 micrometers, and the steel plate has excellent corrosion resistance.

The invention belongs to the field of metal materials, and particularly relates to a 448 MPa grade hot-rolled pickled steel plate for an automobile and a production method of the 448 MPa grade hot-rolled pickled steel plate. The steel plate is mainly suitable for manufacturing automobile chassis structural parts. The steel comprises the following chemical components: C, Si, Mn, Al, Nb, Ta, Zr and Y, P is limited to be less than or equal to 0.015%, S is limited to be less than or equal to 0.005%, N is limited to be less than or equal to 0.003%, and the balance is Fe and inevitable impurities. The yield strength of the steel plate is larger than or equal to 308 MPa, the tensile strength is larger than or equal to 448 MPa, the transverse elongation A is larger than or equal to 42%, the hole expansion rate is larger than or equal to 125%, transverse cold bending is conducted by 180 degrees, D = a, the steel plate is qualified, the surface quality is good, oxide scale stripes and chromatic aberration defects are avoided, the surface roughness Ra is 1.00-1.40 micrometers, and the steel plate has good corrosion resistance.

The invention relates to the technical field of numerical control machining process optimization, in particular to an aluminum alloy milling parameter optimization method based on an improved Bayesian neural network, and the method comprises the following steps: S1, building an aluminum alloy thin-wall part milling finite element model based on a Johnson-Cook constitutive model; s2, carrying out a single-factor milling experiment, and verifying the reliability of the finite element model; s3, constructing a Bayesian neural network model fused with a multi-head attention mechanism, and establishing a mapping relation between process parameters and surface quality indexes; s4, establishing a multi-objective optimization model based on the MHA-BNN prediction model, and solving by adopting an NSGA-II algorithm; and S5, performing experimental verification and analysis on an optimization result. According to the method, a multi-head attention mechanism is introduced into a Bayesian neural network, and a milled surface roughness and residual stress probability prediction model is constructed; and a multi-objective optimization framework fusing physical constraint and data driving is established, collaborative optimization of the surface roughness, the residual stress and the processing efficiency is realized, and meanwhile, the problem that a traditional deep learning method is insufficient in prediction precision under a small sample condition is solved.

The invention discloses a nondestructive testing method for a welding seam of a steel structure. The method comprises the steps that firstly, morphology data of the surface of a building steel structure are obtained through a high-precision laser scanner and an electromagnetic induction technology; coating thickness distribution and weld surface roughness are analyzed based on the morphology data, and a surface information matrix is constructed; then automatically adjusting ultrasonic wave beam parameters of the ultrasonic phased array by using the adaptive parameter regulation and control model and executing detection to obtain a preliminary ultrasonic echo signal; the time-frequency domain difference between the preliminary echo and the reference signal is calculated through an echo signal compensation model, and secondary compensation is completed to obtain an optimized ultrasonic echo signal; fusing the optimized echo signal, the morphology data and the surface information matrix to construct a detection data set, and adopting a pre-trained steel structure detection model to realize defect detection; and finally, evaluating the confidence of a detection result by adopting a three-dimensional confidence evaluation model, performing grading, and performing local re-detection and parameter adjustment on a low-confidence region to form closed-loop optimization. According to the method, the surface state of the steel structure can be adaptively matched, the accuracy of echo signals and the defect detection precision are improved, and the detection reliability is guaranteed through three-dimensional confidence evaluation.

The invention provides a construction method and system of a rock-soil shear strength parameter prediction model, and relates to the technical field of model construction.The construction method comprises the steps that CT scanning data of a real rock-soil sample is obtained and preprocessed, and an irregular three-dimensional particle set for geometric modeling is obtained; performing particle surface roughness modeling analysis on the three-dimensional particle body set to obtain a surface roughness coefficient set of the three-dimensional particle body set; performing particle size cumulative distribution function segmentation processing and fractal dimension control on the surface roughness coefficient set to obtain a structure grading sample set; constructing a THMC multi-field coupling model based on the structure grading sample set to obtain a working condition particulate matter physical response parameter set; and performing a simulation experiment on the working condition particle physical response parameter set, and performing deep neural network modeling processing based on an experiment result to obtain a strength response model capable of predicting the cohesive force and the internal friction angle of the rock-soil body under any working condition input. According to the invention, the prediction efficiency and accuracy are significantly improved.

