JP2018079580A - Shaping apparatus and shaping method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To reliably detect an image defect and improve quality of a solid object.SOLUTION: There is provided a shaping apparatus that prepares a solid object by laminating a shaping material on a stage on the basis of three-dimensional shape data of a solid model. The shaping apparatus comprises image forming means for forming a material image by arranging the shaping material on the basis of slice data generated from the three-dimensional shape data, a carrier for carrying the material image formed by the image forming means, illumination for exposing near infrared light to a region on the carrier including the region where the material image is carried, and image acquisition means provided with an image sensor that receives reflected light of the near infrared light by the material image and the carrier.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a modeling apparatus and a modeling method.

  A modeling apparatus that forms a three-dimensional model by stacking a large number of layers has been attracting attention. This type of additive manufacturing technology is called additive manufacturing (AM), three-dimensional printer, rapid prototyping (RP), or the like. Various modeling methods have been proposed for additive manufacturing technology. Patent Document 1 discloses a modeling method using an electrophotographic process. Patent Document 2 discloses a defect detection method using a light source having a wavelength that is not affected by the design of the printed layer.

JP 2003-053846 A JP 2007-271507 A

In a modeling method such as Patent Document 1 in which a slice image is converted into a particle image using a resin material and laminated as a sheet-unit material image, the material image is formed due to transfer failure when the particle image is formed and when the material image is transferred to a transfer body. There is a possibility that a defective part is generated. When a defective portion occurs, there is a concern that the material image of the next layer cannot be satisfactorily contacted and the lamination cannot be continued.
Japanese Patent Application Laid-Open No. 2004-228688 discloses that defect detection accuracy is improved by using a light source having a wavelength that is not affected by the design of the printed layer. However, in Patent Document 2, a defect on a printed layer having the same amount of reflected light is detected using near-infrared light, and the shape of the material image itself cannot be grasped.

  This invention is made | formed in view of the above situations, and it aims at providing the technique which can detect an image defect more reliably and can improve the quality of a solid object.

The first aspect of the present invention is:
A modeling apparatus for producing a three-dimensional object by laminating modeling materials on a stage based on three-dimensional shape data of a three-dimensional model,
An image forming unit that forms a material image by arranging the modeling material based on slice data generated from the three-dimensional shape data;
A carrier for carrying the material image formed by the image forming means;
Illumination for irradiating the region on the carrier including the region carrying the material image with near-infrared light, and receiving the near-infrared light reflected by the material image and the carrier An image acquisition means having an image sensor;
A modeling apparatus is provided.

The second aspect of the present invention is:
A modeling apparatus for producing a three-dimensional object by laminating modeling materials on a stage based on three-dimensional shape data of a three-dimensional model,
An image forming unit that forms a material image by arranging the modeling material based on slice data generated from the three-dimensional shape data;
A carrier for carrying the material image formed by the image forming means;
Illumination that irradiates near infrared light to an area on the carrier including an area carrying the material image;
In a modeling method by a modeling apparatus having
Receiving, with an image sensor, the material image irradiated by the illumination and the reflected light of the near-infrared light from the carrier;
Using the fact that the absorbance or reflectance for the near-infrared light is different between the carrier and the material image, distinguishing the carrier and the material image, and obtaining information about the material image;
The modeling method characterized by including this is provided.

The third aspect of the present invention is:
A manufacturing method of a three-dimensional object for producing a three-dimensional object by sequentially stacking modeling materials based on slice data of a three-dimensional model,
Arranging the modeling material on a carrier based on the slice data to form a material layer;
Irradiating the material layer with light containing near-infrared light to obtain near-infrared reflected light from the material layer and the carrier; and
Obtaining information on the material layer from the reflected light in the near infrared region;
The manufacturing method of the solid thing characterized by including this is provided.

  According to the present invention, an image defect can be detected more reliably and the quality of a three-dimensional object can be improved.

The figure which shows typically the structure of the modeling apparatus which concerns on 1st Embodiment. Diagram showing melt lamination of material images Diagram showing defect image generation of material image Diagram for explaining bright field illumination and near infrared illumination Schematic diagram showing the illumination optical system for material image acquisition The figure which shows the defect form image of a material image Block diagram of modeling controller Block diagram of image defect acquisition and recovery control The figure explaining the defect image comparison algorithm of a control block diagram Flow chart of defect image acquisition and recovery process of control block diagram Flow chart of defect image acquisition and recovery process of control block diagram Control block diagram of material image thickness detection and stage movement of the second embodiment The figure for demonstrating the relationship between the light absorbency data of 2nd Embodiment, and the thickness of material. Flowchart of material image thickness detection and stage movement control of the second embodiment

DESCRIPTION OF EMBODIMENTS Hereinafter, embodiments for carrying out the present invention will be exemplarily described with reference to the drawings. However, unless otherwise specified, various control procedures, control parameters, target values, etc., such as dimensions, materials, shapes, and relative arrangements of the members described in the following embodiments are described in the present invention. It is not intended to limit the scope of the above to only those.
The present invention relates to a layered modeling technique (AM technique), that is, a modeling apparatus, a modeling method, and a three-dimensional model that employs a technique for producing a three-dimensional object (three-dimensional object) by arranging modeling materials in two dimensions and laminating them in layers. The present invention relates to a method for manufacturing a product.
As the modeling material, various materials can be selected according to the application, function, purpose, etc. of the three-dimensional object to be produced. In this specification, a material constituting a three-dimensional object for modeling is referred to as “structural material”, and a portion formed of the structural material is referred to as a structure. A material that constitutes a support body (for example, a column that supports the overhang portion from below) for supporting the structure being manufactured is referred to as a “support material”. When it is not necessary to distinguish between the two, the term “modeling material” is simply used. As the structural material, for example, a thermoplastic resin such as PE (polyethylene), PP (polypropylene), ABS, PS (polystyrene), and PC (polycarbonate) can be used. As the support material, a material having thermoplasticity and water solubility can be preferably used in order to simplify the removal from the structure. Examples of the support material include carbohydrates, polylactic acid (PLA), PVA (polyvinyl alcohol), and PEG (polyethylene glycol).

