Apparatus for determining the quality of additive-molded products, and method for determining the quality of additive-molded products.

Abstract

To non-destructively analyze an internal defect position of an additive-manufactured article.SOLUTION: An additive-manufactured article quality determination device 1 comprises: a first assessment unit 14 which, from results of monitoring respective layers of an additive-manufactured article obtained by a shaping device equipped with a monitoring instrument, predicts generation of a defect in the respective layers; a second assessment unit 15 which predicts generation and / or disappearance of a defect in the respective layers from results of monitoring performed by the first assessment unit 14 with respect to a prescribed number of layers situated above the respective layers in the additive-manufactured article, and which reflects the prediction in generation prediction result information regarding a defect in the respective layers; and an output unit 11 which, from the generation prediction result information regarding a defect in the layers obtained by the second assessment unit 15, outputs defect position information about the additive-manufactured article.SELECTED DRAWING: Figure 3

Description

Technical Field

[0001] The present invention relates to an additive manufacturing quality determination device and an additive manufacturing quality determination method.

Background Art

[0002] Additive manufacturing is used in the manufacture of aftermarket parts, rapid prototyping of molds and special machine parts, etc. However, quality assurance has not been established for additive manufactured products. This is a factor that prevents manufacturers from adopting metal additive manufacturing for high-value-added applications that require high reliability.

[0003] Regarding methods for checking the quality of additive manufacturing, methods of acquiring data using monitoring equipment such as cameras during manufacturing and estimating quality have been studied. For example, in Patent Document 1, it is said that an imaging device that images a region including the forming surface during the manufacture of an additive manufactured product, and a quality estimation device for an additive manufactured product that estimates quality by machine learning using image information and quality obtained based on images of the forming surfaces of a plurality of layers of the imaged additive manufactured product as a training data set can be provided.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] From the perspective of quality inspection, it is important to confirm the defect position of an additive manufactured product. For example, in the case of an additive manufactured product used as a cooling water pipe, the airtightness of this pipe is very important. Therefore, it is necessary to check for defects around the pipe portion and conduct individual airtightness evaluation tests, etc.

[0006] On the other hand, in the case of additive-molded parts used as cooling water piping, defects outside the piping section may not affect the quality. Therefore, confirming the presence or location of defects in the additive-molded part is extremely important for quality assurance. To guarantee the quality of the additive-molded part, it is advisable to attach information such as defect information to the document as quality information for the additive-molded part.

[0007] Quality checks can be performed using destructive testing or non-destructive testing such as X-ray CT (Computed Tomography). Destructive testing, when used to locate defects, destroys the added-on part, making it unsuitable for inspecting added-on parts used in actual equipment. Furthermore, X-ray CT, used in non-destructive testing, can only be applied to thin-walled areas where X-rays can penetrate.

[0008] Furthermore, Patent Document 1 describes a device that can estimate defects in additive-molded parts during manufacturing based on information obtained from images taken by an imaging device. This device can estimate the density of additive-molded parts and the location of defects from the relationship between the brightness of the molded surface and the molding density. However, analysis based on data obtained from comparing brightness and density makes it difficult to estimate the depth at which defects are formed from the molded surface and thus the location of the defects.

[0009] Therefore, the objective of this invention is to non-destructively analyze the location of internal defects in additive-formed products. [Means for solving the problem]

[0010] To solve the aforementioned problems, the additive manufacturing product quality determination device of the present invention uses the monitoring results of each layer of the additive manufacturing product obtained by an additive manufacturing device equipped with a monitoring device. each layer The device comprises: a first evaluation unit that predicts the generation of defects in the eye; a second evaluation unit that predicts the generation and / or disappearance of defects in each layer of the added-on molded product based on the monitoring results of a predetermined number of layers above each of the layers of the added-on molded product by the first evaluation unit, and reflects this in the defect generation prediction result information for each layer; and an output unit that outputs defect location information of the added-on molded product based on the defect generation prediction result information for each layer obtained by the second evaluation unit. The predetermined number of layers is determined such that the correlation between the defect occurrence region of the X-ray CT image and the superimposed information of the monitoring results from the first layer to the layer above it is higher than a threshold, and the defect location information includes the layer number and the internal defect location including depth information from the fabrication surface. It is characterized by the following:

[0011] The present invention's method for determining the quality of additive-molded products is based on the monitoring results of each layer of the additive-molded product by an additive-molding apparatus equipped with monitoring equipment. each layer Steps to predict the generation of eye defects, The steps include: predicting the generation and / or disappearance of defects in each layer of the additive-molded product based on the monitoring results of a predetermined number of layers above each layer; reflecting this in the defect generation prediction result information for each layer; and outputting defect location information of the additive-molded product based on the defect generation prediction result information obtained for each layer. The predetermined number of layers is determined such that the correlation between the defect occurrence region of the X-ray CT image and the superimposed information of the monitoring results from the first layer to the layer above it is higher than a threshold, and the defect location information includes the layer number and the internal defect location including depth information from the fabrication surface. It is characterized by the following: Other means will be described within the descriptions of embodiments for carrying out the invention. [Effects of the Invention]