The invention relates to the technical field of engine testing, and discloses a reliability test method and system for a small general engine, and the method comprises the steps: collecting an engine vibration signal through a superconducting quantum interferometer, and collecting a temperature distribution parameter through a quantum dot temperature sensor; performing combined testing of cold and hot shock, high-load operation and random working conditions, and synchronously monitoring dynamic response characteristic data; constructing a Weibull probability model, and determining key influence factors through a global sensitivity analysis method; acquiring a surface roughness parameter by adopting a field emission scanning electron microscope, obtaining a material component parameter through energy spectrum analysis, and establishing a parameter mapping relation; establishing a vibration and temperature early warning system, executing a graded sweep frequency excitation and abrasive particle acceleration test, and recording a parameter degradation process; and generating a three-dimensional parameter cloud picture, and outputting a reliability test report. According to the method, the reliability of the small general engine can be comprehensively and efficiently evaluated, and a scientific basis is provided for design, maintenance and improvement of the engine.

The invention provides a multi-axis linkage engraving equipment control algorithm optimization method, which belongs to the technical field of engraving equipment, and comprises the following steps of: decomposing a model into a plurality of curved surface segmentation segments according to a curvature change rate, and performing NURBS curve parameterization processing on each segment to generate control point coordinates and weight coefficient data; a dynamic weight adjustment model is adopted to dynamically calculate a path planning weight coefficient according to a tool cutting force value, a surface roughness demand value and a machining efficiency index value, a minimum spanning tree path algorithm is utilized to carry out path connection sequence planning, and a layered interpolation operation framework combining preprocessing interpolation calculation and real-time interpolation compensation is established. And the optimized interpolation coordinate data is converted into an axis control instruction sequence containing a position instruction, a speed instruction and an acceleration instruction, and the technical problem that the interpolation calculation precision and the real-time performance are difficult to balance when the multi-axis linkage carving equipment is used for machining a complex curved surface is solved.

The invention belongs to the field of metal materials, and particularly relates to a high-elongation 288MPa-grade hot-rolled pickled steel plate for an automobile and a production method thereof. The steel plate is mainly suitable for manufacturing automobile chassis structural parts. The steel comprises the chemical components of C, Si, Mn, Al, Ti, Zr, Ni, Pt and Y, P is limited to be smaller than or equal to 0.015%, S is limited to be smaller than or equal to 0.005%, N is limited to be smaller than or equal to 0.003%, and the balance is Fe and inevitable impurities. The steel plate has excellent mechanical properties, the yield strength of the steel plate is larger than or equal to 238 MPa, the tensile strength is larger than or equal to 288 MPa, the transverse elongation A is larger than or equal to 48%, the hole expansion rate is larger than or equal to 132%, transverse cold bending is conducted by 180 degrees, D = a, the steel plate is qualified, the surface quality is good, oxide scale stripes and chromatic aberration defects are avoided, the surface roughness Ra is 1.08-1.48 micrometers, and the steel plate has good corrosion resistance.

The invention relates to a method for optimizing electroplating and etching process parameters, which comprises the following steps of: establishing an interface migration behavior relationship by analyzing the influence of metal ions, doped impurities and surface roughness factors on a reaction interface migration rate and a boundary layer flow behavior in an electroplating and etching process; interface migration behaviors are combined with parameter sensitivity analysis, the influence of process parameters on the interface behaviors is dynamically evaluated, and priority optimization sorting is carried out; dynamic interval zooming is carried out on the sensitive parameters by using the fitted sensitivity function, and a local adjustment range is generated according to material types and graph complexity; a preorder process memory mapping relation is introduced, and morphology evolution information recorded in the electroplating process is transmitted to an etching parameter optimization module; local fine adjustment is carried out on the etching rate, angle control and a disturbance mechanism, and the previous electroplating state is responded; collecting a fluctuation interface feedback signal; establishing a closed-loop optimization control loop of micro-disturbance, response evaluation and parameter correction; and the process consistency and the structure uniformity are improved.