Further, in this specification, digital data obtained by slicing three-dimensional shape data of a stereo model to be produced into a plurality of layers along the stacking direction is referred to as “slice data”. The slice data is generated by adding information such as support material data as necessary. A layer (an image for one layer) formed from the modeling material based on the slice data is called a “material image” or “material layer”. A “material image (material layer)” is a layer of particles formed by combining one or more material images (material layers) depending on the type of modeling material used. In addition, the image of the particle unit formed with a modeling material based on slice data may be called a “particle image”.
A three-dimensional model (that is, a three-dimensional object represented by three-dimensional shape data given to the modeling apparatus) to be manufactured using the modeling apparatus is referred to as a “modeling object”. A three-dimensional object (three-dimensional object) produced (output) by the modeling apparatus is referred to as a “modeled object”. In the case where the modeled object includes the support body, a portion obtained by removing the support body from the modeled object becomes a “structure” constituting the modeled object. In addition, a direction orthogonal to a laminated surface 49a of the stage 49 described later is referred to as “vertical direction”. Further, in the vertical direction, a direction toward the upper part of the paper surface in FIG. 1A is an upward direction, and a direction toward the lower part of the paper surface in FIG.

<First Embodiment>
(Configuration of modeling equipment)
With reference to FIG. 1A and 1B, the structure of the modeling apparatus 1 which concerns on 1st Embodiment is demonstrated. FIG. 1A is a diagram schematically illustrating the configuration of the modeling apparatus 1 according to the present embodiment. FIG. 1B is a diagram schematically illustrating an enlarged configuration of the material image detection portion.

  The modeling apparatus 1 of the present embodiment includes an outline, a control unit, an image forming unit, and a stacking unit. The control unit is a component that performs processing for generating slice data of a plurality of layers from the three-dimensional shape data of the modeling target, control of each unit of the modeling apparatus 1, and the like. The control unit includes a control unit 60, an image generation controller 10, a modeling controller 70, a plurality of motors 47 to 48, a line sensor 44, an illumination light source 45, a spectrophotometer 85, and the like. The image forming unit is a component that forms a material image for one layer made of a modeling material based on slice data of each layer. The image forming unit includes a laser scanner (exposure device) 20, a cartridge (process cartridge) 30, a transfer roller 41, and the like. The stacking unit is a component that forms a three-dimensional object having a three-dimensional structure by sequentially stacking and fixing a plurality of layers of material images generated by the image forming unit. The stacking unit is composed of a carrier (supporting member) 42, a heater plate 43, a stage 49, a cleaner (disposal unit) 46, and the like.

(Control part)
The control unit 60 has a function of converting the three-dimensional shape data of the modeling target into modeling slice data, a function of outputting slice data of each layer to the image generation controller 10, a function of managing the layered modeling process, and the like. The control unit 60 can be configured by, for example, mounting a program having these functions on a personal computer or an embedded computer. As the three-dimensional shape data, data created by a three-dimensional CAD, a three-dimensional modeler, a three-dimensional scanner, or the like can be used. The format of the three-dimensional shape data is not limited, but for example, polygon data such as STL (Stereolithography) can be preferably used. As the format of the slice data, for example, multi-value image data (each value represents a material type) or multi-plane image data (each plane corresponds to a material type) can be used.

  The image generation controller 10 has a function of controlling an image forming process in the image forming unit based on slice data input from the control unit 60 and a control signal input from the modeling controller 70. Specifically, the image generation controller 10 performs resolution conversion and decoding processing of slice data, control of the image writing position and timing by the laser scanner 20, and the like. In addition, the image generation controller 10 may have the same function as a printer controller built in a general laser printer (2D printer).

  The modeling controller 70 has a function of performing mechatronic control of the modeling apparatus. The drive system includes a transfer roller motor 47 that rotationally drives the transfer roller 41 and a stage drive motor 48 that moves the stage 49. The sensing system includes a line sensor 44 as an image sensor for detecting a material image and an illumination light source 45 for illuminating the material image. The line sensor 44 and the illumination light source 45 constitute an image acquisition unit. Details of the roles of these sensors and the illumination light source will be described later.

In FIG. 7, an example of the circuit block of the modeling controller 70 is shown. The modeling controller 70 includes a CPU 71, a memory 72, an interface 73, a UI unit 74, a motor drive circuit 75, a motor driver 76, a sensor detection circuit 77, a sensor interface 78, other IO circuits 79, a heater circuit 80, and an IO interface 81. . A transfer roller motor 47 and a stage drive motor 48 are connected to the motor driver 76. The sensor interface 78 is connected to a rotary encoder detection sensor (not shown), a cover open detection switch of the modeling apparatus, a home position sensor of the stage 49, and the like. A heater and a thermocouple in the heater plate 43 are connected to the heater circuit 80. Drive signals for the line sensor 44, the illumination light source 45, the cleaner 46, and the like are connected to the IO interface 81.
In the present embodiment, the control unit 60, the modeling controller 70, and the image generation controller 10 are configured by separate controllers. However, these functions may be configured by a single controller.