[0012] According to the present invention, it is possible to non-destructively analyze the location of internal defects in additive-formed products. [Brief explanation of the drawing]

[0013] [Figure 1] This is a schematic diagram of an additive manufacturing device. [Figure 2] This is a schematic diagram of a cross-section showing a molten pool that spans both the N+1 layer and the N layer. [Figure 3] This is a logical block diagram of the additive manufacturing product quality determination device. [Figure 4] This is a physical block diagram of the additive manufacturing product quality determination device. [Figure 5] This is a flowchart of the quality assessment process. [Figure 6] This is a flowchart of the quality determination process for modified versions. [Figure 7] This is a flowchart for determining the number of layers to be stacked in an additive-type molded part. [Figure 8] This figure shows the X-ray CT image of the Nth layer. [Figure 9A] This figure shows the mapping image of the Nth layer of optical tomography. [Figure 9B] It is a diagram showing a mapping image of optical tomography of the N+1 layer. [Figure 9C] It is a diagram showing a mapping image of optical tomography of the N+2 layer. [Figure 9D] It is a diagram showing a mapping image of optical tomography of the N+3 layer. [Figure 10] It is a diagram showing a mapping image of optical tomography. [Figure 11] It is a graph showing the luminance and defect rate of the superposition of optical tomography.

Embodiments for Carrying Out the Invention

[0014] Hereinafter, embodiments for carrying out the present invention will be described in detail with reference to each figure. The present invention is not limited to the embodiments described here, and it is possible to appropriately combine with known technologies or improve based on known technologies without departing from the technical idea of the invention.

[0015] Figure 1 is a schematic diagram of the additive manufacturing apparatus 4. As shown in Figure 1, the additive manufacturing apparatus 4 is roughly divided into an additive manufacturing unit 41 that performs additive manufacturing processing, an inspection apparatus, a control apparatus 430 that controls these, and a communication unit 431. The inspection apparatus evaluates the powder layer and the solidified layer formed in the additive manufacturing unit 41. The control apparatus 430 performs overall control of the additive manufacturing unit 41 and the inspection apparatus. The communication unit 431 transmits inspection data by the inspection apparatus to the additive manufacturing product quality determination apparatus 1 shown in Figure 3.

[0016] The additive manufacturing apparatus 4 in the present invention is a metal three-dimensional additive manufacturing apparatus using the Powder Bed Fusion method. The additive manufacturing apparatus 4 irradiates energy onto a powder layer in which metal powder (raw material powder) serving as a raw material for the additive manufactured object is spread to form a solidified layer in a two-dimensional plane, and manufactures an additive manufactured product by repeatedly stacking.

[0017] Figure 1 shows a laser irradiation device as a heat source supply device for solidifying the powder layer composed of raw material powder 414, and includes a laser oscillator 42, a process fiber 43, a galvanometer head 44, and a laser coaxial illumination 45. The heat source supply device is not particularly limited as long as it can melt and solidify the powder, and may be an electron beam irradiation device in addition to a laser irradiation device.

[0018] The processing chamber 411 in which the additive-formed body 417 is manufactured has a gas supply pipe 412a and a gas exhaust pipe 412b, and is configured to control the atmosphere of the processing chamber 411. The atmosphere can be controlled, for example, by using an inert gas atmosphere or a vacuum atmosphere when using laser light as the heat source, and a vacuum atmosphere when using an electron beam as the heat source.

[0019] The interior of the processing chamber 411 is divided into a raw material powder storage area 420, an additive molding area 421 where the powder layer is melted and solidified to form a solidified layer, and a raw material powder recovery area 422 where excess raw material powder is recovered. The raw material powder storage area 420 is the area for storing the raw material powder 414 of the additive molded body. The additive molding area 421 is the area where the powder layer is formed by stacking the raw material powder 414 and where the powder layer is melted and solidified by a heat source supply device to form a solidified layer. The raw material powder recovery area 422 is the area where excess raw material powder is recovered when the powder layer is formed in the additive molding area 421.

[0020] The powder feeder 413 moves in the direction of the white arrow in Figure 1 to supply powder from the raw material powder storage area 420 to the additive molding area 421. For example, a recoater, coater, squeegee, and blade can be used as the powder feeder 413. In the raw material powder storage area 420 and the additive molding area 421, the sample stages 415a and 415b on which the powder is placed have a configuration that allows them to move up and down in the vertical direction in Figure 1. The sample stage 415b on which additive molding is performed may be equipped with a heater capable of heating the powder layer or solidified layer, although it is not shown. A heater capable of heating from 25°C to about 650°C is preferred. Heating the powder layer or solidified layer has the effect of improving the molding speed by removing moisture from the raw material powder or reducing the amount of heat input by the beam, and reducing distortion by making the temperature distribution uniform.