The invention provides a seed screening method and system based on a laser radar technology, and relates to the technical field of agricultural automation, and the method comprises the steps: extracting geometric features and defect features from optimized point cloud data; extracting mildew and insect pest spectral features from the optimized spectral data; a multi-dimensional feature set is obtained; calculating seed plumpness, roundness and surface roughness based on the optimized point cloud data; calculating a sag index and a crack feature based on point cloud edge detection; fusing the geometric features, the defect features and the pest spectral features to generate a comprehensive score; a machine learning model is utilized to execute final grading, and a first probability predictor based on laser radar features and a second probability predictor based on pest spectral features are trained respectively; and inputting the probability outputs of the two predictors into a second-layer grading model of the meta-learning architecture, and outputting a final quality grade. According to the method, efficient and accurate seed screening is realized through automatic process and algorithm design.

The invention provides a continuous depth gradually-changing grating structure based on a silicon carbide substrate and a preparation method thereof. The preparation method comprises the following steps: forming a sacrificial layer with gradually-changing thickness on the silicon carbide substrate; etching is carried out by adopting a two-step plasma etching process; and polishing the surface through a plasma polishing process. According to the invention, the gradient sacrificial layer is combined with two-step plasma etching, and a high-precision continuous depth gradient grating structure is realized on the silicon carbide substrate, so that the diffraction requirements of RGB three-color wavelengths are met; the micro-groove defect in the etching process is eliminated, the problem that the depth-to-width ratio and the surface quality cannot be considered in the traditional process is solved, and a key technical support is provided for mass production of the single-layer full-color optical waveguide; the transmission loss of the optical waveguide is reduced by controlling the surface roughness; and the product yield is improved, the production cost is reduced, and the method is suitable for large-scale production.

The invention discloses a grinding and polishing processing method of an ultra-precise YIG (yttrium iron garnet) single crystal, which is suitable for a planar YIG (yttrium iron garnet) single crystal (circle) prepared by a liquid phase epitaxy method, a molten salt method and a floating zone melting method, and comprises the following steps: grinding the YIG single crystal, and controlling the thickness and the total thickness deviation (TTV) of the crystal; rough polishing is carried out on the YIG single crystal, and subsurface damage of the wafer is eliminated; and finally, carrying out ultra-precision polishing on the YIG crystal to realize surface planarization with sub-nano-scale surface roughness. According to the method, the surface roughness of the yttrium iron garnet single crystal can be controlled below 1nm.

The invention discloses a conveying device for detecting the protruding height of an oil injector, and relates to the technical field of oil injector detection. Wherein the conveying piece comprises a rolling shaft and an anti-skid sleeve which are connected in a sleeved mode, the roughness of the surfaces of the rolling shaft and the anti-skid sleeve is different, and the rolling shaft is driven by a motor to rotate. According to the conveying device for detecting the protruding height of the oil sprayer, the rolling shaft and the anti-skid sleeve which are connected in a sleeving mode are adopted, the roughness of the surface of the anti-skid sleeve is increased, and therefore the roughness of the device is changed according to needs, and the problem that when an existing conveying device for detecting the oil sprayer is used, the height of the oil sprayer is too large is effectively solved. The technical problem that the roughness of the conveying belt cannot be freely adjusted according to needs is solved, the purpose of freely adjusting the surface roughness of the conveying device is achieved, the surface roughness of the conveying device can be adjusted according to the machining condition of the air cylinder cover, various machining requirements are met, and the applicability of the device is improved.

The invention relates to a surface material uniformity analysis method and system for an insulator of a power distribution network, and belongs to the technical field of data processing, and the method comprises the following steps: S1, obtaining an optical image, a thermal imaging image and ultrasonic echo data of the surface of the insulator; s2, performing image processing on the acquired optical image, extracting characteristic parameters of surface roughness, texture direction and texture consistency, and constructing a surface texture uniformity index; s3, thermal diffusivity and thermal resistance of different areas are calculated from the thermal imaging image, thermal parameter differences of the areas are compared, and a thermal performance uniformity evaluation value is formed; s4, aiming at the ultrasonic echo data, establishing a correlation model of the ultrasonic characteristics and the internal uniformity of the material; s5, the fusion analysis model comprehensively evaluates the uniformity of the surface material of the insulator; the method has the beneficial effects that the fusion analysis model realizes comprehensive evaluation of the uniformity of the surface material of the insulator from multi-dimensional data.

