Deformation Prevention in 3D Printing

3D printing method that reduces internal structure coarsening and improves part performance by controlling laser power to prevent excessive temperature accumulation during printing. The method involves monitoring the temperature of the printed layer and comparing the temperature increase between layers. If the increase exceeds a set threshold, the laser power is reduced to slow down or prevent further temperature rise. This prevents internal structure coarsening and growth that can degrade part performance due to excessive temperature accumulation during long print times.

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Reducing errors in 3D printing by dynamically adjusting printer parameters based on the printed object geometry. The method involves identifying areas prone to printing defects, like gaps or under/over extrusion, and adjusting printing speed, extrusion rate, and linewidth in those areas to compensate. The adjustments are made using a model that predicts where defects are likely to occur based on the printing path. This allows customizing printer kinematics to prevent internal defects without sacrificing speed.

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3D printing method and system that dynamically adjusts the surface roughness of printed layers during multi-layer 3D printing to improve bonding between adjacent layers. After printing a layer, a surface modification component is used to add roughness if necessary based on parameters and detected features. This allows optimizing the surface texture for each layer to enhance layer adhesion.

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Heating control method for 3D printers that prevents material solidification and improves print quality by preheating the printing environment before starting a print. The method involves stirring the print material in the storage chamber using the print platform and then heating the printer environment using a heater. The heater temperature is adjusted based on the ambient temperature to maintain a desired environment. This ensures even heating of the material without local hotspots, preventing solidification and improving print quality.

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3D printing system with improved layer fusion and printing quality. It uses preheating and flattening systems on both sides of the print head to better fuse the printed layers. The system has two layer processing systems, one on each side of the print head, that move synchronously with the print head. Each system has a preheating apparatus to warm the previous layer before printing the next layer. This raises the previous layer temperature to better fuse with the new layer. After printing, a flattening roller on the opposite side compresses the layer to remove warping. The roller also cools the layer. By preheating and flattening on both sides, it improves layer fusion, reduces warping, and enhances print quality.

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3D printing method for manufacturing components with improved reliability in applications like continuous flow engines. It involves monitoring and adapting the 3D printing process in real-time based on temperature measurements. The method involves selectively melting metal powder layers to build the component. Temperature sensors continuously monitor the melted metal. If disadvantageous temperature spots are identified, the 3D printing process is automatically adjusted to prevent local overheating and remelting of lower layers. This prevents weaknesses and defects due to incomplete melting or remelting.

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High-precision concrete 3D printing control system for improving accuracy, speed, and stability of concrete 3D printing. The system monitors and controls parameters like temperature, pressure, and concrete density in real time during printing to optimize printing quality. It uses a dedicated control system rather than a computer to coordinate and execute printing without the need for multiple computers. The control system also has sensors to detect issues during printing and adjust parameters accordingly. A quality detection module checks printed parts for defects, and a data analysis module optimizes printing efficiency and material usage.

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Control method and device for 3D printing that improves precision by using a feedback loop to adjust printing parameters based on real-time monitoring of the printing process. The method involves obtaining preset printing parameters, printing with them, monitoring the result, adjusting parameters based on the monitoring data, and synchronously obtaining overall structure information. It provides a system control strategy pre-constructed from material properties rather than just machine precision. This allows accurate matching of materials and machines to improve printing fidelity.

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Optimizing 3D printing parameters to reduce thermal deformation in printed parts using a rapid prediction model. The method involves establishing a thermal deformation prediction model incorporating print parameters like laser power, scan speed, and direction. This model explores the impact of these factors on printing quality. By optimizing the joint parameters of laser power, scan speed, and direction using a multi-level multi-objective optimization algorithm, the method aims to find the print parameter configurations that maximize deformation reduction.

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3D printing system and method to improve quality and accuracy of 3D prints by optimizing layer adhesion and cooling. The system uses preheating and flattening mechanisms on either side of the extruder that engage based on extruder movement. When the extruder moves to one side, the preheater there heats the upper layer before printing, while the flattener on the other side remains off. Then when the extruder moves back, the preheater there is off and the flattener engages to flatten the printed layer. This prevents deformation of the upper layer while printing, improves fusion, and speeds cooling by contact.

