5984results about "Manufacturing data aquisition/processing" patented technology

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 belongs to the technical field of 3D printing, and particularly relates to a 3D printing control method based on multi-material gradient transition, which comprises the steps of model processing, multi-material distribution, path planning, dynamic parameter control, online monitoring and feedback optimization. According to the method, through dynamic mixing of a nonlinear gradient function and multiple materials, limitation of traditional discrete material switching is broken through, continuous smooth transition of material attributes is achieved, printing parameters are dynamically adjusted, and the forming consistency of gradient materials under complex working conditions is remarkably improved; local strengthening is carried out according to the thermodynamic property difference of a multi-material interface, interface embrittlement caused by a traditional single process is avoided, the curing degree is detected in real time through a spectrum sensor, the problem that the curing degree of a traditional light curing process is uncontrollable is solved by combining partitioned UV light intensity adjustment and control, and precise gradient forming of a soft and hard alternate structure is achieved.

The invention discloses an industrial particle 3D printer cavity temperature heat recovery system and a cavity temperature control method, and relates to the technical field of 3D printing, and the method comprises the following steps: establishing a three-dimensional geometric analysis model of a printing component, based on the structural morphology, thickness distribution and spatial topology characteristics of the component, extracting a spatial temperature control demand corresponding to each region, and calculating a three-dimensional geometric analysis model of the printing component; and determining an adjusting target of the hot air circulation system. According to the method, a space temperature control demand model is established based on geometrical characteristics of a component, oriented distribution of heat energy is realized by dynamically adjusting a hot air circulation system, a cooling characteristic curve is constructed in combination with real-time temperature and stress data, and a warping resonance sensitive area is identified and regulated. And a self-adaptive temperature control system is formed through fixed-point thermal compensation and parameter closed-loop correction. Compared with a traditional strategy, the method has the advantages that the temperature control precision and the response efficiency are remarkably improved, thermal stress resonance and structural deformation in the cooling process are effectively restrained, and the method is suitable for stable forming of high-precision complex components and has wide application prospects.

The invention provides a gradient crosslinking process of photosensitive resin for photocuring 3D printing, and relates to the technical field of photocuring 3D printing materials. The gradient crosslinking process of the photosensitive resin for photocuring 3D printing comprises the following steps: S1, based on double-initiator system design, adopting molecular dynamics simulation screening, respectively adding a 405nm sensitive type TPO-L initiator and a 365nm sensitive type I TX initiator into an epoxy acrylate prepolymer, and verifying initiation energy barrier matching through quantum chemistry calculation, and generating a pre-initiation gradient resin formula. Through molecular dynamics simulation, initiator combinations are screened, energy barrier matching is verified, the ratio of dual-wavelength sensitive initiators is optimized, and accurate triggering of surface layer and deep layer cross-linking reaction is ensured. And a three-roller grinding and dispersing process is combined with ultrasonic oscillation to eliminate nanoparticle aggregation, so that the mechanical uniformity and light scattering effect of a resin system are improved.

The invention relates to the technical field of 3D printing error compensation and precision control, and particularly discloses a printing comprehensive precision measurement method for 3D printing equipment, which comprises the following steps: designing a standard test piece containing various typical geometrical characteristics, and combining non-contact three-dimensional optical scanning and contact probe measurement means to measure the printing comprehensive precision of the 3D printing equipment. Spatial distribution data of printing errors are obtained in a controlled environment, a three-dimensional error field model with direction and amplitude information is constructed through point cloud registration and feature comparison, and based on error distribution features, the local resolution is dynamically adjusted by adopting a multi-level self-adaptive grid division strategy, so that a three-dimensional error field model with direction and amplitude information is constructed. A local error compensation model is established by combining tightly-supported radial basis function interpolation and a linear fitting method, a boundary consistency matching algorithm is introduced to realize multi-level model fusion, a unified global error compensation field is formed, an error offset vector field is embedded into a slicing process, point-by-point dynamic correction of a nozzle path is realized by using a trilinear interpolation algorithm, and the nozzle path is dynamically corrected. And the forming precision and the surface quality of the key structure are improved.

