8391 results about "Additive layer manufacturing" patented technology

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.

A defect prediction method based on a multi-feature parallel multi-stage neural network (MF-PMSNN), includes: obtaining a trajectory dataset, and preprocessing data of a defect of a workpiece in additive manufacturing (AM); building an MF-PMSNN, and evaluating an output classification result based on evaluation indicators; and performing real-time defect prediction, and deploying a trained MF-PMSNN model to a production environment. The present disclosure combines and effectively matches thermal imaging-based in-situ monitoring data and X-ray computed tomography (XCT)-based in-situ monitoring data to ensure temporal and spatial consistency between the thermal imaging-based in-situ monitoring data and the XCT-based in-situ monitoring data. In this way, a molten pool status and a pore of the workpiece can be captured more comprehensively. The MF-PMSNN is proposed to obtain a molten pool status and the porosity distribution in the data and perform defect prediction.

A method for performing an automated additive manufacturing (AM) process comprises obtaining a current state that includes a set of first AM factors and a set of first characteristics, which are geometric or physical properties of a first AM output are generated using the first AM factors. The current state is provided to a reinforcement learning (RL) framework designed to adjust welding parameters in real-time based on the current state and reward feedback. The RL framework includes an actor network and a critic network, and safety limits are enforced through a custom loss function. The first AM factors are updated based on the critic network's evaluation, resulting in second AM factors used to perform the second AM process and generate a second AM output.

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 relates to the technical field of additive manufacturing, and particularly discloses a real-time defect detection and in-situ repair method and device in the additive manufacturing process, and the method comprises the steps that a multi-mode sensing system is integrated on additive manufacturing equipment, the multi-mode sensing system comprises a high-resolution CCD camera, a laser scanner and a thermal infrared imager, and data acquisition is synchronized; a hierarchical scanning strategy is adopted, two-dimensional surface images, three-dimensional point cloud data and thermal field distribution information are collected before and after each layer is machined, and the data synchronization precision is smaller than 0.1 ms; according to the invention, a multi-modal sensing system and an advanced algorithm are integrated, so that real-time detection and accurate repair of defects are successfully realized; a high-resolution CCD camera, a laser scanner, a thermal infrared imager and other sensors are adopted, a cross-scale RANSAC algorithm and a depth feature fusion network are combined, surface and internal defects are accurately detected, and laser remelting parameters are adjusted in real time for in-situ repair.

The invention discloses a thin-wall structure lightweight design method based on additive manufacturing, and belongs to the technical field of additive manufacturing, and the thin-wall structure lightweight design method comprises the following steps: S100, designing an initial structure based on function and performance mapping; s200, an additive manufacturing process knowledge base is constructed, and the adaptability of the additive manufacturing process is evaluated; s300, carrying out process and structure bidirectional coupling finite element analysis; s400, collaborative optimization of a process and a structure driven by a multi-objective optimization algorithm; s500, feature part manufacturing verification and design and process scheme iterative optimization are carried out; and S600, generating a design and process integrated scheme, wherein the integrated scheme comprises design parameters and manufacturing parameters. The technical problem that in the prior art, the structure optimization target and the manufacturing adaptive capacity are lack of collaboration can be solved.

An information technology system for a distributed manufacturing network includes an additive manufacturing management platform configured to manage process and production workflows for a set of distributed manufacturing network entities through design, modeling, printing, and supply chain stages. The information technology system includes an artificial intelligence system configured to learn on a training set of outcomes, parameters, and data collected from the set of distributed manufacturing network entities of the distributed manufacturing network to optimize digital production processes and workflows. The information technology system includes a distributed ledger system integrated with a digital thread configured to provide unified views of workflow and transaction information to entities in the distributed manufacturing network.

The invention relates to the technical field of structure optimization design, and particularly discloses a box-type beam structure lightweight design method based on topological optimization, which comprises the following steps: constructing a multi-physics field coupled three-dimensional parameterized model, and performing cross-scale analysis on integrated structure rigidity, thermal deformation, vibration mode and fatigue life to obtain a three-dimensional parameterized model; a variable density method and level set method mixed topological optimization strategy is adopted, and an initial material distribution scheme meeting the structural function integrity is generated by modeling the relationship between density and stress strain; starting a quantum genetic algorithm and deep reinforcement learning fused intelligent optimization engine; according to the method, through the multi-physics field coupled three-dimensional parameterized model, a variable density method and level set method mixed topological optimization strategy is combined, the relation between the structural lightweight target and the additive manufacturing process is effectively balanced, multi-physics field analysis of thermal response, vibration, fatigue and the like is introduced, and the structural lightweight target is obtained. And it is guaranteed that the box-type beam meets the mechanical property and other functional requirements in actual application.

