15693results about "Computational materials science" patented technology

The invention relates to the technical field of 3D printing models, in particular to a 3D printing model slice feature compensation method and system and a medium. The method comprises the following steps that a 3D printing model is collected, a model feature skeleton is split, transverse slice features are generated, then a 3D printing environment is collected, virtual simulation is carried out, environment stress analysis is carried out on the skeleton slice features to obtain environment stress data, then deformation scale deduction and thermal deformation prediction are carried out based on the environment stress data, and a thermal deformation model is obtained. On the basis, reverse deformation simulation is carried out, deformation repair requirements are evaluated, a deformation compensation path is planned, finally, compensation simulation of a model feature skeleton is achieved, non-target deformation is analyzed, a compensation path is adjusted, and therefore the feature compensation method is optimized. According to the 3D printing model slice feature compensation method, the accuracy and quality of the 3D printing model are integrally improved.

The present invention relates to a method for constructing a digital twin system for an additive manufacturing process with lifecycle data management. The digital twin system for an additive manufacturing process constructed by the method includes a data acquisition and supervisory control system, a data management and storage system, a manufacturing executing system, a configuration model, a simulation system, and a multi-physics simulation system. The present invention can fully and accurately characterize the molding quality of parts, thereby saving a lot of time required for offline data processing, and achieving real-time acquisition and updating of data.

The invention discloses a loess tunnel surrounding rock deformation monitoring method, and relates to the technical field of civil engineering and geological engineering, and the method comprises the steps: integrating a spiral winding type optical fiber sensor, a double-cavity humidity compensation vibrating wire sensor and a microseismic array, and capturing surrounding rock strain, vibration and geological activities; the vibrating wire sensor suppresses humidity interference through a silicone oil damping medium and self-adaptive excitation frequency, and the optical fiber sensor is fixed through a pre-embedded silica gel sleeve to adapt to surrounding rock deformation; edge computing nodes are deployed on the inner wall of the tunnel, an FPGA chip and a lightweight GRU model are integrated, optical fiber strain, a micro-seismic energy spectrum and laser point cloud displacement field data are fused in real time, the strain gradient is analyzed, and a crack propagation probability cloud picture is generated; redundant optical fiber link switching is combined with a six-degree-of-freedom mechanical arm to realize breakpoint self-repairing, and a self-cleaning air curtain is integrated to inhibit dust adhesion; and constructing a geological parameter library based on a BIM-GIS fusion platform, driving a finite element-discrete element coupling model to dynamically update boundary conditions, and predicting surrounding rock deformation and collapse risks.

The invention discloses a plug-in inductor winding process control method based on production parameter dynamic compensation, and provides a closed-loop compensation control strategy taking a physical field evolution model as a core aiming at the problems of unstable sensing quantity and non-uniform structure in the traditional process. The method comprises the following steps: firstly, constructing an ideal physical field reference model according to target inductance parameters and a structure, and describing an evolution law of characteristics such as a magnetic field, an electric field or impedance in an ideal state; in the actual winding process, a physical field signal is collected through a sensor, production parameters such as wire tension and main shaft rotating speed are obtained, and a physical field distribution snapshot of the current winding state is generated. And the system compares the extracted actual features with the reference model, identifies a dominant disturbance source causing deviation in combination with real-time production parameters, and outputs a disturbance type label carrying causal information. And according to a label and a production parameter change trend, a compensation instruction is dynamically generated and executed, fine adjustment of tension, a winding displacement track or density is realized, and consistency and precision of an inductor finished product are ensured.

The invention discloses an asphalt pavement fatigue damage model calibration method based on AI and digital twinning, and relates to the technical field of damage detection. Compared with the prior art, the problems that traditional asphalt pavement design depends on empirical formulas and static parameters, material aging, environmental coupling and load uncertainty are difficult to dynamically reflect, and cross-scale correlation between microscopic interface behaviors and macroscopic structure performance is lacked are solved; the method comprises the following steps: collecting and preprocessing multi-source heterogeneous data in real time through a multi-modal data fusion and dynamic sensing system, simulating and accurately quantifying asphalt-aggregate interface binding energy and adhesion work in combination with molecular dynamics, and constructing a cross-scale damage evolution model; a physical information neural network is used for embedding an improved Paris formula, actually measured data and a physical rule are deeply fused, and the locality hypothesis of a traditional model is effectively corrected.