The invention relates to a 3D printing method and system for a composite prestressed reinforcing rib, and solves the main problems of weak interlayer adhesive force, large thermal stress and lack of an effective reinforcing rib adding method in the mechanical property of an existing 3D printing technology. Printing parameters and prestress release are cooperatively controlled on the basis of characteristic parameters of printing materials through a stress linkage algorithm, vertical ribs are tensioned for the second time through an interlayer compensation algorithm every time a preset number of layers are printed, and the printing parameters are dynamically adjusted through a path optimization algorithm in a high-stress area; when printing is completed and the material is not completely cured, stress distribution of all the reinforcing ribs is scanned through a stress balance algorithm and adjusted to a preset deviation range, and it is ensured that stress distribution of the whole structure is uniform; and after the printing material and the reinforcing ribs are compounded and cured, corresponding curing is performed according to the characteristics of the printing material. The 3D printing structure has the following effects that the mechanical property and reliability of the 3D printing structure are improved, and the interlayer strength and the overall stress distribution are optimized.

The fused deposition 3D printing fault monitoring method is applied to 3D printing equipment, the 3D printing equipment at least comprises a spray head, a base plate, a wire feeding module, a fault detection module and a main control system, and the fault detection module comprises a regulation and control assembly and a camera. The method comprises the steps of obtaining model printing information; setting a motion mode of the regulation and control assembly and a collection mode of the camera; acquiring an image of a monitored area; performing fault feature calculation on the monitored area image to obtain a fault feature value; judging whether a printing error occurs or not according to the fault characteristic value; and if a printing error occurs, an error alarm is given out. According to the method, the printing fault caused by the material shortage problem caused by nozzle blockage, material breakage and the like possibly existing in the printing process of the 3D printing equipment can be rapidly monitored in real time, and material waste and printing time waste are reduced.

The invention provides a real-time 3D printing defect detection method and system based on artificial intelligence, and aims to solve the problems of dependence on single visual information, lack of dynamic tracking and insufficient model generalization ability in the prior art. According to the method, the printing process is comprehensively perceived through multi-modal data fusion, and defects are analyzed and classified in real time by using a deep learning model. The model output comprises defect category, position, abnormal score and severity evaluation, and the defect development trend can be dynamically tracked and early warning can be carried out in advance. Through comparison with a printed digital model, the system can accurately identify geometric deviation, and early intervention is realized in combination with trend analysis. When serious defects are detected, the system automatically triggers response measures, such as printing pause, parameter adjustment or correction scheme provision, to form a complete quality control closed loop. According to the method, the detection precision and robustness are remarkably improved, and incremental learning is supported to adapt to different printing environments.

A robotic fleet resource provisioning system includes a computer-readable storage system storing a fleet resources data store and resource provisioning rules. The fleet resources data store maintains a fleet resource inventory indicating fleet resources, each with features, configuration requirements, and a status. The resource provisioning rules are accessible to an intelligence layer to ensure that provisioned resources comply with the resource provisioning rules. The system receives a request for a robotic fleet to perform a job and determine a job definition data structure. The definition data structure defines a set of tasks that are to be performed in performance of the job. The system determines a robotic fleet configuration data structure corresponding to the job based on the set of tasks and the fleet resource inventory. The system determines a respective provisioning configuration for each respective fleet resource. The system deploys the robotic fleet to perform the job.

A sample batch is accessed and a conditioning is computed based on given constraints. A dense relaxation is computed based on the computed conditioning and the sample batch to generate kernels for conditioning a diffusion optimization model. A sample is selected from the sample batch based on a randomly sampled timestep and random noise is sampled from a Gaussian distribution. A noisy representation of the selected sample is sampled based on the randomly sampled timestep and the sampled random noise. The diffusion optimization model is run in a forward direction using the noisy representation of the selected sample to generate a prediction. An error loss is computed to minimize an error between the prediction and a target; the target is the sampled random noise. A diffusion optimization loss is computed based on the error loss and the diffusion optimization model is updated based on the diffusion optimization loss using backpropagation.

The invention provides an intelligent image processing method and device for UV curing 3D ink-jet printing and a storage medium, and the method comprises the steps: carrying out the contour feature extraction of a slice image, and intelligently recognizing a supporting region based on the interlayer contour change and geometric features; printing data corresponding to the color ink layer, the white ink layer, the gloss oil layer, the shaping layer and the supporting layer are generated according to the contour features, the shrinkage judgment result and the overhanging area recognition result, the printing data serve as spot color channels and are combined with CMYK channels of the color ink layer, halftone processing is carried out on the data of the channels, and the color ink layer is obtained. And finally, generating and outputting a TIF format image file comprising at least eight channels, wherein the TIF format image file is used for driving the UV curing 3D ink-jet printer, the equipment and the storage medium storage and is used for realizing the intelligent image processing method during execution. According to the method, the printing precision and the material utilization rate are improved, and the problems of support generation, color management and quality control in multi-material 3D printing are effectively solved.