The invention discloses a manufacturing method of 925 silver jewelry, which comprises the following steps: S1, preparing alloy components, S2, designing a cast tree structure, S3, casting, and S4, densifying a casting, the alloy components comprise 92.5-93.0% of silver, 1.0-3.0% of platinum, 0.05-0.5% of copper, 1.5-3.5% of zinc, 1.5-3.5% of indium, 0.03-0.3% of tin, 0.05-0.5% of germanium, 0.03-0.3% of rare earth, and 0.003-0.02% of boron. According to the manufacturing method, alloy components are optimized, the casting performance of the alloy components is improved, the casting process is optimized, the feeding capacity of molten metal is improved, shrinkage porosity and air holes are reduced, the initial density of the casting is improved, and subsequent composite treatment is carried out, so that the density from the surface to the shallow surface of the casting is further improved, grains are refined, and the surface roughness is further reduced; and the requirement of high brightness of the jewelry surface is met.

The invention relates to a method for quickly depositing and modifying the surface of a polypropylene base material and the polypropylene base material subjected to surface deposition modification. The method comprises the following steps: pretreating the surface of the polypropylene base material; dissolving with a Tris-HCl buffer solution to prepare a dopamine solution and a NaIO4 solution; the preparation method comprises the following steps: mixing a prepared dopamine solution and a prepared NaIO4 solution according to a preset proportion, uniformly stirring to form a mixed solution, immersing a polypropylene base material, and reacting for preset time to form a deposition layer on the surface of the polypropylene base material. By optimizing an oxidizing agent system, the reaction time can be shortened to be within 30 minutes as soon as possible, meanwhile, the excellent performance of a deposition layer is kept, synergistic improvement of surface roughness, hydrophilicity and active functional group density is achieved, a theoretical foundation is laid for expanding application of the method in the fields of automobiles, packaging, medical instruments and the like, and the method is worthy of popularization and application. The technology can also be applied and popularized to surface functionalization of other non-polar polymers, and has great industrial application significance.

The invention provides a grinding fluid, the grinding fluid comprises a base fluid, a grinding material, nano-diamond and a dispersion stabilizer, the base fluid comprises polyether and silicone oil, the grinding material comprises surface modified silicon carbide particles, and the dispersion stabilizer comprises polyethylene glycol stearate and polyvinylpyrrolidone. The surface roughness of a grinding piece can be reduced, the scratch density and microcracks of the surface are reduced, damage of grinding fluid to the grinding piece is reduced or even avoided, and the service life of a cutter can be prolonged. The invention also provides a preparation method of the grinding fluid and a process method using the grinding fluid.

The invention relates to the technical field of ceramic materials, in particular to a method for reducing the surface roughness of a silicon nitride instant-fired sheet, which comprises the following steps: S1, mixing alpha-silicon nitride, beta-silicon nitride, absolute ethyl alcohol and herring fish oil, grinding and dispersing to obtain basic mixed powder; s2, mixing, grinding and dispersing the filling mixed powder, absolute ethyl alcohol and herring fish oil, then carrying out filter pressing and drying, calcining and regrinding to obtain ground filling mixed powder; s3, mixing and ball-milling the basic mixed powder, the ground filling mixed powder, a solvent, an adhesive, a plasticizer and a dispersing agent to obtain slurry; s4, casting the slurry into a film; s5, attaching the thin film to the surface of the silicon nitride green body, and pressing the silicon nitride green body into a whole piece through an isostatic press to obtain a pressed green body; s6, a boron nitride isolation layer is printed on the surface of the pressed green body, and the green body with the isolation layer is obtained; and S7, stacking the green bodies with the isolating layers, and then carrying out adhesive removal and sintering reaction to obtain the silicon nitride ceramic chip.