The invention relates to the technical field of welding, in particular to a corrosion steel welding cooperative control method and system. Comprising the following steps that welding seam geometric parameters, molten pool dynamic characteristic parameters and welding heat input parameters in the corrosion steel welding process are collected in real time through a multi-dimensional sensor array; constructing a corroded steel welding seam feature space model based on the welding seam geometric parameters, and determining material corrosion grade distribution and mechanical property parameters of a welding seam area in combination with a preset corroded steel material database; a molten pool form evolution prediction model is established through an adaptive Kalman filtering algorithm by utilizing the dynamic characteristic parameters of the molten pool and the welding heat input parameters, and the solidification behavior and the welding seam forming trend of the molten pool are predicted in real time; according to the material corrosion grade distribution, the mechanical property parameters and the molten pool forming trend, a dynamic adjustment strategy of the welding process parameters is generated through a multi-objective optimization algorithm; the reliability and safety of the corrosion steel welding joint can be improved.

The invention discloses a freezing and thawing stratum grouting numerical analysis modeling method and system, particularly relates to the technical field of hydraulic engineering numerical simulation, and is used for solving the problem of grouting parameter prediction inaccuracy caused by neglecting the dynamic coupling relationship between the thermodynamic behavior of a phase-change material and a seepage field and a damage field in an existing grouting model. A dynamic correlation system of phase change energy storage performance parameters and seepage field-damage evolution is established, a slurry diffusion path priority channel weight is corrected based on a freeze-thaw interface migration track, a bidirectional iteration equation of a seepage field and a diffusion migration field is coupled, and multi-physical field spatial-temporal scale cooperative solution is realized through a separated iteration algorithm; through multi-dimensional coupling of thermodynamic characteristics of the phase-change material, heterogeneous evolution of seepage paths and damage mechanical response, the prediction precision of the mechanical characteristics of the grouting stone body and the slurry diffusion range under the action of freeze-thaw cycle is remarkably improved, and the leakage and structural instability risks are effectively reduced.

The invention provides a switch cabinet solid insulating material performance detection method and system based on temperature detection, and relates to the technical field of temperature detection, and the method comprises the steps: constructing a grid detection matrix on the surface of a solid insulating material, collecting real-time temperature data through a passive radio frequency identification temperature sensor, and building an initial state model; adopting a deep learning algorithm to calculate a temperature gradient value and thermal stress distribution, and constructing a performance degradation index; and the recurrent neural network is combined to predict a performance evolution trajectory, and dynamic early warning and diagnosis are realized. The performance degradation state of the solid insulating material can be accurately evaluated, the detection precision and the prediction accuracy are improved, and intelligent monitoring and early warning are achieved.

The invention provides a multi-heat-source multi-material additive manufacturing optimization method and system based on machine learning, and relates to the field of additive manufacturing, and the method comprises the following steps: collecting process parameters, material physical properties and target response data in real time, and constructing a multi-source data set; on the basis, a multi-task learning model is trained, common features are extracted, target responses are synchronously predicted, a proxy target function is constructed through predicted values, if errors exceed a threshold value, experimental verification is conducted, and new data is fed back to update the model. And applying the weight coefficient to a multi-objective evolutionary algorithm, iteratively screening a Pareto candidate solution set, verifying a high-uncertainty solution, and updating the model to error convergence. And selecting and deploying an optimal parameter combination by using an entropy weight-TOPSIS method, and embedding an online learning mechanism to form closed-loop optimization. According to the method, multi-task learning and a self-adaptive evolutionary algorithm are combined, and efficient optimization of multi-heat-source multi-material additive manufacturing process parameters can be achieved.