[0021] In this embodiment, the inspection apparatus includes a visible light image camera 46, an infrared image camera 47a, a plasma emission camera 47b, and a molten pool observation device 48. The visible light image camera 46 and the plasma emission camera 47b observe images in the visible light region of the powder layer and the solidified layer. The infrared image camera 47a captures infrared radiation images of the powder layer and the solidified layer and observes the resulting thermal images. As described later, excessive or insufficient heat input in additive manufacturing can cause internal defects. If a defect occurs inside the solidified layer, the thermal conductivity decreases and the thermal diffusivity also decreases. Therefore, by analyzing the cause of the internal defect, it is possible to estimate the internal defect caused by the change in heat input. The molten pool observation device 48 observes the state of the powder layer when it is irradiated with a heat source and melted.

[0022] This embodiment relates to an analysis method using thermal images during the manufacturing process, and utilizes data in the layering direction (e.g., the N+1th layer relative to the Nth layer). The analysis method will be described in detail below.

[0023] The control device 430 is connected to the additive manufacturing apparatus 4 and the inspection apparatus by wire or wireless means, and controls their operation. The drive of the powder feeder 413 and sample stages 415a and 415b, as well as the operation of the laser oscillator 42 and galvanometer head 44, are also controlled and monitored by the control device 430.

[0024] Furthermore, the control device 430 also determines the quality of the powder layer and the solidified layer based on the evaluation results of the inspection device. The control device 430 includes a visible light image processing unit that processes images obtained from the visible light image capture device 46 and the plasma emission image capture device 47b and determines whether or not there are defects, and an infrared image processing unit that processes images obtained from the infrared image capture device 47a and determines whether or not there are defects.

[0025] Non-uniformity in the quality of add-on molded parts The quality of an additive-built part depends not only on the characteristics of the part itself, but also on the presence or absence of heterogeneity, such as defects or unusual metallic structures, that occur during the manufacturing process. Here, heterogeneity is classified and described as pores, cracks, structural heterogeneity, and geometric anomalies.

[0026] Pores are caused by gases remaining in the additively manufactured part during additive manufacturing. In gas atomization, which is used to manufacture metal powders, gas is used to produce the metal powder, so it is presumed that gas components remaining in the metal powder become pores in the additively manufactured part. In addition, excessive heat input during additive manufacturing can lead to pore formation in relation to metal melting phenomena such as Marangoni convection caused by changes in the surface tension of the molten pool, elemental evaporation, and keyhole formation.

[0027] If the energy of the molten pool is insufficient, the powder particles cannot be melted, resulting in voids due to unmelted areas (LOF: Lack of Fusion). These voids are often irregular in shape and may contain unmelted powder.

[0028] Cracks are also a type of defect that can occur in additive-formed parts, and they result from the pores and unmelted areas mentioned earlier remaining as cracks. Cracks can also occur if there is a difference in the coefficient of thermal expansion between the substrate and the additive-formed part, or if there is a large thermal gradient in the molten pool during solidification.

[0029] Next, we will discuss structural heterogeneity (anisotropic inclusions). Generally, changing the heat input during fabrication alters the microstructure of the additive-formed part. Changes in the temperature gradient of the molten pool alter the solidification rate, affecting the microstructure, such as the anisotropy of the additive-formed part. Furthermore, the atmosphere during fabrication and impurities in the metal powder, such as oxide formation by oxygen, also affect the microstructure. Changes in the microstructure directly lead to changes in the mechanical properties of the additive-formed part.

[0030] Finally, let's discuss geometric anomalies. Geometric anomalies relate to dimensional changes and surface roughness. They relate to the stability of the melt value, and changes in the size and shape of the molten pool have a significant impact on the dimensions and surface roughness of the additive-formed part. To minimize these geometric anomalies, a stable molten pool size and shape are required.

[0031] The pores (including LOF), cracks, structural heterogeneity, and geometric anomalies described above are all thought to be formed as a result of melting phenomena during additive manufacturing. Therefore, by monitoring information related to melting phenomena during additive manufacturing, it is possible to determine the quality of the additively manufactured product.

[0032] Monitoring data There are various methods for additive manufacturing. Powder bed fusion is a method in which a laser beam (L-PBF: Laser Powder Bed Fusion) or electron beam (EBM: Electron Beam Melting) is irradiated onto a flat layer of metal powder to perform additive manufacturing. Directed energy deposition (DMP) is a method of additive manufacturing that involves extruding metal powder, and includes LMD (Laser Metal Deposition) and DMP (Direct Metal Printing). Here, we will describe, as a representative example, the monitoring of the molten pool in powder bed fusion bonding using laser beam irradiation.

[0033] Melting in additive manufacturing is caused by laser irradiation. The molten pool is affected by the manufacturing parameters in the process, such as laser power and scan speed, as well as material properties such as the enthalpy required for melting the metal powder and the laser reflectivity. One change in the molten pool is its temperature. By monitoring information related to the temperature change of the molten pool, the condition of the molten pool can be inferred. In powder bed fusion induced by laser beam irradiation, reflected and scattered lasers, electromagnetic waves such as infrared radiation depending on the temperature, and a plasma plume composed of ionized gas and metal vapor are emitted from the molten pool. Since the electromagnetic waves and plasma plume emitted from the molten pool change depending on the state and temperature of the molten pool, they are important indicators for capturing the formation of various defects related to the melting phenomenon and determining quality.