(Image forming part)
The image forming unit is a unit that forms a material image for one layer made of a modeling material using, for example, an electrophotographic process. The electrophotographic process is a technique for forming a desired image by a series of processes in which a photoreceptor is charged, a latent image is formed by exposure, and developer particles are attached to the latent image to form an image. In the modeling apparatus, particles made of a modeling material are used instead of the developer, but the basic principle of the electrophotographic process is almost the same as that of the 2D printer.

In FIG. 1A, a photosensitive drum 34 is an image carrier having a photosensitive layer such as an organic photosensitive member or an amorphous silicon photosensitive member. The primary charging roller 33 is a charging device for uniformly charging the photosensitive layer of the photosensitive drum 34. The laser scanner 20 is an exposure device that scans the photosensitive drum 34 with a laser beam and draws a latent image in accordance with an image signal given from the image generation controller 10. The modeling material supply unit 31 is a device that accommodates and supplies the modeling material 35. The developing roller 32 is a developing device that supplies the modeling material 35 to the electrostatic latent image on the photosensitive drum 34. The transfer roller 41 is a transfer device that transfers an image of the modeling material 35 formed on the photosensitive drum 34 to a conveyance body (transfer belt) 42. Although not shown, a cleaning device for cleaning the surface of the photosensitive drum 34 may be provided downstream of the transfer nip between the photosensitive drum 34 and the transfer roller 41. In the present embodiment, the photosensitive drum 34, the primary charging roller 33, the modeling material supply unit 31, and the developing roller 32 are integrated as a drum cartridge 30 and configured to be detachable from the modeling apparatus main body, so that replacement is possible. It has become easier. On the other hand, in order to cope with an increase in capacity of the modeling material, a replenishment configuration in which the exposure / development apparatus unit and the material supply unit are separated may be employed.
The image forming unit is not limited as long as the modeling material can be arranged based on the slice data. In addition to the above-described electrophotographic process method, the image forming unit directly forms an image on the carrier using an inkjet. A method etc. can also be adopted.

(Laminated part)
In FIG. 1A, a conveyance body 42 is a member that carries the material image 50 formed by the image forming unit and transferred from the photosensitive drum 34 and conveys it to the stage 49 (lamination nip). The conveyance body 42 is configured by, for example, an endless belt member such as resin or polyimide. The heater plate 43 has a built-in heater, melts the material image 50 on the transport body 42, and is placed on the laminated surface 49a of the stage 49 or on the upper surface of the modeled object 51 on the stage 49 (on the laminated surface 49a). It has a heat laminating function for laminating. The stage 49 is a member that holds the modeled object 51 that is being stacked, and can be moved in the vertical direction (stacking direction) by the stage drive motor 48.

(Operation of modeling equipment)
Next, a basic operation for producing a modeled object by the modeling apparatus will be described.
In FIG. 1A, the control unit 60 calculates the cross-sectional shape of each layer by slicing the modeling object at a predetermined pitch (for example, a thickness of several microns to several tens of microns) based on the three-dimensional shape data of the modeling object. Generate slice data for each layer. The image data is input to the image generation controller 10 in order from the lowermost slice data. The image generation controller 10 controls laser emission and scanning of the laser scanner 20 according to the input slice data.

In the image forming unit, the surface of the photosensitive drum 34 is uniformly charged by the primary charging roller 33. When the surface of the photosensitive drum 34 is exposed by the laser beam from the laser scanner 20, the exposed portion is neutralized. The modeling material charged with the developing bias is supplied to the charge removal portion by the developing roller 32, and a material image made of the modeling material is formed on the surface of the photosensitive drum 34. The material image 50 is transferred onto the transport body 42 by the transfer roller 41.
The carrier 42 rotates while carrying the material image 50 and conveys the material image 50 to the stacking position. By applying heat with the heater plate 43, the material image 50 is thermally welded to the upper surface of the modeled object 51 on the stage. Each time the material image 50 is laminated, the modeling controller 70 lowers the stage 49 by the thickness of one layer in the vertical direction to prepare for the lamination of the next layer. Here, the stacking position is a position where the material images 50 are stacked. In the configuration of FIG. 1, in the transport body 42, the stacking surface 49 a of the stage 49 or the top surface of the model 51 on the stage 49. The portion sandwiched between the heater plates 43 corresponds to the stacking position.
The modeling object 51 is formed on the stage 49 by repeating the modeling operation including the image forming process for forming the material image 50 and the stacking process for stacking the material images 50 for the number of slice data.

(Explanation of melt lamination of material images)
The process in which the material image 50 is thermally welded and laminated on the upper surface of the modeled object 51 will be schematically described with reference to FIGS.
FIG. 2A shows a state where particles constituting the material image 50 are spread. Generally, the close-packed state on a plane means a hexagonal close-packed state.
FIG. 2B shows a state where the particles are in close contact with each other on the modeled object 51 and the heater plate 43 is in contact with the particle upper surface via the transport body 42. The particles shown in FIG. 2B correspond to the AA ′ cross section of FIG. 2A. In the present embodiment, the particle radius is expressed as “r”. Therefore, the diameter is 2r. Actually, “r” is a length having a certain particle size distribution range, and the average value is treated as “r” here.
FIG. 2C shows a state in which the particles start to melt, begin to fill the gaps between the particles, and the position of the shaped object 51 starts to rise. FIG. 2D shows a state where the melting is completed and the gaps between the particles are filled. At this time, since there is no change in the volume of the entire particle, the material image 50 that has been melted has a plate shape as shown in FIG. 2D. The plate thickness of the material image 50 is 1.6r by simple calculation.
In FIG. 2E, the top view of the material image 50 similar to FIG. 2A is shown in the upper part, and the AA ′ cross section in the plan view is shown in the lower part. In FIG. 2F, the top view of the state in which the melting is completed and the gap is filled is shown in the upper part and the cross section aa ′ in the plan view is shown in the lower part as in FIG.