The invention discloses a part polishing surface roughness prediction method and device, equipment and a storage medium. Comprising the following steps: training a roughness prediction model of the cascaded long-short-term memory network based on a constructed sample data set to obtain a trained roughness prediction model; the polishing process parameters of the part and the surface roughness of the previous moment are collected on line; the polishing process parameters and the surface roughness at the previous moment are input into the roughness prediction model, and a change value sequence of the surface roughness of the part is obtained from the current moment within k moments after the current moment; and on the basis of the change value sequence of the surface roughness of the part and the surface roughness at the previous moment, obtaining the predicted surface roughness at k moments after starting from the current moment. The invention provides a part polishing surface roughness prediction method based on a cascaded long-short-term memory network model. The part surface roughness can be predicted on line under the complex polishing working condition.

The invention relates to the technical field of aero-engine quality inspection, and discloses an aero-engine part machining quality detection method based on data management. The method comprises the following steps: collecting multi-source machining data of a part, wherein the multi-source machining data comprises dimensional tolerance, surface roughness and material hardness data; key quality features are extracted according to a design drawing, and the key quality features and the multi-source processing data are subjected to time sequence alignment according to process stages; a three-dimensional mass space model with the process stage as the horizontal axis, the key quality features as the longitudinal axis and the multi-source machining data as the depth axis is constructed, and a mass distribution thermodynamic diagram is generated; identifying an abnormal process region based on the thermodynamic diagram, extracting a corresponding data subset, inputting the data subset into the dynamic convolution kernel quality analysis network, and outputting a process abnormal probability value; when the probability value exceeds a threshold value, triggering cross-process correlation analysis, and fusing upstream and downstream process data to generate a composite quality feature vector; and an abnormal type is judged through a cascade classifier, and a compensation strategy library is called to generate a machining parameter adjustment instruction set.

The invention belongs to the technical field of aluminum alloy material preparation, and particularly relates to an optical microcrystalline aluminum material, a preparation method and application, an aluminum alloy ingot is extruded twice to obtain an extruded bar, the extrusion temperature is 275-325 DEG C and the extrusion ratio is 2-8 during primary extrusion, and the extrusion temperature is 375-425 DEG C and the extrusion ratio is 20-30 during secondary extrusion; the extruded bar is sequentially subjected to annealing treatment, drawing treatment and stabilizing heat treatment, the optical microcrystalline aluminum material is obtained, and the temperature of the stabilizing heat treatment is 90-240 DEG C; the grain size and the surface roughness are reduced, and meanwhile the hardness is improved.

The invention belongs to the field of metal materials, and particularly relates to a hot-rolled pickled steel plate for an automobile chassis and a production method thereof. The steel plate is mainly suitable for manufacturing automobile chassis structural parts. The steel comprises the following chemical components: C, Si, Mn, Al, Ta, Ni, Cu, Cr, Pt and Y, P is limited to be less than or equal to 0.015%, S is limited to be less than or equal to 0.005%, N is limited to be less than or equal to 0.003%, and the balance is Fe and inevitable impurities. The yield strength of the steel plate is larger than or equal to 265 MPa, the tensile strength is larger than or equal to 378 MPa, the transverse elongation A is larger than or equal to 46%, transverse cold bending is conducted by 180 degrees, D = a, the steel plate is qualified, the surface quality is good, oxide scale stripes and chromatic aberration defects are avoided, the surface roughness Ra is 1.10-1.35 micrometers, and the steel plate has good corrosion resistance.

The invention provides a gear contact fatigue life prediction method considering macro-micro geometric parameter influence. The method comprises the following steps: firstly, establishing a tooth surface contact-dynamics analysis model considering macro-micro geometric parameter influence, and calculating tooth surface dynamic load distribution; secondly, establishing a calculation model of the stress on the surface and below the surface of the gear considering the influence of lubrication and surface roughness, and calculating the oil film pressure and the stress on the surface and below the surface of the gear; and finally, establishing a gear contact fatigue life prediction model considering the influence of macro and micro geometric parameters, and calculating the contact fatigue life of the gear. According to the method, the influence of the rigidity change of the gear on the contact fatigue life of the gear is considered, the contact fatigue life of gears with different web structures is predicted, and the deviation between the contact fatigue life obtained through theoretical calculation and the actual contact fatigue life is reduced. Meanwhile, the method considers the influence of the error on the contact fatigue life of the gear, so that the prediction of the contact fatigue life of the gear with the modification and the error is realized.