[0034] Common monitoring equipment in additive manufacturing includes visible light monitoring, electromagnetic wave monitoring, and acoustic monitoring. This section will provide a detailed explanation of electromagnetic wave monitoring.

[0035] Electromagnetic wave monitoring primarily involves observing infrared radiation (wavelengths 700 nm and above), whose intensity changes with temperature. Methods include optical tomography (OT), photodiodes, two-color thermometers, thermography, spectrometers, and high-speed cameras. While photodiodes and two-color thermometers used for detailed observation of melt pools (melt pool monitoring) lack spatial resolution, their spatial relationship can be determined by measuring them coaxially with the laser and comparing them to a set laser irradiation pattern. In addition to thermal radiation from the melt pool, reflection and scattering of laser light must also be considered; however, generally, only the desired wavelength is observed using a spectral filter.

[0036] Optical tomography and thermography using CCD (Charge Coupling Device) cameras and CMOS (Complementary MOS) cameras have the characteristic of having spatial resolution. These CCD and CMOD cameras can be installed coaxially with a laser to monitor electromagnetic waves. Furthermore, CCD cameras and CMOD cameras can monitor the entire build area from above the build chamber, etc. These are non-coaxial electromagnetic wave monitoring means. Also, like the photodiodes mentioned above, CCD cameras and CMOD cameras select the wavelength range to be observed using spectral filters.

[0037] In this way, electromagnetic wave monitoring, either coaxial or non-coaxial, during additive manufacturing makes it possible to observe information related to melting. Since changes in the molten pool (temperature, convection, size, melting depth, etc.) are related to defect formation, the location of defects can be inferred by analyzing the relationship between defects formed in the additively manufactured product and electromagnetic wave monitoring.

[0038] Defect inspection of add-on parts As mentioned above, melting in additive manufacturing is influenced by the manufacturing parameters and the physical properties of the metal powder being manufactured. The manufacturing parameters and the metal powder being manufactured vary depending on the additive-manufactured product being manufactured. Therefore, for example, in electromagnetic wave monitoring, the monitoring means cannot estimate quality using a uniform method such as acquiring brightness information, determining a brightness threshold, and estimating the presence or absence of defects. Thus, the quality determination method used in this invention requires prior confirmation of defect formation in the target material in order to know the correlation between quality items (inhomogeneity, material properties, residual stress, etc.) and monitoring data. Therefore, defect inspection methods (destructive and non-destructive) are described here.

[0039] Destructive methods include cutting and grinding of the additive-formed part, and some require specimen processing to obtain material properties. The aforementioned heterogeneity (pores, cracks, structural heterogeneity, and geometric anomalies) can also be confirmed by observing the structure at various locations on the additive-formed part. For example, by polishing the cut surface at a desired location and observing it with an optical microscope, scanning electron microscope, EPMA (electron probe microanalyzer), or EBSD (electron backscatter diffraction), pores, cracks, structural heterogeneity, and geometric anomalies (deformations) inside the additive-formed part can be confirmed. Geometric anomalies (roughness) can also be evaluated using a roughness meter or laser microscope.

[0040] Furthermore, material properties include mechanical properties (tensile tests, fatigue tests, hardness tests, impact tests, etc.) and corrosion resistance evaluation tests (immersion tests in various corrosive liquids, electrochemical methods such as public potential measurement). Non-destructive testing methods include X-ray CT, surface roughness measurement, and density measurement.

[0041] Correspondence between heterogeneous areas and monitoring data In laser beam-induced powder bed fusion, additive manufacturing is performed by stacking layers of a certain thickness. The thickness T of the layers is adjusted by the material and manufacturing parameters. Here, we consider the Nth layer (where N is a natural number) and the N+1 and above layers. First, defects in the Nth layer are formed during the melting process during the Nth layer's construction. Therefore, it is necessary to predict the defect location in the Nth layer from the monitoring data of the Nth layer. Generally, laser melting is performed with a layer thickness of T or greater.

[0042] Figure 2 is a schematic diagram showing a molten pool 21 that spans both the N+1th layer 23 and the Nth layer 22. In the fabrication of the N+1th layer 23, the Nth layer 22 is melted, causing the Nth layer 22 and the N+1th layer 23 to be densely bonded. Therefore, the quality of the Nth layer 22 is affected by the melting of the upper layers, from the N+1th layer 23 upwards. In the Nth layer 22, the melting of the N+1th layer 23 causes the disappearance and formation of heterogeneous areas, which must be determined from the monitoring data of the N+1th layer 23. Similarly, in the layers above this, from the N+2nd layer upwards, it is necessary to consider the influence of the Nth layer 22 on defect formation over a predetermined number of layers.

[0043] Since the depth of the molten pool is finite, when considering the Nth layer defect, it is not necessary to consider the melting of all the upper layers. The depth of melting is influenced by the temperature at the time of melting. In the monitoring data, areas where high temperatures are predicted, i.e., areas with high infrared brightness, indicate deep melting. Areas where low temperatures are predicted, i.e., areas with low infrared brightness, indicate that the melting depth is relatively shallow. In this way, by considering that the penetration depth differs depending on the infrared brightness, it is possible to estimate the influence of the upper layers on the formation of the Nth layer defect.