(Problems of additive manufacturing)
As in this embodiment, in a modeling apparatus of a type that forms a modeled object by stacking material images, a partial defect in the material image affects the quality of the modeled object. For example, when a development coat cannot be formed partially and uniformly in the development process due to a defect in the developing device, streaks or missing coating may occur. Further, when the image of the modeling material 35 is transferred from the photosensitive drum 34 to the conveyance body 42, if the transfer bias is not set appropriately or the modeling material 35 is not sufficiently charged, the transfer failure May cause a defect in the material image 50. When a defect occurs in the material image 50, the flatness of the corresponding portion is remarkably lowered at the time of melting, and it becomes difficult to stack and fix the material image 50 of the next layer on the shaped object 51 on the stage 49. Such an image defect can be said to be a problem peculiar to the modeling apparatus.

(Description of material image defect generation)
A process in which a partial defect image is generated in the material image 50 will be schematically described with reference to FIGS.
3A and 3B are schematic diagrams for explaining a case where one particle of the modeling material constituting the material image 50 is defective. FIG. 3A shows a state before melting, and FIG. 3B shows a state after melting. . In FIGS. 3A and 3B, a plan view is shown in the upper part, and a cross-section AA ′ and aa ′ cross-section in the plan view are shown in the lower part. In FIG. 3B, the defect state for one particle after melting is shown. However, since the flow of molecules actually occurs, the boundary between the defect part and the non-defect part is not vertical as shown in the figure. It seems to be inclined to.
3C and 3D are schematic views for explaining a case where one particle of the material image 50 is defective as in FIGS. 3A and 3B. FIG. 3D schematically shows a state where it is assumed that the upper surfaces of some of the particles around the defect portion are uniformly lowered and the defect portion is filled during melting. At this time, in FIG. 3D, the stacking thickness of the defective portion and a part of the particles around the defective portion is 1.28r in calculation. Also in this case, there is no change in the total volume of the entire particles between the state shown in FIG. 3C and the state shown in FIG. 3D. As in FIG. 3B, since the molecular flow actually occurs, the part including the defect part and a part of the particle around the defect part and the part not affected by the defect of the particle around the defect part, The boundary seems not to be vertical as shown, but to be gently inclined.

Next, FIG. 3E shows a case where the cross-sectional shape of the boundary 53 shown in FIGS. 3B and 3D is gently inclined. FIG. 3E shows a state in which, when there is a defect of one particle in the N layer at the lower part, a part of the defect part and particles around the defect part flow and incline toward the defect center. In the upper part, when the N + 1 layer as the next layer is in contact with the N layer, and the N + 2 layer as the next layer are stacked almost normally.
If the material is, for example, a complete liquid (for example, milk), the upper surface is uniformly flat throughout the entire layer, but if the material remains solid (for example, processed cheese), the defective portion is maintained as it is. . When the resin melts, because of the intermediate state (for example, yogurt), FIG. 3E schematically illustrates a state where only the vicinity of the defect portion is complemented by being inclined toward the defect portion.
As described above, in the case of a single particle defect, a part of the particles around the defect portion flows, so that the defect portion is naturally repaired and normal lamination can be performed from the third layer.

In the above description, the form in which the defective portion can be normally repaired has been described. However, when the defect range becomes large, the repair may be difficult. This point will be described with reference to FIGS. 3F and 3G.
3F and 3G show schematic arrangements when seven particles are defective. FIG. 3F shows a state before melting, and FIG. 3G shows a state after melting. In FIGS. 3F and 3G, a plan view is shown in the upper part, and an AA ′ cross section and an aa ′ cross section in the plan view are shown in the lower part.
When the particles shown in FIG. 3F are melted, the peripheral particles around the defect part flow toward the center, and the state shown in FIG. 3G is reached. In FIG. 3G, even if the particles are melted, they do not reach a complete liquid state, and even if the material flows, the defective portion is not completely filled, so that an inclined depression is formed. As shown in FIG. 3G, in the N-th layer where the defect portion exists, the defect portion is not completely filled and a portion having no material is generated. Then, by laminating the (N + 1) th layer, the defect portion of the Nth layer is somewhat supplemented, but it is not sufficient, and when the (N + 2) th layer is laminated, the particles of the (N + 2) th layer cannot be brought into close contact with each other. For this reason, it is difficult to continue the lamination.

As described above, an example of the defect that makes it difficult to continue the lamination has been described. In addition to the above example, there are various cases of this phenomenon depending on the viscoelastic characteristics of the modeling material and the melting temperature condition.
Here, we do not mention the number of particles that will be the criterion for determining whether or not it is a defect (the standard for stacking, which will be described later), but verify the shape and number of particles that make it difficult to continue stacking for each modeling material. Just decide. Further, the specification of the sensor resolution may be determined in accordance with the shape and area determination criteria determined for detecting the material image.