The invention discloses a reef limestone strength prediction method based on pore feature machine learning, and belongs to the technical field of crossing of geotechnical engineering and artificial intelligence, and the method comprises the steps: obtaining a reef limestone three-dimensional digital core, and extracting pore features after preprocessing; establishing a multi-source pore-physical coupling feature data fusion framework based on the pore features; integrating a random forest and a gradient boosting tree, dynamically weighting parameters in the pore features, designing a composite loss function, performing hierarchical training, and embedding physical constraints to construct a reef limestone strength prediction machine learning model; layered K-fold cross validation is adopted, the robustness of the reef limestone strength prediction machine learning model is tested, and interpretability is verified. According to the method, through data processing, the reef limestone strength prediction machine learning model is constructed and optimized, the relationship between the pore characteristics and the reef limestone strength is deeply excavated, the model prediction accuracy, reliability and interpretability are improved, and an effective solution is provided for reef limestone strength prediction.

The invention relates to the technical field of data processing, and discloses an anti-oxidation barrier layer performance degradation prediction method and system based on multi-source data. The method comprises the following steps: collecting and preprocessing multivariate data of a barrier layer environment, and constructing three-dimensional tensor data; indexes such as oxygen blocking efficiency and anti-permeability performance are calculated, and performance time sequence data are obtained; extracting multi-dimensional features and fusing the multi-dimensional features through an automatic encoder; applying a hybrid deep learning model to predict performance degradation; analyzing degradation curve characteristics, and executing mode clustering to obtain a risk matrix; and optimizing a maintenance decision scheme based on risk assessment. According to the invention, the hybrid deep learning model is utilized to capture the dependency relationship of time and space dimensions at the same time, and the prediction precision is significantly improved; based on degradation mode identification and risk level evaluation, accurate multi-scene maintenance decision is realized, environmental risk is reduced, and maintenance cost is optimized.

The invention relates to a method for establishing and optimizing a multi-scale prediction model for constitutive parameters of a fiber reinforced composite material. Comprising the following steps: constructing a fiber-matrix microscopic RVE model, applying a boundary condition and a load, determining a failure criterion, and calculating equivalent mechanical parameters of a fiber bundle; a fiber bundle-matrix mesoscopic RVE model is generated, fiber bundle equivalent mechanical parameters serve as material attributes of fiber bundles in the mesoscopic RVE, a viscoelastic model is adopted for a matrix, and fiber reinforced material unit cell equivalent mechanical parameters are calculated; constructing a macroscopic fiber reinforced composite material finite element model, endowing a macroscopic model with unit cell equivalent mechanical parameters, defining an orthotropic material card, simulating an explosion impact process by adopting an explicit dynamics algorithm, outputting structural displacement, a damage area and stress distribution, and comparing calculation and test data. And correcting the microscopic RVE model and the mesoscopic RVE model through reverse optimization iteration to obtain a final macroscopic prediction model. The explosion-proof performance of the fiber composite structure is remarkably improved.

The invention discloses a multi-scale multi-deep neural network driven metal material fatigue life prediction method. The method comprises the following steps: constructing a metal material fatigue test data set; adding the maximum normal stress of the critical plane into a data set by considering the additional hardening effect of the metal material under a non-proportional load, and preprocessing the data set; establishing a multi-scale multi-depth neural network framework; considering physical constraint to guide neural network training so as to increase prediction precision, and establishing a physical loss function with soft regulation capability; building a metal material fatigue life prediction model driven by the multi-scale multi-depth neural network based on the multi-scale multi-depth neural network framework and the physical loss function; continuously iteratively optimizing the model to obtain an optimal prediction model; and predicting the fatigue life of the metal material by using the optimal prediction model. The method not only has the advantage of high prediction precision, but also can enhance the stability and generalization ability of the prediction precision.