[0044] To predict defects, it is necessary to first perform additive manufacturing on a standard test specimen under the same conditions as the actual additive manufacturing process, and to obtain monitoring data and quality measurement results. As mentioned above, additive manufacturing is performed considering the melting depth. For example, consider the case where the location of pores, which are internal defects, is confirmed by X-ray CT, and the relationship between the pores and the monitoring data is analyzed. As mentioned above, excessive heat input, i.e., high temperatures in the molten pool, is a factor that causes pores.

[0045] When the molten pool reaches high temperatures, the infrared brightness increases. By examining the correspondence between the results of electromagnetic wave monitoring and the results obtained by X-ray CT, it is possible to calculate the presence or absence of pores, or the probability of pore formation, according to the brightness. In this case, since the formation of pores in the Nth layer is influenced by the formation of defects in the N+1th layer and above, an analysis is performed considering the formation and disappearance of pores in the Nth layer from the monitoring data of the N+1th layer and above. By comparing the pore formation location obtained by X-ray CT with the monitoring data, the relationship between the monitoring data of the Nth layer and the layers above it that influences pore formation in the Nth layer can be obtained.

[0046] Here, we have illustrated the relationship between the pore location obtained by X-ray CT and infrared monitoring data, but similar methods can be used to obtain the data relationship for other heterogeneous areas and monitoring methods.

[0047] Figure 3 is a block diagram showing the functional configuration of the additive-molded product quality determination device 1. The additive-molded product quality determination device 1 determines the quality of the additive-molded product from the optical tomography data obtained during the molding process. Functionally, the additive-molded product quality determination device 1 includes an output unit 11, a determination unit 12, a correlation determination unit 13, a first evaluation unit 14, a second evaluation unit 15, a data acquisition unit 16, and a defect location calculation unit 17. The additive-molded product quality determination device 1 is a computer equipped with a processor described later, and by executing an additive-molded product quality determination program (not shown), it realizes each functional unit and executes the additive-molded product quality determination method.

[0048] The first evaluation unit 14 predicts the generation of defects in each layer of the additive-formed product based on the monitoring results of each layer of the additive-formed product by the additive-formed product manufacturing apparatus 4 equipped with monitoring equipment. Here, the prediction of the generation of defects in each layer is the first evaluation result. The monitoring results are provided from at least one of the following: optical tomography, CCD camera, CMOS camera, two-color thermometer, thermography, high-speed camera, and photodiode. The first evaluation unit 14 uses the brightness mapping image of the optical tomography of each layer of the additive-formed product as the monitoring result, but is not limited to this; a predetermined shape in the optical tomography may also be used as the monitoring result.

[0049] The second evaluation unit 15 predicts the generation and / or disappearance of defects in each layer based on the monitoring results of a predetermined number of layers above each layer of the added-on product by the first evaluation unit 14, and reflects this in the defect generation prediction result information for each layer. Here, the defect generation prediction result information for each layer is the second evaluation result.

[0050] The output unit 11 outputs defect location information of the additive-molded product based on the defect generation prediction result information for each layer obtained by the second evaluation unit 15. The determination unit 12 determines the quality of the additive-molded product based on the defect information of the additive-molded product output by the output unit 11. The quality data determined by the determination unit 12 includes at least one of the following: location of internal defects, dimensions of internal defects, density, defect rate, cracks, structural heterogeneity, and geometric anomalies.

[0051] The correlation determination unit 13 determines the superposition of a predetermined number of layers in which the correlation with the defect in the Nth layer (where N is a natural number) of the added-on molded product is higher than a threshold.

[0052] The data acquisition unit 16 acquires monitoring data for all layers of the additive manufacturing process. The defect location calculation unit 17 calculates the defect location information of the added part from the monitoring data.

[0053] Figure 4 is a block diagram showing the hardware configuration of the additive-molded product quality determination device 1. The additive molding quality determination device 1 has a hardware configuration comprising a processor 101, a storage unit 102, an operation unit 103, a display unit 104, and a communication unit 105. The processor 101, storage unit 102, operation unit 103, display unit 104, and communication unit 105 are connected by a bus 106.

[0054] The processor 101 is, for example, a CPU (Central Processing Unit) and provides overall control of the additive-molded product quality determination device 1. The memory unit 102 is the work area for the processor 101. The memory unit 102 is a non-temporary or temporary recording medium that stores various programs and data, and stores an additive-molded product quality determination program (not shown). Examples of the memory unit 102 include ROM (Read Only Memory), RAM (Random Access Memory), HDD (Hard Disk Drive), and flash memory.

[0055] The operation unit 103 is used to input operation data. Examples of the operation unit 103 include a keyboard, mouse, touch panel, and numeric keypad. The display unit 104 is, for example, a display or printer, and displays data. The communication unit 105 connects to a network and sends and receives data.