Next, a method for detecting the material image of each layer will be described.
First, details of the illumination light source will be described.
(Material image by bright field illumination)
4A and 4B show a state in which the thin material image 50 is transferred onto the carrier 42. Since the material image 50 is used for stacking, the stacking thickness is set according to the specifications of the modeling apparatus. Here, for example, the range of the laminated thickness of the material image 50 is set to 10 μm or more for high-precision lamination and 100 μm or less for high-speed lamination. Since the material images 50 of any laminated thickness are in a thin layer state, when the modeling material is a resin, the transmittance becomes high, and it becomes difficult to obtain contrast with the conveyance body 42.
FIG. 4A shows a state in which normal visible light is irradiated at an angle close to perpendicular to the surface of the material image, and reflected light is incident on the sensor. This is called bright field illumination. As shown in the drawing, the contrast between the material image 50 and the carrier 42 is quite small because the reflected light component from the carrier 42 due to the transmitted light is large. When the modeling material is colored in black, the contrast becomes smaller. In order to obtain a good contrast, it is necessary to increase the thickness of the material image 50. However, increasing the thickness of the material image 50 in order to increase the modeling accuracy causes a contradiction.

(Sensing of material images by near infrared illumination)
With reference to FIG. 4B, a method for grasping the shape of the material image 50 from the difference in light absorbance of the material in the carrier 42 and the material image 50 when irradiated with near-infrared light will be described. In FIG. 4B, near-infrared light is irradiated by the illumination light source 45 at an angle close to the surface of the carrier 42 and the material image 50, and the reflected light is used as a line sensor 44 in InGaAs (indium gallium arsenide). It shows about the form which injects into a sensor. Here, the illumination light source 45 irradiates near infrared light to a region on the carrier 42 (on the carrier) including the region carrying the material image 50.
Generally, near-infrared light is a kind of electromagnetic wave having a wavelength region (range) of 800 to 2500 nm, and is light that cannot be directly seen by humans because it has a longer wavelength than visible light. In addition, near infrared light is most easily absorbed by an object. Therefore, when light including near infrared light is applied to the carrier 42 and the material image 50, light reflection is caused by the difference in the substance of the carrier 42 and the material image 50. And the difference in absorbing characteristics is displayed as an image, and the carrier 42 and the material image 50 can be distinguished. Therefore, by using this illumination method, it is possible to easily perform image detection of the thin layer resin or the dark colored resin used in the present embodiment.

(Illumination optics)
Next, the illumination optical system and sensor arrangement using the illumination including near infrared light will be described.
FIG. 5 shows a state in which the material image 50 is transferred onto the conveyance body 42.
The illumination light source 45 is configured such that uniform parallel light irradiation is possible, for example, by passing a cylindrical lens and a diffusion plate through an LED array. And the reflected light when the illumination light source 45 irradiates the surface of the material image 50 injects into the sensor part 44a via the filter 44c and the lens 44b. In this way, the material image 50 is detected.
The illumination light source 45 and the line sensor 44 can acquire the material image 50 on the transport body 42 as a two-dimensional image by moving relative to the transport body 42. In this embodiment, for example, if one particle has a resolution of 600 dpi (dots per inch), the particle diameter “2r” is 42 μm. Therefore, in order to discriminate a single particle defect, a sensor resolution of at least 21 μm is desirable.
As will be described later, a filter 44c that filters reflected light incident on the line sensor 44 with a predetermined wavelength band as a pass band is selected and installed based on the absorbance data in the carrier 42 and the material image 50. The At this time, it is more preferable that the filter has a pass band in a wavelength region where the difference in absorbance between the carrier 42 and the material image 50 is maximum. However, the present invention is not limited to this, and any filter may be used as long as the absorbance is filtered using different wavelength bands between the carrier 42 and the material image 50 as a pass band. In this case, the filter 44c can be determined by using the wavelength region that represents the feature by the absorbance of the modeling material constituting the material image 50. For example, there is a peak in the wavelength region of 1.68 μm for ABS resin and 1.72 μm for PP resin. A filter having such a peak wavelength band as a pass band may be determined, or the type of modeling material is specified from the absorbance peak, and the filter is determined according to the specified type of modeling material. Also good.

(Defect form of material image)
Next, various defect forms of the material image detected using the illumination optical system will be described.
6A shows a defect shape of the material image 50, and FIG. 6B shows acquired data when the material image 50 of FIG. 6A is detected.
In FIG. 6A, the position when the material image 50a is formed without deformation based on the slice data with respect to the material image 50a on the transport body 42 is indicated by a broken line 50b, and a defective portion (missing portion) of the material image 50a is shown. ) Is indicated by 50c. The defect portion 50c is shown as a star for convenience of explanation.
The carrier 42 moves from the top to the bottom in the drawing according to the arrow direction shown in FIG. 6A. BB ′ shown in FIG. 6A is the sense line position of the image, and the line sensor output voltage waveform detected at this position is shown in FIG. 6B. The position of BB ′ is a position on the surface of the transport body 42 and is orthogonal to the traveling direction of the transport body 42.
As described above, an InGaAs sensor is used as the line sensor 44, and the analog output signal is binarized through a noise removal filter, shading correction, defect edge enhancement processing, and the like. Accordingly, the final output voltage is digitized, and V0 (for example, 0 V) is output when there is no reflected light amount, and V1 (for example, +5 V) is output when there is a reflected light amount. The defective part has an output voltage of V0.