The invention provides a big data-based building construction safety assessment method, which comprises the following steps of: acquiring a tension distribution gradient, a local stress peak value, a tension fluctuation frequency and a pulley contact surface friction coefficient of a tower crane steel wire rope under a dynamic working condition, and determining a dynamic stress distribution characteristic of the steel wire rope; performing real-time data screening and peak value identification processing on the dynamic stress distribution characteristics to obtain a stress concentration coefficient distribution diagram of each measuring point, and determining a tension concentration area with the stress concentration coefficient higher than a preset threshold value; carrying out weighted adjustment on the non-uniform wear characteristics, calculating to obtain the surface wear depth, the wear particle size and the weight change of the contact surface temperature rise under different working conditions, and obtaining the real-time wear index weight distribution; and aiming at the real-time wear index weight distribution, combining the tension distribution gradient and the wear region area in the historical operation data, and analyzing and predicting the change trend of the surface roughness of the steel wire rope through wear trend fitting and load history to obtain a residual life prediction value.

The invention discloses a vibration suppression and surface quality control method for part cutting of a numerical control machining center, which relates to the technical field of numerical control machining and comprises the following steps: multi-modal sensor fusion monitoring: mounting multi-modal sensors on key parts such as a main shaft, a cutter and a workbench of the numerical control machining center, comprising an acceleration sensor, a strain sensor and an acoustic emission sensor and monitors a vibration signal, a cutting force signal and an acoustic emission signal in the machining process in real time; self-adaptive machining parameter optimization is carried out, and machining parameters are preset according to machining task requirements; dynamic adjustment and intelligent feedback control of machining parameters are realized by adopting a vibration prediction model based on deep learning and an adaptive optimization algorithm; according to the technology, the automation level of the machining process is improved, machining errors and surface quality defects caused by vibration are effectively reduced, and the surface roughness and size precision of the workpiece are remarkably improved.

The invention discloses a gear pair life prediction method based on dynamic stress, belongs to the technical field of reliability design of mechanical transmission systems, and aims to solve the problem of inaccurate life prediction caused by neglecting a dynamic effect and a real surface topography in an existing gear design method. The method comprises the following steps: firstly, acquiring gear pair interface mechanical property parameters considering shafting vibration, dynamic load and surface roughness, wherein the parameters comprise transient oil film pressure, friction coefficient, oil film thickness and shear stress; solving a three-dimensional subsurface stress field by adopting an algorithm and calculating von Mises equivalent stress distribution; stress volume integration is conducted on different rough surfaces, the honed surface is used as the reference, the relative fatigue life of other surfaces is calculated based on the Zaretsky life prediction theory, and accurate prediction and process optimization of the contact fatigue life are achieved; meanwhile, the dynamic bending stress of the tooth root is calculated based on the dynamic normal load, and the bending fatigue safety coefficient is checked.

The invention relates to a photoetching ordered microcosmic fixed abrasive polishing pad and a preparation method thereof. Compared with the prior art, the method has the advantages that the to-be-photoetched surface of the substrate is photoetched according to the photoetched pattern, so that the plurality of microscopic grinding columns are orderly distributed and formed on the grinding material base according to the preset distribution and arrangement mode, the distribution shape, direction and position of the microscopic grinding columns on the grinding material base are kept unchanged, and regular distribution and uniform distribution can be presented; the distribution and the size of the micro grinding columns can be adjusted according to the photoetching pattern to meet different polishing requirements, then the micro grinding columns of the substrate are compositely filled with the filling body to keep the stability of the micro grinding columns, and the polishing pad can be suitable for the micro level to meet the requirement of the manufacturing procedure below 5nm, so that the production efficiency is improved, and the production cost is reduced. The microcosmic grinding columns are formed through photoetching to replace traditional grinding particles, the chemical stability of the polishing pad during polishing is improved, the removal rate is increased, the surface roughness is reduced, and the polishing effect of the polishing pad on a polishing product is improved.