[0056] Quality assessment of add-on parts in actual 3D printing. In the fabrication of additive-type molded parts, the presence of heterogeneity is predicted based on the relationship between monitoring data obtained from standard test specimens and the presence of heterogeneity, and the quality is then determined.

[0057] Figure 5 is a flowchart of the quality judgment process. Initially, the data acquisition unit 16 acquires monitoring data for all layers of the additive manufacturing process (step S10). This monitoring data is optical tomography data. Next, the processor 101 repeats the processing of each layer of the monitoring data of the added part from step S11 to S15.

[0058] In this iterative process, the first evaluation unit 14 predicts the formation of a heterogeneous region in the Nth layer based on the monitoring results of the Nth layer (step S12). The information predicted here is also called the first evaluation result.

[0059] The second evaluation unit 15 then predicts the generation and / or disappearance of heterogeneous areas in the Nth layer based on the monitoring results of the N+1th layer (step S13), and reflects this in the defect generation prediction result information for the Nth layer (step S14). The information reflected here is also called the second evaluation result.

[0060] The processor 101 determines whether it has repeated the processing for all layers of the monitoring data of the additive-molded part (step S15). If the processor 101 has not repeated the processing for all layers of the monitoring data of the additive-molded part, it returns to the process in step S11.

[0061] The output unit 11 outputs heterogeneity information of the added-on product based on the second evaluation results obtained for each layer (step S16). Then, the determination unit 12 determines the quality of the added-on product based on this output result (step S17), and the process shown in Figure 5 is terminated. The quality determination can be arbitrarily set based on the defect rate, defect size, density, defect location, number density of inclusions, material properties, etc. The quality assessment described here can be performed after the molding process, or it can be performed simultaneously with the molding process.

[0062] Although monitoring data for layers N+1 and above is not discussed here, monitoring data for layers N+1 and above may be used to predict heterogeneity in the Nth layer, taking into account the melting depth during melting.

[0063] Figure 6 is a flowchart of the quality determination process for the modified example. Initially, the data acquisition unit 16 acquires monitoring data for all layers of the additive manufacturing process (step S20). Next, the processor 101 repeats the processing of each layer of the monitoring data of the added part from step S21 to S25.

[0064] In this iterative process, the first evaluation unit 14 predicts the formation of the heterogeneous region in the Nth layer based on the monitoring results of the Nth layer (step S22). The information predicted here is also called the first evaluation result.

[0065] The second evaluation unit 15 then predicts the generation and / or disappearance of heterogeneous areas in the Nth layer based on the monitoring results from the N+1th layer to the N+Mth layer (where M is a natural number) (step S23), and reflects this in the defect generation prediction result information for the Nth layer (step S24). The information reflected here is also called the second evaluation result.

[0066] The processor 101 determines whether it has repeated the processing for all layers of the monitoring data of the additive-molded part (step S25). If the processor 101 has not repeated the processing for all layers of the monitoring data of the additive-molded part, it returns to the process in step S21. If the processor 101 has repeated the processing for all layers of the monitoring data of the additive-molded part, it proceeds to the process in step S26.

[0067] In step S26, the output unit 11 outputs non-uniformity information of the added-on product based on the second evaluation results obtained for each layer. Then, the determination unit 12 determines the quality of the added-on product based on this output result (step S27), and the process shown in Figure 6 is terminated.

[0068] Furthermore, it is advisable to pre-determine the number of layers above the Nth layer that affect the Nth layer. This allows the additive manufacturing product quality determination device 1 to calculate the optimal number of layers for the additive manufacturing product, output highly accurate heterogeneity information, and determine the quality of the additive manufacturing product with high accuracy. In addition, to guarantee the quality of the additive manufacturing product, it is conceivable to attach an image of the superimposed layers of the additive manufacturing product as documentation to demonstrate its quality.

[0069] Figure 7 is a flowchart of the process for determining the number of layers to be stacked in an add-on product. First, the data acquisition unit 16 acquires monitoring data for all layers of the additive manufacturing process (step S31). The defect location calculation unit 17 calculates defect location information for the additive manufactured product from this monitoring data for all layers of the additive manufacturing process (step S32).

[0070] Next, the processor 101 repeats the variable P from 1 to a predetermined number over steps S33 to S36. Note that the variable P is a natural number.

[0071] In this iterative process, the first evaluation unit 14 and the second evaluation unit 15 calculate superposition information of the monitoring results from the Nth layer to the N+Pth layer (step S34). Then, the correlation determination unit 13 calculates the correlation between the defect occurrence area in the X-ray CT image and the superposition information of the monitoring results.

[0072] In step S36, the processor 101 determines whether the variable P has been repeated from 1 to a predetermined number of times. If the processor 101 has not been repeated from 1 to a predetermined number of times, it returns to the process in step S33. If the processor 101 has been repeated from 1 to a predetermined number of times, it proceeds to the process in step S37.

[0073] In step S37, the correlation determination unit 13 terminates the process shown in Figure 7 when it determines a predetermined number of layers to be superimposed, the number of layers whose correlation with the occurrence of defects is higher than a threshold. The present disclosure will be further described below with reference to examples. However, the present disclosure is not limited to these examples.