Next, defect forms other than the defective part will be described.
In FIG. 6A, the material image 50a is in an expanded / contracted state with respect to the broken line 50b. A portion where the material image 50a protrudes from the entire line with respect to the broken line 50b is an expansion defect, and a portion where the entire material line is recessed inward is a contraction defect. If there is an uneven defect in a part of the line, it is expressed as an edge defect 50d or an edge protrusion 50e. Although not particularly shown here, distortion of the entire image can also be detected as a defect state.
Such a defect form can be used as a criterion for determining whether or not the defect is a defect (a stackability criterion described later).

(Defect image acquisition and recovery control 1)
A mechanism for detecting and recovering each defect form of the material image will be described.
FIG. 7 shows the modeling controller 70 described above. The illumination light source 45, the line sensor 44, and the cleaner 46 are connected to the IO interface 81 and drive signals are output. Here, the handling of the output signal of the line sensor 44 will be described in detail.

FIG. 8 is a configuration block diagram of defect image acquisition and recovery control.
As shown in FIG. 8, the control unit 60 includes a 3D data slicer 61 and a printer driver 62 as functions related to generation of slice data. Shape data such as STL is input to the 3D data slicer 61 as 3D shape data by 3D CAD or the like. In accordance with the input data, slice data for each layer is generated by the 3D data slicer 61. The slice data is output from the printer driver 62 to the image generation controller 10. The control unit 60 and the image control unit 11 are connected with a control signal and image data by an interface signal line.
The image control unit 11 has a storage function, accumulates slice data, causes the slice image generation unit 12 to generate a material image based on the slice data according to the stacking timing, and transfers the material image to the stacking unit. The stacking unit includes a laser scanner 20, a drum cartridge 30, a transfer stacking unit 40, and the like. Further, a comparison unit (comparison means, determination means) 13 is connected to the image control unit 11 in the image generation controller 10. The comparison unit 13 compares the image acquired by the line sensor 44 with the slice data.

(Image comparison algorithm)
FIG. 9 is a schematic diagram for explaining the function of the comparison unit 13.
In FIG. 9, a heart-shaped cosmetic box-like shape is used as a three-dimensional model. In the left grid-like image in FIG. 9, the upper part shows slice data, and the lower part shows material image acquisition data acquired by the line sensor 44. In FIG. 9, the comparison result by the comparison unit 13 is shown on the right side. One-dimensional data of the line N among the data is input to the comparison unit 13.
Here, let the acquisition data of the material image typically shown by a black pixel in FIG. 9 be a defective part. The lower line data of the comparison unit 13 indicates the comparison result, and the x part indicates that they do not match. If the comparison result is a mismatch determination, it is determined whether it is a defect or a non-defect. If the stacking cannot be continued, it is determined as a defect. The above is an example performed by comparison of raw data, but if it is a one-dimensional binary comparison, a comparison by run length can be easily performed. Further, the present invention is not limited to line units, and several lines may be combined to perform block unit comparison. Below, an example of a defect detection algorithm is demonstrated according to a flowchart.

(Flowchart 1 of defect image acquisition and recovery processing)
The defect detection algorithm in units of layers will be described along the flowchart shown in FIG. 10A.
In step 101, slice data is input and stored in the image generation controller 10.
In step 102, the slice data is developed into a raster image, and a material image is generated from the particle image. In step 103, the material image is detected and acquired by the line sensor 44. In step 104, the image acquired by the line sensor 44 is compared with the accumulated slice data. In step 105, it is determined whether the comparison result matches or does not match. If it is determined in step 105 that the values match, the process proceeds to step 106, and the process proceeds to the stacking process.

(Image defect judgment 1)
In step 107, it is determined whether or not the mismatched portion is within the stacking standard. If it is determined in step 107 that the inconsistent portion is within the stackable reference, the process proceeds to step 106 and proceeds to the stacking process. If it is determined in step 107 that the mismatched portion is not within the stackable standard, the process proceeds to step 108 and the corresponding material image is discarded.
Then, it returns to step 102 and modeling is performed by repeating the above process.

Here, a method of detecting and acquiring the material image in step 103 with the line sensor 44 will be described along the flowchart shown in FIG. 10B.
In step 103a, the spectrophotometer 85 is used to detect the absorbance data of the carrier 42 and the material image 50, that is, the relationship between the absorbance of the carrier 42 and the material image 50 and the near infrared region (near infrared region). get. As a relationship between the absorbance of the carrier 42 and the material image 50 and the near-infrared region, a relationship as shown in FIG. 13 can be exemplified in the second embodiment to be described later.
In step 103b, a wavelength region in which the difference in absorbance between the carrier 42 and the material image 50 is maximized is calculated.

In step 103c, a filter 44c that filters reflected light incident on the line sensor 44 is determined using the calculated wavelength band as a pass band, and is mounted at a predetermined position. The line sensor 44 is configured by mounting the filter 44c. The filter 44c may be attached by the user, but is not limited thereto. An apparatus for automatically selecting and mounting a filter having a plurality of filters having different pass bands and having the pass band in the wavelength band calculated in step 103b may be provided.
In step 103d, information regarding the shape of the material image 50 is acquired using the line sensor 44 and the illumination light source 45 to which the filter 44c is attached in step 103c.
Steps 103 a and 103 b are filter selection steps for clarifying the contrast between the transport body 42 and the material image 50. Therefore, this step can be omitted when the wavelength region in which the difference in absorbance data between the carrier 42 and the material image 50 is maximum is known in advance, or when an optimum filter has been determined.