The invention belongs to the technical field of electrohydrodynamic printing control, and discloses an electrohydrodynamic printing control method and device, electronic equipment and a storage medium. The method comprises the steps that the viscosity state parameters of fluid in the printing process of an electrohydrodynamic printing system are obtained in real time, inputting the viscosity state parameter into a preset fluid state calculation formula, calculating to obtain a fluid state evaluation value of the fluid, adjusting the electric field parameter according to the fluid state evaluation value and a preset adjustment criterion to adapt to the state change of the fluid, obtaining the adjusted electric field parameter, and determining the viscosity of the fluid based on the viscosity change of the fluid and the Taylor cone form. The adjusted electric field parameters are optimized to control the electro-hydrodynamic printing system to maintain the stability of the Taylor cone; the electric field parameters are adjusted according to the fluid state evaluation value, the viscosity change and the Taylor cone form of the fluid in the printing process, so that the stability of the Taylor cone is maintained by the electro-hydrodynamic printing system, and the printing efficiency of electro-hydrodynamic printing is improved.

The application discloses a kind of based on 3D printing's three-dimensional model slice path processing method and device. Including: control 3D printer to starting point as starting point;Based on the region surrounded between starting point and reference point, determine the printing point to be printed that 3D printer needs to execute printing action during executing 3D printing job;Starting point and the printing point to be printed are sequentially connected by smooth straight line, generate printing path;Based on printing path control 3D printer executes 3D printing job and obtains target object, to effectively reduce the number of line segments of slice contour line of three-dimensional model forming target object, avoid 3D printer in printing job, in the pause frequency of line segment head, tail, simultaneously avoid the problem that 3D printing model surface protrusion is caused by long time scanning due to pause.In reducing pause frequency, printing is more smooth, improve printing efficiency, simultaneously also reduce algorithm complexity, help the simplification of algorithm.And effectively simplify the algorithm of 3D printer.

The invention discloses a flexible structure 3D printing support control method combined with a resistor, and relates to the technical field of 3D printing, and the flexible structure 3D printing support control method comprises the following steps: generating a continuous density field and a dynamic damping parameter field of a support structure based on multi-target topological optimization of dynamic stiffness constraint and material distribution; quantifying the contact probability of the support structure and the target model through a membership function, and constructing a nonlinear gradient field of a contact interface; the stress coupling relation between the supporting structure and the model interface is defined based on the mixed constitutive tensor, the interface stress transfer coefficient is adaptively updated based on the material shrinkage rate difference, multi-physics field collaborative optimization is achieved in combination with regularization constraint, the key part of the supporting structure is recognized, and the resistor is integrated to the key part of the supporting structure. Through multi-target topological optimization, membership construction and adaptive stress transfer coefficient updating, the material consumption is reduced, the printing efficiency is improved, stress concentration at an interface is inhibited, the forming defect risk is reduced, and the structural reliability is improved.

The invention provides a composite material additive manufacturing method and device based on size error compensation, and the method comprises the steps: selecting the number, type and combination mode of materials needed by each layered structure strip according to the material performance of a prepared component and the local thermodynamic performance and mechanical performance of the component; all strips of the single layered structure are laid at the same time or in each round, so that the single layered structure is formed; each strip is made of a single type of material or is formed by laminating and compounding a plurality of single type of materials, and the self-adaptive laying method is executed according to data of various sensors in the laying process and data of a detection mechanism after laying; and the steps are repeated, and the strips are laid layer by layer according to the layer sequence of the layered structure till the component is formed. According to the method, through effective combination of additive manufacturing modes of multiple fiber composite materials, multiple materials are combined and then laid, and it is ensured that the distribution of fibers and the infiltration effect of a matrix reach the ideal state.