A friction stir additive manufacturing system is provided. In one aspect, the system includes a spindle configured to rotate about a central axis, and a housing configured to receive at least a portion of the spindle, the housing configured to remain stationary relative to the spindle. The housing includes a wire inlet extending between an exterior surface of the housing and an interior surface of the housing, and a track extending from the wire inlet and partially around a circumference of an interior surface of the housing. The system also includes a feeding system configured to receive a wire from a roller and feed the wire through the wire inlet and into the track of the housing.

An additive manufactured (AM) metal coupon for a turbomachine component is provided. The component includes an airfoil body having a pressure side, a suction side, a trailing edge, and a coupon opening. The AM metal coupon has an AM metal member with at least one slanted coupon side wall at an angle of less than 90° with an exterior surface of the AM metal member. The AM metal member also has a first porous region having a first porosity and a second porous region having a second porosity different than the first porosity. A braze material couples the AM metal coupon in the coupon opening in the airfoil body and infiltrates into at least one of the first porous region based at least on a characteristic of the first porosity and the second porous region based at least on a characteristic of the second porosity.

The invention relates to a hybrid architecture mechanical property prediction method and system for an additive manufacturing lattice structure, and the method comprises the steps: converting a lattice structure node three-dimensional coordinate into a normalized distance from a lattice center, introducing a self-attention mechanism, and constructing a feature prediction model in combination with residual connection and layer normalization; constructing a generative adversarial network comprising a generator and a discriminator; a strategy network based on prediction error rewards is constructed, and a depth deterministic strategy gradient algorithm is adopted for optimization; jointly training the models, and constructing a performance prediction feedback model; finally, the node coordinates of the target structure serve as input, and mechanical property prediction output is achieved. According to the method, the generative adversarial neural network, reinforcement learning and an attention mechanism are mutually combined to generate a hybrid architecture, the problems of gradient disappearance and mode collapse of a traditional GAN can be effectively relieved, the model can accurately recognize implicit association between node positions and mechanical properties, and then the mechanical properties of target lattice structure parameters are rapidly predicted.

The present disclosure relates to a method of using a metal specimen block having a porous region for a component. A method of coupling a metal sample block (200) to a component (202) may include additive manufacturing a metal sample block (200) having a first porous region (300A) having a first porosity and a second porous region (300B) having a second porosity different from the first porosity. A metal specimen block (200) is positioned in a specimen block opening (204) in a body (206) of the component (202). The metallic sample block (200) may be infiltrated with a braze material (310) to couple the metallic sample block (200) in a sample block opening (204) in the body (206). The one or more brazing materials (310) are infiltrated into at least one of the first porous region (300A) and the second porous region (300B) based at least on a characteristic of the first porosity and at least on a characteristic of the second porosity. The cured braze material (310) includes different segments having different physical properties based on different porosities and / or different braze materials.

A component (202) is disclosed that includes a body (206) and an additive manufactured (AM) metal mass (200) that includes a first section (292) and a stress relief section (294). The stress relief section (294) includes a porous region (300) having a porosity in which an open spatial volume accounts for 2% to 50% of the total volume of the stress relief section (294). The stress relief section (294) has a smaller at least one of thermal conductivity, tensile strength, ductility, and fatigue strength than the first section (292). A braze material (310) couples the AM metal specimen block (200) to a specimen block opening (204) in the body (206). The stress relief section (294) creates a low stress zone in the metal specimen block (200), which can be located near or adjacent to a high stress zone. The stress relief section (294) reduces residual stress in the metal sample block (200), which reduces related challenges to couple the metal sample block (200) to the body (206) of the component (202), particularly in the case of the use of superalloy materials.

The invention relates to a metal sample block with a porous area for a component and the component with the metal sample block. A component (202) includes a body (206) and an additive manufactured (AM) metal sample block (200) that includes at least one first porous region (300A) having a first porosity and can include any number of second porous regions (300B) having a second porosity different from the first porosity. A braze material (310) couples the metallic sample block (200) in a sample block opening (204) in the body (206). The braze material (310) is infiltrated into at least one of the first porous region (300A) and any second porous region (300B) based at least on a characteristic of the first porosity and at least on a characteristic of the second porosity. Different porosities can be tailored to provide different physical characteristics for the first porous region (300A) and the second porous region (300B) having the braze material (310) therein.