The invention discloses a lightweight concrete column beam durability evaluation method and device based on twinborn simulation, and relates to the technical field of twinborn simulation, and the method comprises the steps: collecting the degradation characteristics of a lightweight concrete column beam, and constructing a three-dimensional multi-scale structure model; executing coupling simulation to generate a twin simulation model; monitoring a column beam through a multi-source sensor, and determining a real-time performance degradation parameter; updating the twinborn simulation model by using the degradation parameters; and collecting future environment and load data, inputting the future environment and load data into the updated model, simulating a degradation evolution process, and generating a durability evaluation result. The technical problem that a prediction result and actual service performance have significant deviation due to the fact that a traditional durability evaluation method is difficult to dynamically couple a multi-physical field environment and a material microcosmic degradation mechanism is solved, and a dynamic evaluation system for virtual-real mapping is constructed through twinborn simulation. And the prediction precision of the degradation process of the lightweight concrete column beam and the dynamic deduction capability of the service life are improved.

The invention discloses a dynamic leakage prediction method and system for an old oil and gas pipeline, and the method comprises the steps: collecting pressure, flow and temperature parameters in real time through a multi-source sensor, and recognizing abnormal fluctuation through the combination of time sequence analysis and frequency domain feature extraction; calculating a pipeline state evaluation result based on the material degradation model; establishing a leakage prediction model fusing a wall thickness degradation kinetic equation and an LSTM neural network, calculating a leakage probability by adopting a Monte Carlo method, and generating a diffusion velocity and a concentration gradient through CFD numerical simulation; when the diffusion prediction exceeds a safety threshold value, a control strategy is optimized through fuzzy logic and a genetic algorithm; the verification model is fed back after real-time adjustment, and online learning is carried out through Bayesian optimization; and finally, calibrating the model by using experimental data, and deploying and generating risk early warning. The system comprises a multi-source sensor array, a data processing platform and other modules, and full-chain closed-loop control from sensing to early warning is achieved.

The invention discloses a method and a system for determining the reaction activity of material molecules based on quantum computing, and relates to the technical field of material performance analysis based on quantum computing, and the method comprises the following steps: generating UCCSD (Unified Conduction Cathode Spectroscopy) simulation according to an electronic structure of molecules of a target material; obtaining at least one effective excitation operator based on parameters in the coefficient of each excitation operator in UCCSD simulation and by combining all the excitation operators; constructing a quantum circuit according to all the effective excitation operators; determining energy level distribution of molecules of the target material based on the quantum circuit; and determining the reaction activity of the molecules of the target material according to the energy level distribution of the molecules of the target material. According to the method, in the process of obtaining the effective excitation operator, an ADAPT-VQE algorithm does not need to be executed, so that the analysis cost of the physical and chemical properties of the material can be reduced, and the analysis efficiency of the physical and chemical properties of the material can also be improved.

Disclosed in the present invention is a macro-micro combination-based method for quantitatively evaluating a battery electrode charging strategy, comprising: constructing an electrochemical-stress model to obtain distribution and evolution rules of an electrode diffusion stress; according to the stress evolution rule in a lithiation process, using a multi-stage constant-current optimization strategy to perform rate optimization on the lithiation process, and on the basis of the stress of material yield strength, designing a rate optimization solution; separately performing macroscopic and microscopic quantitative verification on the rate optimization solution by means of a long-cycle test, an electrochemical impedance spectroscopy test, a scanning electron microscope test and a selected area electron diffraction test; and performing macro-micro combined analysis on macroscopic and microscopic quantitative verification results. By using macro-micro combined evaluation, the impact of charging strategies on microstructures of electrode materials and the cycle performance of batteries is studied, thus quantitatively revealing the optimization effects of different optimization strategies. By combining the microscopic characteristics of electrode materials and the macroscopic performance of charging strategies, the impact of different charging strategies on the performance of batteries is studied so as to optimize the charging strategies, thus improving the performance and prolonging the cycle life of batteries.