The invention relates to the technical field of additive manufacturing, and discloses an additive manufacturing lens surface optimization method based on real-time feedback control. The method comprises the following steps: firstly, acquiring eye biometric data and personalized visual demand parameters of a wearer, establishing a personalized optical model of eyeballs according to the eye biometric data and the personalized visual demand parameters, and deducing a retina defocusing amount distribution map; and determining spatial arrangement and geometric morphology parameters of the distributed out-of-focus microstructure layer on the lens according to the atlas. In the additive manufacturing process, the optical material is deposited and solidified layer by layer, and the three-dimensional shape information of the surface of the optical material is obtained in real time after deposition of each layer. And comparing and analyzing the morphology information with a theoretical model to generate a morphology deviation result. And then, a correction instruction of the printing parameters is calculated by using the correction model, and the printing parameters of the additive manufacturing equipment are adjusted in real time, so that real-time compensation and optimization of the morphology deviation are realized, and finally, a high-precision lens product meeting personalized optical design requirements is manufactured.

The invention provides a universal 3D printing global continuous path planning method and system, and relates to the technical field of 3D printing data processing or control, the method comprises the following steps: inputting an STL model and slicing to obtain each layer of section contour; calculating offset contours of the inner and outer contours by adopting vector operation; interference is eliminated when self-intersection or intersection occurs in the offset contour; constructing a path point set; establishing a mathematical model for path planning; solving the mathematical model by adopting a pointer network based on reinforcement learning to obtain a continuous filling path traversing all the path point sets; curvature constraint smoothing is carried out on the path through an n-order Bezier curve; and generating a 3D printing global continuous path by adopting a greedy search algorithm. The method is suitable for a complex porous structure, path intersection and interruption are reduced, the path length and the bending angle are minimized, and therefore the printing precision and the mechanical property are improved.

The invention discloses a path planning method and system for curved surface integrated conformal jet printing, and belongs to the field of additive manufacturing. According to the method, the geometric data of the non-deployable curved surface are obtained and converted into the discretized grid model, then the plane pattern is designed and mapped to the three-dimensional curved surface, the mapped pattern is locally optimized, and the complete printing area model is generated. Path planning is divided into initial jet printing path generation and five-axis linkage motion path planning, conformal characteristics are achieved through an adaptive interpolation algorithm and NURBS curve optimization, five-axis motion parameters are calculated based on an inverse kinematics algorithm, and the positioning relation between a jet printing head and a curved surface is kept through the RTCP technology. The method solves the problems of precision and path planning efficiency in complex curved surface manufacturing, and is suitable for manufacturing heterogeneous complex curved surface functional structures in the fields of aerospace, medical instruments and the like.

The invention discloses a 3D printed part morphology control method and system based on supporting structure morphology compensation, and relates to the technical field of additive manufacturing. Comprising the steps that a to-be-printed target model is processed, and a to-be-printed model considering a supporting structure form and slurry infiltration characteristics is generated; the printing direction of the to-be-printed model and the interface positions of different slurries during multi-material printing are determined, and the interface positions are perpendicular to the printing direction; according to the determined printing direction and the determined interface position, parts corresponding to different kinds of slurry are printed in sequence, and when the slurry is replaced, the datum plane of the printed part is cleaned, and residual materials are removed; and curing the printed model, removing the supporting structure, cleaning and integrally curing to obtain a final finished product. Liquid shaping is achieved only by means of the modified supporting structure, the printing time cost and the mechanical cost are saved, and accurate regulation and control of the liquid resin form are achieved on the premise that no external device is additionally arranged through integrally-formed supporting morphological optimization.

An information technology system for a distributed manufacturing network includes an additive manufacturing platform configured to manage workflows for a set of distributed manufacturing network entities associated with the distributed manufacturing network. The information technology system includes a set of digital twins generated by the additive manufacturing platform. The information technology system includes an artificial intelligence system configured to be executed by a data processing system in communication with the additive manufacturing platform. The artificial intelligence system is trained to generate process parameters for the workflows managed by the additive manufacturing platform using data collected from the set of distributed manufacturing network entities. The information technology system includes a control system configured to adjust the process parameters during an additive manufacturing process performed by at least one of the set of distributed manufacturing network entities.