The invention relates to a porous metal sample block with a pipeline for repairing a component, the component with the porous metal sample block and a related method. A metal sample block (200) for repairing a component (202) includes an additive manufactured (AM) metal component (330) having a conduit (296) inside the AM metal component (330) and a first porous region (300) surrounding the conduit (296). The duct (296) is hollow. A porous metal sample block (200) with conduits (296) allows the brazing material (310) to be directed based on characteristics of the first porous region (300). The second porous region (350) can be disposed within the conduit (296) to increase heat dissipation of the conduit (296).

A method of additive manufacturing a part having integrated passages is provided. In one aspect, the method includes forming a part having a near net shape by moving a friction stir tool to deposit a filler material in a predetermined formation. The tool can include a rotating spindle having a channel configured to hold the filler material. The method can include machining the near net shape part to form a plurality of grooves extending into a surface of the part, the plurality of grooves sized and shaped to each receive a tube. The method can include placing a tube into each of the plurality of grooves and moving the tool across the surface of the part and depositing additional material configured to secure the tubes within the plurality of grooves. The method can include machining the additional material deposited over the tubes to a predetermined shape.

The invention relates to the technical field of additive manufacturing, in particular to an air field system with uniform airflow distribution for an additive manufacturing large-breadth forming platform, which comprises an air inlet three-way pipe unit and a negative pressure air draft unit, the air inlet three-way pipe unit comprises a main air supply pipe, an upper air outlet pipe and a lower air outlet pipe; the upper air outlet pipe and the lower air outlet pipe are each internally provided with a flow dividing grating assembly, air outlets of the upper air outlet pipe and the lower air outlet pipe are divided into a plurality of sub air outlets with the same opening size through the flow dividing grating assemblies, and flow guide pipes are arranged at the air outlets of the upper air outlet pipe and the lower air outlet pipe. An airflow dispersion unit is arranged on the side, close to the sub air outlets, in the flow guide pipe, a linear air outlet hole is formed in the other side of the flow guide pipe, and a conical flow channel with a conical section is arranged between the airflow dispersion unit and the linear air outlet hole. By means of the multi-stage flow dividing and guiding design, the effect that airflow in all areas of the large-breadth forming platform is evenly distributed under the condition of low air volume is achieved.

The invention discloses an additive manufacturing parameter optimization method using machine learning. The method comprises the following steps: acquiring a first cladding layer surface topography constructed by powder layers with different powder characteristics under different powder laying parameters, and establishing a first database at least mapping a relationship among the powder laying parameters, the powder characteristics and the first cladding layer surface topography; and according to the first database, a first machine learning model is constructed and trained to generate a first application model used for powder laying parameter prediction, and then the first application model is used for predicting input powder characteristics to output optimal powder laying parameters used for additive manufacturing so as to optimize the additive manufacturing quality.

The invention provides an additive manufacturing method of a short fiber reinforced ceramic composite aircraft engine swirler, which comprises the following manufacturing steps: performing interface preparation on the fiber surface to obtain composite powder, and mixing the powder with silicon carbide powder and resin to obtain mixed powder; carrying out selective laser sintering printing molding on the mixed powder to obtain a ceramic green body, and carrying out degreasing treatment to obtain an intermediate; performing two-step impregnation and cracking on the intermediate through a ceramic precursor and resin to obtain a preform 2, performing gas / liquid phase reaction sintering treatment on the preform 2 to obtain Cf / SiC ceramic, depositing silicon carbide on Cf / SiC through a chemical vapor infiltration method, and performing abrasive flow grinding and polishing to obtain a finished product. The short fiber reinforced silicon carbide powder precise printing forming swirler is achieved, fiber, silicon carbide and resin powder are subjected to composite granulation, fiber agglomeration is weakened, the powder density is increased, the cohesive force between carbon fibers is increased, and therefore the biscuit density is increased; and structure accurate forming and surface quality control of the swirler with the complex structure are facilitated.