The invention relates to the technical field of electrical design and intelligent optimization calculation, and discloses a capacitor structure design optimization method and system and a storage medium. The method comprises the following steps: carrying out modeling processing on capacitor structure parameters, and dividing a parameter space to obtain an initial model set; obtaining a multi-physical field simulation model according to the geometric model and the material attributes; obtaining performance indexes such as capacitance value, heat distribution and stress based on the simulation model; setting a target function and constraint conditions, and operating an optimization algorithm to obtain an optimal solution set; performing feedback control processing on an optimization result, and analyzing a convergence path to obtain a structure parameter; according to the invention, the execution efficiency of capacitor structure design optimization is improved, and the consistency and stability of the optimization process and the performance prediction result are improved.

The invention discloses a waterproof coating formula optimization method and system based on cloud computing, and relates to the technical field of coating formula optimization. A waterproof coating formula optimization system based on cloud computing comprises a data acquisition and management module, a data model construction module, a formula performance optimization module, a formula cost performance optimization module, a new formula verification module and a data recording and loop optimization module. The performance data model and the environment data model are constructed, and the performance range index of the waterproof coating is combined with the use environment adaptability; a forward optimization channel and a reverse optimization channel are designed, and optimization is carried out for improvement of existing formula performance and adaptation of target environment requirements; by combining the performance evaluation model and the optimization algorithm, efficient searching and updating of the formula parameters are achieved, and the formula optimization efficiency is greatly improved.

The invention discloses a fluid-solid coupling numerical simulation method and device based on an embedded discrete fracture model. A structured grid is adopted to divide a reservoir matrix, and a discrete fracture model is embedded to construct an oil and gas reservoir geological grid. Establishing a transmission function, and formulating a porous medium fluid seepage control equation according to the transmission function. The equation is solved at each time node, a pore pressure field is obtained, and an equivalent pore pressure field is obtained through weighted averaging and serves as a boundary condition of the solid mechanical control equation. Expanding an oil and gas reservoir geological grid model, combining a crack-rock constitutive model, an effective stress principle and a stress balance equation, constructing a solid mechanical control equation, and solving and simulating rock deformation by adopting a finite element method and a Newton-Rafson method. Based on the constitutive relation of the composite material, matrix and crack seepage attributes are updated, the interaction between a seepage field and a stress field is considered in an iterative coupling mode, and multi-field coupling simulation is achieved. The problem that an existing method cannot be applied to a real oil and gas reservoir is solved.

The invention provides a self-adaptive high-temperature damage model construction method and system. Belongs to the technical field of material science and engineering calculation. The method comprises the following steps: acquiring experimental data of a material in a high-temperature environment, preprocessing the collected data through a machine learning algorithm, and extracting key damage features; constructing a basic damage prediction model based on a deep learning framework, training the model by using the preprocessed data, and enabling the basic damage prediction model to adaptively adjust internal parameters according to prediction errors through a reinforcement learning mechanism; and carrying out global search on thermodynamic parameters in the model by utilizing a genetic algorithm, searching an optimal parameter combination, and dynamically adjusting model parameters in combination with real-time temperature monitoring data. Through integration of deep learning, reinforcement learning, a genetic algorithm and a molecular dynamics simulation technology, the damage evolution process of the material in a high-temperature environment can be predicted more accurately and comprehensively.

The invention relates to the technical field of computational material science, and discloses a phase field simulation method, device and equipment for simulating hydrogen-induced fatigue crack propagation of a material and a storage medium. The method comprises the following steps: acquiring hydrogen transmission parameters and mechanical property parameters of a target material, and establishing a material deformation equation, a hydrogen diffusion equation and a phase field evolution equation corresponding to the target material based on the hydrogen transmission parameters and the mechanical property parameters; constructing a three-field coupling relation by using a material deformation equation, a hydrogen diffusion equation and a phase field evolution equation to obtain a three-field coupling phase field simulation model; and building boundary conditions of a hydrogen diffusion equation based on a surface hydrogen absorption flux equation corresponding to the target material, and solving the three-field coupling phase field simulation model to obtain crack propagation parameters of the target material in the gaseous hydrogen environment. Therefore, through the constructed three-field coupling phase field simulation model, the dynamic crack propagation behavior of the material in the gaseous hydrogen environment is accurately described, and the prediction precision of the crack propagation process of the material in the complex environment is remarkably improved.