As described above, it is possible to obtain the shape of the mismatched part by repeatedly sensing the image of one layer in line units and comparing it with the original slice data, thereby determining the shape and size of the mismatched part. By comparing with the reference, it can be determined whether there is a defect. In the case of defect determination, the material image of the corresponding layer can be discarded, and the material image determined to be defective can be re-formed based on the corresponding slice data. When forming a defect-determined material image, slice data may be read again if it remains in a memory or the like provided in the storage or apparatus, and slice data may be regenerated if it does not remain in the memory.
In the present embodiment, the cleaner 46 is disposed as a discarding unit that discards the material image, and the material image on the transport body 42 is removed by an instruction from the modeling controller 70 when determining the defect. As the cleaner 46, a scraper or an adhesive transfer removing roller for stripping off the material on the plane may be used, and it may be appropriately selected according to the characteristics of the conveying body 42 and the modeling material.

In the above description, the material images to be discarded have been described as one layer. On the other hand, when high-speed lamination is performed, the material image of the next layer may be generated when the defect determination of the material image is performed. In such a case, when discarding the material image, it is preferable to discard all the material images that have been transferred to the carrier 42 or that are in progress. Then, a material image is re-formed based on the corresponding slice data for each of the discarded material images.
As another method, the material image on the transport body 42 is not discarded, but only the material image of the layer determined to be defective is discarded, and the material image of the transferred next layer is stacked on the transport body 42. Alternatively, it can be kept on the carrier 42. In that case, the re-formed material image is transferred to the transport body 42 in a state where the normal material image of the next layer is positioned on the transport body 42 upstream of the transfer nip in the transport direction of the transport body 42. Then, it is good to continue the lamination.

  On the other hand, the material image of the next layer may be the same image as the waste material image. In this case, only the material image of the layer determined to be defective is discarded, the generated material image of the next layer is not discarded, and the material image of the next layer is reproduced instead of the material image of the layer determined to be defective. To add. Also, for example, if the modeling object is a cylinder, only the material image of the layer determined to be defective is discarded, the generated material image of the next layer is not discarded, and the layer number is subtracted from the material image. It is preferable to continue the stacking by starting from the subsequent layer number. Thereby, the grade which impairs a lamination speed and lamination time can be eased.

(Flowchart 2 of defect image acquisition and recovery processing)
Next, the flowchart 2 will be described with reference to FIG. In the flowchart 1 shown in FIG. 10, the defect detection algorithm in units of layers has been described. In the flowchart 2, the defect detection algorithm in units of lines will be described.
Steps 201 to 205 are almost the same as steps 101 to 105 in the flowchart 1. In step 202, the slice data is developed into a raster image to generate a particle image. In step 203, a particle image is detected and acquired by a sensor. In step 206, it is determined whether or not the detection has been completed up to the last line of the material image. If not, the process returns to step 203. When the detection is completed up to the final line, the process proceeds to step 207, where the process proceeds to the stacking process.

(Image defect judgment 2)
In step 205, if there is a mismatch, the process proceeds to step 208. In step 208, the mismatch line data is stored. Thereafter, in step 209, it is determined whether or not the detection has been completed up to the final line of the material image. If it is not the final line, the process returns to step 203, and if it is determined that the detection has been completed up to the final line, the process proceeds to step 210. In step 210, it is determined whether or not the composite image of the mismatched portion of the detection line is within the stackable reference. If the composite image of the mismatched portions of the detection lines is within the stackable reference, the process proceeds to step 207, and the process proceeds to the stacking process in step 207. In the case of negative determination in step 210, the process proceeds to step 211. After the corresponding material image is discarded in step 211, the process returns to step 202.

According to the configuration of the modeling apparatus of the present embodiment described above, it is possible to continue modeling without causing a stacking failure even when a defect occurs in the material image. Therefore, the quality of the modeled object can be improved.
Here, in the present embodiment, the image defect when the material image is generated from the slice data has been described, but the present invention is not limited to this. Even when markers that are formed at the leading edge position (including the side edge position and the trailing edge position) of the material image and markers that correspond to the purpose are generated with the material image, the material image can be detected to achieve each purpose. The present invention can be applied. Depending on the purpose of the marker, the marker is formed at the time of modeling a modeled object by adding the marker data to the slice data generated from the three-dimensional shape data of the modeled object when the model is formed. There is a case. Such a marker may be one in which a marker processing unit included in the control unit 60 performs processing to be formed by the image forming unit, transferred to the transport body 42, and transported.
In addition, as described above, the material image is formed by combining one or a plurality of material images according to the type of modeling material to be used. That is, the present invention can be suitably applied even when modeling is performed using a plurality of modeling materials.
Moreover, in this embodiment, the image acquisition means which has the near infrared illumination which irradiates light to a conveyance body, and the image sensor which light-receives (photographs) the reflected light of the modeling material on a conveyance body is provided. Thus, even if the material image of each layer is a thin layer and has high transmittance or low contrast, the shape of the material image or particle image can be recognized. For this reason, this invention can be applied to the modeling apparatus in general which models a layered material image, and can be utilized for many uses besides the defect detection of a material image. For example, the present invention can be used also when detecting a residue such as a modeling material remaining on the transport body 42 or when detecting a cleaning failure after cleaning the transport body 42.
Moreover, in this embodiment, the form which grasps | ascertains the shape of the material image 50 from the difference in the light absorbency of the material by the conveyance body 42 and the material image 50 at the time of irradiating near infrared light was demonstrated. On the other hand, you may use the difference in the reflectance of the light by the material in the conveyance body 42 and the material image 50 at the time of irradiating near infrared light. In addition, if the transport body 42 is configured to transmit near infrared light, the difference in light transmittance between materials in the transport body 42 and the material image 50 when irradiated with near infrared light. May be used. That is, any material that can grasp the shape of the material image 50 from the difference in the degree of light absorption, reflection, and transmission by the material in the carrier 42 and the material image 50 when irradiated with near infrared light. .