[0074] 《Modeling and Monitoring Equipment》 The fabrication was performed by powder bed fusion induced by laser beam irradiation, and monitoring was carried out using an optical tomography monitoring system. Within the fabrication area, the luminance generated from the molten pool by laser irradiation from vertically above was captured by an sCMOS (scientific CMOS) camera installed at an angle above. Although the sCMOS camera was installed at an angle above the fabrication surface, the distance and angle were corrected in the analysis program to transform it into an image as if observed from directly above. Subsequent measurements used integrated luminance values. Integrated luminance values ​​are the sum of the luminance values ​​in each pixel of the image. The unit of optical tomography data is expressed in Gv (Gray value).

[0075] 《Formation Requirements》 In this fabrication process, only in-skin conditions were applied, where the fabrication area was filled with laser irradiation. The laser power and fabrication speed were set to result in a heat input greater than the optimal range. Here, the optimal range was defined as the condition under which the internal defect rate was experimentally below a predetermined percentage.

[0076] X-ray CT To identify defects in additive-built parts from optical tomography data, it is necessary to obtain X-ray CT results that serve as a reference for determining defect size and location. The obtained CT data was reconstructed into three-dimensional data and then analyzed using the defect and inclusion analysis module of analysis and visualization software. The inventors then obtained the defect rate in addition to the defect location and defect distribution.

[0077] 《Density measurement》 To verify the accuracy of the defect rate obtained by X-ray CT, the inventors calculated the defect rate from relative density measurements using the immersion method. The immersion method employed Archimedes' principle. By comparing the obtained density with the true density, the inventors obtained a defect rate of 2.5%. Although there was an error of approximately 11% compared to the defect rate of 2.8% obtained by the X-ray CT analysis, the inventors determined that the defect information was generally accurate using X-ray CT.

[0078] Correlation between optical tomography and quality The X-ray CT output was generated as slice data with a predetermined pitch thickness, and eight of these slices were stacked to achieve the same thickness as each individual layer of the fabricated object.

[0079] Figure 8 shows the X-ray CT image of the Nth layer. The black areas in this X-ray CT image indicate defects.

[0080] Figures 9A to 9D show the luminance mapping images in optical tomography from the Nth layer to the N+3rd layer. When comparing the high-brightness areas of optical tomography with X-ray CT in each layer from the Nth to the N+3rd layer, many defects are also found in the low-brightness areas.

[0081] Figure 10 shows an image obtained by stacking four layers of luminance mapping images obtained using optical tomography. This image is a stacked image of luminance mapping images obtained from optical tomography, spanning four layers. This image allows us to indicate, by density, areas where heat was continuously applied across multiple layers. This image is intended for quality evaluation of the printed object and should ideally be included as documentation accompanying the object as part of the printing quality information.

[0082] Figure 11 is a graph showing the relationship between brightness and defect rate in superimposed optical tomography images. The vertical axis of the graph represents the defect rate. The horizontal axis of the graph represents the brightness of the optical tomography. From this graph, it can be seen that the higher the brightness, the higher the probability of it being a defect. By analyzing monitoring data while considering the melting depth, and then performing the actual additive manufacturing process, it is possible to predict the heterogeneity of the additively manufactured product.

[0083] (modified version) The present invention is not limited to the embodiments described above, and includes various modifications. For example, the embodiments described above are described in detail to make the present invention easier to understand, and are not necessarily limited to those having all the configurations described. It is possible to replace parts of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add configurations from other embodiments to the configuration of one embodiment. Furthermore, it is possible to add, delete, or replace parts of the configuration of each embodiment with other configurations.

[0084] Each of the above configurations, functions, processing units, and processing means may be implemented in part or in whole by hardware, such as an integrated circuit. Each of the above configurations and functions may also be implemented in software by a processor interpreting and executing a program that implements each function. Information such as programs, tables, and files that implement each function can be stored in a recording device such as memory, a hard disk, or an SSD (Solid State Drive), or on a recording medium such as a flash memory card or a DVD (Digital Versatile Disk).

[0085] In each embodiment, the control lines and information lines shown are those deemed necessary for explanation and do not necessarily represent all control lines and information lines in the actual product. In practice, it can be assumed that almost all components are interconnected. [Explanation of Symbols]

[0086] 1. Additive-formed product quality determination device 101 Processors 102 Storage section 103 Operation section 104 Display section 105 Communications Department 106 Bus 11 Output section 12 Judgment section 13 Correlation Determination Unit 14. First Evaluation Department 15. Second Evaluation Department 16. Data Acquisition Unit 17. Defect location calculation unit 21 Melting pool 22nd layer N 23 N+1th layer 4. Additive molding device 41 Add-on molding part 411 Processing Room 412a Gas supply pipe 412b Gas exhaust pipe 413 Powder feeding machine 414 Raw material powder 415a Sample stage 415b Sample stage 417 Add-on parts 42 Laser Oscillators 420 Raw material powder storage area 421 Additive molding area 422 Raw material powder recovery area 430 Control device 43 Process Fibers 44 Galvano Head 45. Laser coaxial illumination 46 Visible light imaging device 47a Infrared imaging device 47b Plasma emission imaging machine 48. Molten pool observation device