The invention relates to the technical field of rocket engine thrust chamber additive manufacturing, and discloses a metal powder recovery system for rocket engine thrust chamber additive manufacturing. Comprising a powder collection module, a screening classification module, a purification treatment module, a particle size adjustment module and an intelligent control module. The powder collecting module captures residual metal powder and collects data to generate a data set; the screening and grading module performs particle size separation by using a joint algorithm; the purification treatment module is used for constructing a multi-stage purification model to remove an oxide layer and decompose organic matters; the particle size adjustment module designs a composite regulation strategy to adjust particle size distribution; and the intelligent control module establishes an optimization model to generate a parameter adjustment instruction. In addition, the system is further provided with a powder characteristic analysis module, a circulation monitoring module and an exception handling module. The system can efficiently recover the metal powder, improve the quality and reutilization value of the metal powder, reduce the cost and guarantee the quality of additive manufacturing products, and is suitable for metal powder recovery treatment in the field of additive manufacturing of rocket engine thrust chambers.

Additive manufacturing devices with subtractive tools are provided. In one aspect, an additive manufacturing device includes a shoulder configured to rotate about a central axis, the shoulder including a central channel extending from a first end of the shoulder to a second end of the shoulder, the central channel configured to allow a filler material to pass through the shoulder from the first end towards the second end. The additive manufacturing device also includes a subtractive tool assembly including a subtractive tool and an insert sleeve. At least a portion of one or more cutting surfaces or edges of the subtractive tool extend outside the central channel of the shoulder when the subtractive tool is removably received in the insert sleeve and the insert sleeve is received in the central channel of the shoulder.

The invention provides a data correction method and system combined with lattice structure additive manufacturing process characteristics, and the method comprises the steps: firstly employing a Newton iteration method, taking the radius of a pillar as a variable, optimizing the relative density of an iteration target and generating a lattice structure geometric model under the condition that a process constraint condition is satisfied, and extracting actual geometric parameters after forming through CT scanning, the elastic modulus is corrected through the pillar diameter deviation and the defect volume fraction, a compression failure mechanism is combined, a density-related failure criterion is introduced, a nonlinear relation with the relative density is constructed through specific energy absorption data, material plasticity parameters are inversely optimized, and a defect coupling evaluation model is constructed according to the surface powder sticking rate and the pillar diameter deviation. And establishing a Gaussian mixture model based on a stress-strain curve to screen out abnormal data, and finally fusing the parameters to construct a data correction model. The dot matrix structure design precision can be improved, and then a high-quality data basis is provided for performance prediction-structure design two-way feedback.

A method for performing an automated additive manufacturing (AM) process comprising obtaining a set of first AM factors associated with performing a first AM process and obtaining a set of first characteristics of a first AM output, wherein the first AM output is generated by performing the first AM process using the set of first AM factors. A predictor is trained using the set of first AM factors and the set of first characteristics, which is a machine learning model, and a mathematical model is generated based on extracting internal parameters of the predictor. The mathematical model comprising represents an estimated relationship between first AM factors and first characteristics. A set of target characteristics is obtained and, using the mathematical model, a set of second AM factors is identified. The set of second AM factors are then used to perform a second AM process, generating a second AM output.

The invention belongs to the field of metal powder hot isostatic pressing and additive manufacturing near-net forming, and discloses a hot isostatic pressing forming method for preparing a substrate for additive manufacturing by mixing SS-PREP and EIGA Ti6Al4V ELI spherical powder, which comprises the following steps of: 1, mixing Ti6Al4V ELI powder prepared by SS-PREP and Ti6Al4V ELI powder prepared by EIGA according to a proportion under atmosphere protection; 2, in vacuum vibration powder loading equipment, the mixed powder is loaded into a steel sheath, and degassing seal welding is carried out; step 3, placing the seal-welded sheath in hot isostatic pressing equipment, carrying out heat preservation and pressure maintaining sintering forming, and then carrying out gradient cooling; and 4, after cooling, the steel sheath is removed through machining, and the large-size substrate for additive manufacturing is obtained. The prepared substrate has excellent comprehensive mechanical properties which are higher than the level of traditional forgings, and the application requirements of additive manufacturing are met.

A feeder system includes a donut-ring-shaped first wheel having a first groove recessed below its top surface, with a first hopper positioned above to receive material. A first conduit connects the hopper bottom to the groove, defining a feeding path. A mixer unit receives materials, connected to the first groove via a first suction head. A first motor rotates the first wheel, transferring material from hopper to groove to mixer unit. A donut-ring-shaped second wheel includes a second groove, second hopper, and second conduit. A second suction head connects this groove to the mixer unit, while a second motor enables wheel rotation for material transfer. The mixer unit combines both materials. The feeder system implements coaxial wheel arrangement with independent material flow control, enabling independent powder delivery for additive manufacturing applications.