The invention relates to the technical field of process parameter analysis, and discloses a process parameter analysis method and system for a foaming material forming process and a storage medium, and the method comprises the following steps: carrying out parameter collection and data statistics on an MPPO material under a 160 DEG C standard process condition to obtain process parameter reference data; carrying out a load pressure parameter orthogonal test on the material to obtain a load pressure process parameter database; forming temperature gradient analysis is carried out, sampling testing is carried out at the interval of 5 DEG C from 160 DEG C to 190 DEG C, and a temperature control curve is obtained; performing coupling analysis and pressure dynamic compensation to obtain a forming process parameter combination; carrying out wet weight, dry weight and size detection on a product produced under the forming process parameter combination, and carrying out multi-dimensional parameter calculation to obtain a quality control index; and performing association verification and parameter correction on the quality control index and the forming process parameter combination, and outputting target process specification data. The method improves the control precision of the process parameters.

The invention discloses an inertial microsystem thermal coupling analysis method based on a physical information neural network, and the method comprises the steps: S1, setting material parameters and boundary conditions of an inertial microsystem, and building a thermal coupling analysis model; s2, performing electrothermal coupling analysis to obtain temperature distribution of the inertial microsystem; s3, thermal stress distribution of the microsystem is obtained through thermal-mechanical coupling simulation analysis; s4, predicting a temperature field of the microsystem through the physical information neural network; s5, taking the temperature field as a boundary condition of mechanical simulation of the microsystem to obtain mechanical properties such as stress and strain of the microsystem; and S6, performing mechanical-electric coupling simulation analysis, and analyzing the influence of structural deformation on each parameter in electrical performance. According to the method, the neural network solving precision is improved through the improved adaptive weight strategy, the complete polynomial basis function is combined with the neural network, the state dimension is reduced by introducing the expansion basis function, the calculation cost and time are reduced, accurate prediction of the micro-system multi-moment temperature field is achieved, and the micro-system performance under electric heating power multi-physics field coupling is calculated.

The invention discloses a concrete strength remote monitoring method and system suitable for a complex environment, and belongs to the technical field of civil engineering structure health monitoring. The remote monitoring method comprises the following steps: step 1, acquiring performance data of a concrete structure and related environmental factor data, and transmitting multi-source heterogeneous data to a data processing center in real time through a preset wireless communication protocol; step 2, constructing a five-dimensional tensor data structure, and realizing accurate mathematical expression of a complex coupling relationship between environmental factors and material characteristics through tensor decomposition; step 3, capturing nonlinear time-varying characteristics of concrete strength evolution; 4, quantifying the age effect through an intensity development rate index; step 5, based on the intensity development rate change trend, adaptively adjusting the data sampling frequency and monitoring the environmental condition fluctuation; and step 6, evaluating the safety state of the concrete structure in real time, and ensuring safe and reliable operation of the concrete structure in a complex environment.

The invention provides a coast defense monitoring system based on unmanned aerial vehicle group collaboration and an application method thereof, and relates to the technical field of coast defense monitoring, and the coast defense monitoring system integrates air unmanned aerial vehicles, water surface unmanned ships and underwater unmanned ships, constructs a four-dimensional collaborative monitoring network, and combines a frequency domain resonance superposition technology and real-time analysis of marine environment variables. A dynamic three-dimensional environment model is generated, threat prediction of a time dimension is realized, multi-source sensor data can be fused in real time, a complete space contour of a target is formed, threat distribution in several hours in the future is predicted through environment change, for example, unmanned aerial vehicle deployment is adjusted in advance to cope with illegal border crossing behaviors in storms, and the risk of threatening is reduced. Quantum entanglement communication and acousto-optic hybrid communication ensure the stability and security of data transmission, and support millisecond response. According to the effect, the real-time performance and foresight of coast defense monitoring are remarkably improved, so that the system not only can quickly cope with current threats, but also can actively prevent potential risks.