Claims

1. A first evaluation unit predicts the generation of defects in each layer of an additively manufactured product based on the monitoring results of each layer of the additively manufactured product using an additive manufacturing device equipped with monitoring equipment. A second evaluation unit predicts the generation and / or disappearance of defects in each layer of the added-on molded product based on the monitoring results of a predetermined number of layers above each of the layers of the added-on molded product by the first evaluation unit, and reflects this in the defect generation prediction result information for each of the layers. An output unit outputs defect location information of the added-on product from the defect generation prediction result information of each layer obtained by the second evaluation unit, Equipped with, The predetermined number of layers is determined such that the correlation between the defect occurrence region of the X-ray CT image and the superimposed information of the monitoring results from the aforementioned layer to the layer above it is higher than a threshold. The aforementioned defect location information includes the layer number and the internal defect location, which includes depth information from the fabrication surface. An additive manufacturing product quality determination device characterized by the following:

2. A determination unit determines the quality of the added-on product based on the defect location information of the added-on product output by the output unit, The additive molded product quality determination device according to claim 1, further comprising the following:

3. The quality data determined by the determination unit includes at least one of the following: the location of internal defects including their depth from the molded surface, the dimensions of internal defects, density, defect rate, cracks, structural heterogeneity, and geometric anomalies. The device for determining the quality of an added molded product according to feature 2.

4. The first evaluation unit uses the luminance mapping image of each layer of the additive-formed product as the monitoring result. The device for determining the quality of an added molded product according to feature 1.

5. The first evaluation unit uses the predetermined shape in the optical tomography of each layer of the added-on product as the monitoring result. The device for determining the quality of an added molded product according to feature 1.

6. The second evaluation unit estimates the melting depth based on the infrared brightness, determines the range of the upper layer to refer to the range of layers 1 to M layers (where M is a natural number of 2 or more) above each of the layers of the added-on molded product corresponding to the estimated melting depth, predicts the generation and / or disappearance of defects in each of the layers from the monitoring results of the upper layer range, and reflects this in the defect generation prediction result information for each of the layers. The device for determining the quality of an added molded product according to feature 1.

7. The monitoring results are provided by at least one of the following: optical tomography, CCD camera, CMOS camera, two-color thermometer, thermography, high-speed camera, and photodiode. The device for determining the quality of an added molded product according to any one of claims 1 to 6.

8. The system includes a correlation determination unit that determines the superposition of a predetermined number of layers in which the correlation between the defect occurrence region of the Nth layer (N is a natural number) of the X-ray CT image of the aforementioned additive-formed product and the superposition information of the monitoring results is higher than a threshold. The device for determining the quality of an added molded product according to feature 1.

9. The system includes a defect location calculation unit that calculates internal defect location information, including the layer number and depth from the fabrication surface of the added part, based on the monitoring results. The device for determining the quality of an added molded product according to feature 8.

10. A step of predicting the generation of defects in each layer of an additively manufactured product based on the monitoring results of each layer of the additively manufactured product using an additive manufacturing device equipped with monitoring equipment. A step of predicting the generation and / or disappearance of defects in each layer of the aforementioned additive product based on the monitoring results of a predetermined number of layers above each layer of the aforementioned additive product. A step of reflecting the result of defect generation prediction information for each of the aforementioned layers, A step of outputting defect location information of the added product from the defect generation prediction result information obtained in each of the aforementioned layers, Execute, The predetermined number of layers is determined such that the correlation between the defect occurrence region of the X-ray CT image and the superimposed information of the monitoring results from the aforementioned layer to the layer above it is higher than a threshold. The aforementioned defect location information includes the layer number and the internal defect location, which includes depth information from the fabrication surface. A method for determining the quality of an add-on molded product, characterized by the following features.

11. A step of determining the quality of the added-on product based on the defect location information of the added-on product, The method for determining the quality of an added molded product according to claim 10, further comprising the above.

12. The quality data used to determine the aforementioned added part includes at least one of the following: location of internal defects including depth from the molded surface, dimensions of internal defects, density, defect rate, cracks, structural heterogeneity, and geometric anomalies. The method for determining the quality of an add-on molded product according to feature 11.

13. The step of obtaining the luminance mapping image of each layer of the additively fabricated product from optical tomography as the monitoring result, The method for determining the quality of an added product according to claim 10, characterized by performing the following:

14. The step of obtaining the predetermined shape in optical tomography of each layer of the additive-formed product as the monitoring result, The method for determining the quality of an add-on molded product according to feature 10.

15. A step of estimating the melting depth based on the brightness of infrared rays, determining an upper layer range that refers to a range of layers 1 to M layers (where M is a natural number of 2 or more) above each layer of the added-on molded product according to the estimated melting depth, predicting the generation and / or disappearance of defects in each layer from the monitoring results of the upper layer range, and reflecting this in the defect generation prediction result information for each layer, The method for determining the quality of an add-on molded product according to feature 10.

Images