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Intelligent concrete 3D printing method that aims to improve the success rate of 3D printing concrete by using a preset printing parameter model to determine the concrete mix ratio and printing parameters based on a target interlayer bonding strength. During printing, the parameters are actively adjusted to optimize extrusion. This leverages machine learning to provide more consistent and reliable printing results compared to relying solely on personal experience.

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3D printing metal objects without warping or cracking by using a tacky polymer substrate. The process involves spreading a layer of metal particles over a polymer substrate with low thermal conductivity and melting the unmasked metal with pulsed light to form each layer of the object. The polymer substrate reduces lateral heat transfer during melting, preventing warping and cracking.

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A 3D printing material for dental applications that allows the fast production of accurate dental models. The material contains specific monomers and initiators that reduce polymerization shrinkage and deformation after printing. The key components are monofunctional acrylate monomers with an electronegativity difference of less than 1.0 between adjacent atoms and initiators with absorption bands of 350-450nm. The material may also include low-shrinkage polyfunctional methacrylates, non-dendritic polymers, fillers, and colorants.

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Generating distortion-compensated geometry for a workpiece by predicting and compensating for sintering-induced distortion in 3D-printed parts. The method involves comparing a post-sintering mesh to a model mesh, determining node displacements, and moving corresponding nodes in the green body mesh opposite to the predicted sintering displacement directions. The green body mesh is iteratively adjusted until the predicted post-sintering geometry meets the desired tolerances.

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Efficiently leveling the heated bed of a 3D printer to improve print quality. The method involves scanning the bed with a probe to generate a 3D point cloud of the bed surface. The cloud is processed to determine the bed's flatness. Compensation values are calculated based on the flatness data to adjust the bed height and nozzle position at specific locations. This compensates for bed warping and ensures consistent print bed-nozzle distances for each layer.

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Heat-aware toolpath generation for 3D printing of physical objects. The toolpath is generated with criteria that optimize the path to minimize heat accumulation and deformation during the print. The toolpath design accounts for factors like the amount of heat generated in a zone, the proximity to previously printed zones, and the time between printing zones to strategically plan the order and placement of printed paths. This reduces heat-related deformations and improves the quality of printed objects.

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A 3D printing technique for reducing distortion in sintered metal parts made from powdered metal and binder. The method involves using a shaped support structure with grooves into which the part's projections fit. When the part is heated to sinter the metal powder, the support constrains the shrinkage direction to prevent distortion.

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A cranial remodeling device to correct a deformed head of a subject. The device has layers conforming to the head shape and is additively manufactured using a data file derived from 3D scans of the subject's head. The technique captures head shape data, modifies it to a desired shape, and projects contour lines onto the modified shape to design the device. The additive manufacturing process prints customized layers with removable supports at the point of service. This enables accurate, one-step, in-clinic production of cranial devices without shipping delays. The technique can further include printing alignment marks, sensors, transducers, and fasteners for enhanced functionality.

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Variable layer thickness metal 3D printing method that allows efficient and accurate printing of metal parts with varying layer thicknesses. The method involves dynamically adjusting the actual powder coating thickness for each layer based on the previous layer's printed thickness. This prevents waste by using the exact amount of powder needed for each layer instead of a fixed thickness. The adjustment is done by subtracting the previous layer's actual printed thickness from the set thickness to get the shrinkage, then adding it to the current layer's set thickness. This ensures accurate part geometry and quality when printing with variable layer thicknesses.

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A method to improve multi-degree-of-freedom 3D printing quality by using deep learning to predict and correct printing parameters in real-time. The method involves using a camera to continuously monitor the printing area and feed the images into a neural network to simultaneously predict the flow rate, lateral speed, longitudinal offset, and hot end temperature. If the predicted values deviate from normal ranges, it triggers parameter correction. The adjusted parameters are then sent back to the printer for closed-loop control. This allows self-regulation of printing parameters to prevent defects without manual intervention.

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