The invention relates to the technical field of laser material processing, and discloses a laser annealing temperature control method and device.The method comprises the steps that the specific heat capacity, the heat conductivity coefficient, the heat diffusivity, the heat radiation rate, the heat reflectivity and the heat loss coefficient are obtained, and a material thermal characteristic model is established; performing grid division according to the material thermal characteristic model to obtain a laser energy density parameter; extracting a thermal strain coefficient according to the laser energy density parameter, and establishing a temperature-thermal strain mapping relation prediction model; dynamically adjusting a laser power proportion and a pulse width time sequence according to the temperature-thermal strain mapping relation prediction model to obtain temperature field distribution data; according to the temperature field distribution data, carrying out shortest path analysis to obtain a temperature regulation and control decision; and according to the temperature regulation and control decision, inputting a preset control regulation model, and combining reinforcement learning iteration optimization parameters to obtain a temperature control scheme. According to the method, large-range temperature crossing and fine adjustment in an extremely short time can be realized.

The present invention relates to a sodium-ion battery material, and specifically relates to a sodium ferric phosphate pyrophosphate composite material, and a preparation process, evaluation method and process optimization method therefor and the use thereof. The sodium ferric phosphate pyrophosphate composite material has a spinel-type NaFePO4 phase; and in an XRD diffraction pattern obtained by subjecting the sodium ferric phosphate pyrophosphate composite material to XRD diffraction using CuKα radiation, there are a characteristic diffraction peak T of sodium ferric phosphate pyrophosphate and a characteristic diffraction peak O of spinel-type NaFePO4 at 2θ of 33.6±0.2° and 32.8±0.2°, respectively, the peak intensities thereof are respectively IT and IO, and the diffraction peak intensity ratio Ir is equal to IT / IO, wherein 6.19≤Ir≤13.

The invention discloses a process parameter tracing and quality collaborative management system for ceramic production, and relates to the technical field of ceramic production, the management system comprises a step of obtaining a plurality of process data and quality data in a ceramic production process, and each group of process data comprises a raw material ratio, a forming pressure, a firing temperature and a firing time. According to the process parameter tracing and quality collaborative management system for ceramic production, a specific process parameter deviation link can be quickly positioned by reversely tracing to a raw material source from a finished product quality problem and combining batch association identifiers and data records of all links; the quality problem solving efficiency is improved, and batch loss caused by traceability lag is avoided; besides, a process quality association relationship constructed by the module is established, a quality detection result is closely associated with process parameters, and multi-dimensional analysis is performed, so that a quality problem can be fed back to process parameter adjustment in time, and targeted measures can be taken according to an analysis result.

The invention relates to the technical field of structure health monitoring, and discloses an underground pipe gallery structure corrosion monitoring and evaluation method based on optical fiber sensing, which comprises the following steps: constructing a composite optical fiber sensing structure which comprises a Fe-C alloy coated optical fiber and a long-period grating, synchronously monitoring the chemical component change and the structural mechanical state of the corrosion product through the photoluminescence spectrum characteristic peak displacement and the strain distribution cloud picture; measuring deviation caused by coating layer loss is dynamically corrected in combination with the change rate of the environment refractive index; a reference grating is embedded to eliminate temperature drift interference, and iron ion in-situ deposition is triggered to realize sensing interface self-repairing. According to the invention, through a multi-physical-quantity cooperative sensing and self-adaptive compensation mechanism, the problems of signal interruption and the like caused by difficult decoupling of chemical-mechanical parameters and film coating failure in traditional monitoring are avoided, the long-term stable monitoring capability of a corrosion process in a complex environment is remarkably improved, and high-precision data support is provided for safety assessment of an underground pipe gallery structure.