The invention belongs to the technical field of 3D printing, relates to 3D printing equipment and a printing control method thereof, and solves the problems that printing parameters cannot be adjusted in real time and the printing effect cannot be ensured. The 3D printing equipment comprises a mechanical arm controlled by an industrial robot, a printing device is arranged on the mechanical arm, the industrial robot and the printing device are connected with an upper computer, the printing device comprises a material storing and feeding mechanism and a screw extrusion mechanism which are arranged on a fixing support, and the discharging end of the material storing and feeding mechanism is connected with the feeding end of the screw extrusion mechanism; the upper computer comprises a three-dimensional model layered slicing module, a path optimization module and a dynamic cooperative control module. A laser distance measuring sensor is arranged on the outer side of a nozzle of the printing device and used for synchronously adjusting the motion trail of the mechanical arm. According to the equipment, the melt extrusion type printing mechanism is adopted, the product performance is improved, the application range of 3D printing is expanded, cooperation of the printing equipment and the mechanical arm is enhanced, specific parameters can be adjusted according to actual operation, and the printing quality is guaranteed.

The invention discloses a photocuring 3D printing mechanism method for multi-material cooperative spraying, and relates to the field of cooperative control, and the photocuring 3D printing mechanism method comprises the following steps: layering a model, generating a material distribution map, and labeling a functional area and a transition area; a multi-nozzle array is used for accurately positioning and spraying according to the atlas; a gradual change path is planned in the transition area, and gradient change of material components is achieved through cooperative spraying; a partition curing strategy is adopted, ultraviolet light full curing and visible light semi-curing are adopted, and leveling is promoted in combination with micro-vibration; spraying a penetrating agent between layers to form a mechanical interlocking enhanced interface; based on visual online defect detection, printing parameters are fed back and adjusted in real time through a CNN model. The device and the method have the advantages that accurate mixing of multiple materials is realized through cooperative spraying of the multiple piezoelectric sprayers and gradual change path planning, and real-time defect detection and correction can be realized by matching with differentiated photocuring, high-frequency micro-vibration leveling and interlayer penetrating agent interlocking, so that high-precision multi-material functional component forming is efficiently completed.

The invention belongs to the technical field of metamaterials, and particularly relates to a diamond substrate lattice metamaterial and an additive manufacturing method thereof. The structure is designed based on the geometric configuration of a diamond lattice, and has excellent high specific absorption mechanical properties. The design process comprises the following steps: firstly, carrying out spatial registration positioning on the basis of a unit cell structure with a preset wall thickness t and a unit cell width a; then, through a multi-unit-cell splicing design, the unit cells are spliced repeatedly for 12 times to form three-dimensional periodic stacking; then, the stacked structure is cut along the six datum planes; then cross plates are inserted into the six cutting faces respectively to enhance the integrity and strength of the structure; and finally, a complete DM plate lattice metamaterial structure is formed. The structure is provided with a plate-plate cross supporting framework, a complex three-dimensional inner cavity and highly symmetrical space distribution, the energy absorption capacity under unit mass is greatly improved, the mechanical properties are basically consistent in multiple directions, and the structure is suitable for the engineering fields of impact protection, energy absorption buffering and the like.

The invention discloses a three-dimensional printing method and three-dimensional printing equipment for manufacturing a target object. The three-dimensional printing method comprises the following steps: curing a first material to form a first part of a first layer in a plurality of target layers; curing the second material to form a second portion of the first layer of the plurality of target layers; curing the first material to form a first portion of a second layer of the plurality of target layers on the first portion of the first layer, the total thickness of the first portion of the first material being greater than the total thickness of the second portion of the second material; the steps are repeated to form a plurality of target layers of the target object. According to the scheme provided by the invention, various risks caused by the positioning accuracy of movement in the vertical direction are reduced.

An additive manufacturing real-time process parameter optimization method based on reinforcement learning driving comprises the steps that firstly, a multi-mode online monitoring hardware platform is constructed, and a visible light camera, a thermal imaging camera and an acoustic sensor are integrated to sense the working condition of the additive manufacturing process in real time in an omnibearing mode; secondly, extracting key features, including visible light images, thermal imaging and acoustic signal branches, of modal data on line through a lightweight CNN-Transform hybrid network, and generating a unified low-dimensional state vector through convolution feature extraction and fusion of a Transform encoder; then, a reinforcement learning model is established, a multi-target reward function is designed in combination with the extracted feature data, a process jitter penalty term, an overheating penalty term and the like are covered, and dynamic mapping of parameters and performance is achieved. And finally, an online learning and real-time decision-making system is deployed, the trained strategy network is embedded into manufacturing equipment, self-adaptive adjustment and closed-loop control of technological parameters are achieved, and the stability and product performance of the additive manufacturing process are effectively improved.