The invention discloses an engine additive component service life prediction method based on modal polycondensation and expansion, and the method specifically comprises the steps: firstly, achieving the grid discretization of an initial defect structure based on a finite element method, and carrying out the local refinement of a defect region; performing modal polycondensation on the overall model to obtain a low-dimensional system model, and performing rapid calculation under the low-dimensional model to obtain modal response; establishing a conversion relation between a displacement vibration mode and a stress vibration mode, and obtaining full-field stress by utilizing modal response and based on modal extension; and solving a stress intensity factor-time history of the crack tip by adopting a stress extrapolation method, obtaining a delta K-R-n three-variable rain flow matrix by utilizing a rain flow counting method, and finally carrying out residual life evaluation through an iLAPS model. According to the method, system dynamics, fracture mechanics and modal order reduction technologies are fused, a new efficient and accurate fatigue life prediction path adapting to complex working conditions is provided for additive manufacturing components with defects, and the method has remarkable engineering application value and popularization potential.

The invention relates to an in-situ alloying method for an electric arc additive ultrahigh-strength aluminum alloy, in particular to an in-situ alloying method for a double-wire feeding electric arc additive ultrahigh-strength aluminum alloy. The invention aims to solve the problems that the microscopic uniformity of the material is poorer and the ideal mechanical property is difficult to realize due to the fact that the aluminum alloy is easy to form a thick organization structure in a deposition state, and the compactness and the quality stability of the material are seriously influenced due to the fact that solidification shrinkage cavities are easy to appear in the deposition process. By adjusting the wire feeding speed and angle of the two wires, it is ensured that the two wires are molten in the same molten pool, and in-situ alloying of ultrahigh-strength aluminum alloy additive manufacturing is achieved. By optimizing the components and the organization structure, the method effectively improves the component uniformity, the forming stability and the mechanical property of the aluminum alloy, and the high-performance aluminum alloy additive component with high strength, excellent toughness and durability is prepared. The invention belongs to the technical field of additive manufacturing.

Methods and apparatus for modulating a multi-mode shaped beam in powder bed additive manufacturing are provided. The method comprises the following steps: analyzing a geometric model of a component to obtain a plurality of feature regions which comprise at least one part of the component and correspond to different structure types; according to the structure type and in combination with material attributes and / or process targets, distributing corresponding light beam modes for the plurality of feature regions, wherein the light beam modes are modulated to have different light intensity distributions and / or phases; the light beam mode is applied to a light beam, so that the light beam is switched to the light beam mode corresponding to the light beam mode when the light beam scans a plurality of feature areas on a powder bed, matched light spot regulation and control are carried out on different feature areas in the additive manufacturing process, and therefore different forming requirements of various structure types are met in the same component at the same time.

The invention discloses a lightweight design method for a thin-wall structure containing a flow channel based on additive manufacturing, and belongs to the technical field of additive manufacturing, and the lightweight design method comprises the following steps: S100, analyzing functional requirements, and constructing a functional requirement model containing the flow channel; s200, a printing direction and additive manufacturing constraint conditions are set, topological optimization is executed on the function demand model, and a preliminary topological structure is obtained; s300, carrying out geometric reconstruction on a topological optimization result to obtain a three-dimensional model suitable for additive manufacturing; s400, carrying out parameter optimization design; s500, the section shape and size of the runner are optimized based on fluid performance requirements; and S600, additive manufacturing manufacturability inspection is conducted on the structure with the section shape and size optimized. And S700, the model obtained after additive manufacturing manufacturability optimization is analyzed and checked. The technical problems that in the prior art, the design requirement of a thin-wall structure containing a runner cannot be met, and the forming quality and the section shape of the inner surface of the runner are difficult to accurately control can be solved.

The invention discloses a dynamic monitoring and analyzing method for a thermal management component molten pool based on image recognition, and relates to the technical field of laser powder bed melting additive manufacturing. The method comprises the following steps: continuously capturing an original thermal management component molten pool image and temperature field distribution data during laser powder bed melting additive manufacturing; and optimizing the original thermal management component molten pool image to generate an optimized thermal management component molten pool image. Molten pool images and temperature field data are captured in real time through a high-frame-rate synchronous camera and an infrared sensor, defect types are recognized online in combination with a lightweight classification model, classification results are fed back to a control system to dynamically adjust laser power or scanning speed, interlayer defect accumulation can be inhibited in time, and the defect quality is improved. The defect diffusion problem caused by response lag of a traditional off-line analysis method is avoided, and the forming precision and the process stability of the heat management component are remarkably improved.