Systems and methods for generating a shape-based graded lattice structure that can be used in additive manufacturing. A slicer computer system generates the lattice structure by simulating the packing of a planar region with variable-sized packing shapes, where packing shape sizes correspond to intensity values of a non-uniform physical field expected to be experienced by the article. An intermediate lattice structure is then generated using a first set of polygonal cells, followed by a second set of polygonal cells that refine the final lattice structure. Tailored sectioning and field-based smoothing can modify polygon packing algorithms to adapt lattice generation. The resultant multi-stage graded lattice structures, which may include multiple lattice patches and transition zones, from shape-based packing, tailored sectioning, field-based smoothing, and slicer-based additive manufacturing processing improve connectivity and manufacturability over traditional lattice structures.

Described herein are systems and methods for detecting flaws during an additive manufacturing (AM) process. A method can include accessing, by a computer, simulation results of a computer-modelled part representing a physical part to be formed using the AM process. The simulation includes a thermal history model for the computer-modelled part. During run-time formation of the physical part, the method includes receiving, from sensor devices, real-time sensor data of temperature values for nodes within regions of the physical part as each region is formed. The method also includes determining, for each region as the region is formed in the physical part, a deviation between the real-time sensor data of temperature values for nodes within the region and temperature values of the thermal history model for the computer-modelled part, and identifying flaws in the physical part based on determining that the deviation satisfies criteria indicating a flaw.

The invention relates to the technical field of oral implantation guidance, and provides a digital false tooth mounting and positioning device based on three-dimensional scanning and a method thereof. The method comprises the following steps: scanning the oral cavity of a patient through an oral cavity scanner to generate a three-dimensional oral cavity digital model; generating a virtual implant of the patient and corresponding initial implantation parameters based on the three-dimensional oral cavity digital model through a SegFormer segmentation network; based on historical operation data, initial implantation parameters, bone density, patient attributes and occlusal force parameters, a long sequence prediction network is adopted to predict the position offset and bone resorption degree of the virtual implant after osseointegration through a segmented attention mechanism and a network memory cache mechanism, and optimal implantation parameters are obtained; and pushing a visual dynamic model containing the candidate positioning strategy to the user based on the optimal implantation parameter, driving an oral cavity model printer to obtain an implantation guide plate model and an oral cavity environment model, assisting the user in verifying the installation effect of the candidate positioning strategy, and improving the accuracy of false tooth installation and positioning.

A continuous fiber reinforced composite topological lattice structure design and 3D printing method comprises the steps that firstly, the elastic tensor of a unit cell structure of a 3D printing continuous fiber reinforced composite is represented, then a topological optimization model of the unit cell structure of the continuous fiber reinforced composite is constructed and divided into sub-regions, fiber tracks are generated for the sub-regions, and finally the topological lattice structure of the continuous fiber reinforced composite is obtained. The arrangement angle of the unit cell structures is calculated, the unit cell structures are periodically filled, and fiber tracks in the sub-regions are obtained; secondly, extracting fiber tracks of the sub-regions, obtaining minimum printing paths of the fiber tracks in each sub-region in combination with a periodic unit cell structure printing form, and connecting to obtain 3D printing sub-paths; then the 3D printing sub-paths are connected to obtain a 3D printing path of the continuous fiber reinforced composite topological lattice structure, and G-codes are generated in combination with the printing speed and the material feeding rate and used for sample piece manufacturing of a 3D printer; the manufacturing requirements of continuous fiber 3D printing are met.

Improved manufacturing techniques involve automatically adjusting manufacturing parameters in real time as an object is being manufactured to avoid or reduce manufacturing defects. A 3D printer begins printing an object based on a 3D design and using initial parameters. Cameras perform a volumetric capture of the object as it is being printed. A 3D model of the object being printed is generated in real time. The 3D model and the 3D design are compared to determine the differences and to detect defects. A machine learning model recommends new parameters to compensate for the defects. The 3D printer continues printing the object using the new parameters automatically. Users need not guess the manufacturing parameters or take multiple iterations of trial-and-error to manually adjust the parameters.