Second Embodiment
The second embodiment will be described below. In the following description, configurations and processes different from those of the first embodiment will be described, and descriptions of configurations and processes similar to those of the first embodiment will be omitted.
(Configuration of modeling equipment)
The modeling apparatus of this embodiment has a function of calculating the thickness of the material image 50 from the absorbance characteristics of the material image 50 obtained using the spectrophotometer 85 and moving the stage 49 based on the thickness data.
The process of calculating the thickness of the material image 50 from the absorbance data from the spectrophotometer 85 is mainly performed by the material thickness detection unit 15 of the image generation controller 10 in the control unit. Further, the process of moving the stage 49 in the vertical direction is performed mainly by the modeling controller 70 controlling the stage drive motor 48.

(Material image thickness detection and stage movement control)
Below, control of the material image thickness detection and stage movement of the modeling apparatus which concerns on this embodiment is demonstrated. The difference of this embodiment from the first embodiment is that the thickness of the material image is detected by the material thickness detection unit 15 and the stage moves in accordance with the detected thickness.
FIG. 12 is a control block diagram of material image thickness detection and stage movement of the modeling apparatus according to the present embodiment. FIG. 13 is a diagram for explaining the relationship between the absorbance data and the material thickness of the present embodiment, and a thin layer 91 made of an ABS resin material, a thick layer 92 made of an ABS resin material, and a carrier (base material). 42 shows the actually measured absorbance data. In the present embodiment, the thick layer 92 has a thickness corresponding to about 10 layers of the thin layer 91.
As shown in FIG. 13, a difference of about 0.5 in the absorbance value can be confirmed between the thin layer 91 and the thick layer 92. As described above, the absorbance of the material image 50 varies depending on the thickness.
Such a relationship between the thickness of the material image and the absorbance data in the material image and / or the difference between the maximum value and the minimum value of the absorbance value in the material image (hereinafter referred to as the absorbance characteristics for each thickness) It calculates | requires beforehand for every kind of material, and is previously memorize | stored in the modeling apparatus. In addition, when the type of modeling material to be used is specified by the specifications of the modeling apparatus, the thickness, the absorbance data, and / or the maximum value and the minimum value of the absorbance value are related to that type of modeling material. What is necessary is just to memorize | store the relationship with a difference.

The material image thickness detection and stage movement control will be described with reference to FIG.
In the present embodiment, the spectrophotometer 85 is used to acquire the absorbance data of the material image 50 on the carrier 42. The acquired absorbance data is taken into the material thickness detection unit 15. The material thickness detection unit 15 calculates the difference between the maximum value and the minimum value of the absorbance value in the near infrared region (900 to 2400 nm). Furthermore, the material thickness detection unit 15 determines the material image 50 based on the calculated difference, the material type used in the material image 50 (for example, ABS resin material), and the relationship stored in advance in the modeling apparatus. The thickness is detected (determined).
The detected thickness of the material image 50 is converted into a stage movement amount and transmitted to the modeling controller 70. The modeling controller 70 controls the stage drive motor 48 to move the stage 49 according to the received stage movement amount. Thereby, in the lamination position, the distance between the lamination surface 49a of the stage 49 or the upper surface of the model 51 on the stage 49 and the surface of the transport body 42 is the same length as the thickness of the detected material image 50. Become.

(Flow chart of material image thickness detection and stage movement control)
The material image thickness detection and stage movement control will be described along the flowchart of FIG.
In step 301, the spectrophotometer 85 is used to detect the absorbance data of the material image on the carrier 42.
In step 302, the material thickness detection unit 15 extracts the difference between the maximum value and the minimum value of absorbance.
In step 303, the actual thickness of the material image is calculated from the extracted difference value, the type of material image, and the absorbance characteristics for each thickness.
In step 304, the moving amount of the stage 49 is calculated from the calculated thickness (acquisition result) of the material image.
In step 305, the stage 49 is moved according to the calculated movement amount.
In step 306, the process proceeds to the lamination process.
Regarding the movement of the stage 49, in this embodiment, for each material image of one layer, the spectrophotometer 85 is used to detect the absorbance data and execute the stage movement control, but the present invention is not limited to this. When the modeling operation is started, the absorbance data is detected only for the material image of the first layer, the thickness of the material image is calculated, the thickness is set as the thickness of the material image related to the modeling operation, Until the modeling operation is finished, the movement of the stage may be controlled based on the thickness.

  According to the present embodiment, the thickness of the material image 50 can be detected using near-infrared light, and the movement control of the stage 49 can be performed by using the detected thickness of the material image 50. Become. Accordingly, it is not necessary to install a dedicated displacement sensor or the like in order to detect the thickness of the material image 50, so that the cost and size can be reduced.

DESCRIPTION OF SYMBOLS 1 ... Modeling apparatus, 34 ... Photosensitive drum, 42 ... Conveyance body, 44 ... Line sensor, 45 ... Illumination light source, 49 ... Stage, 50 ... Material image

Claims (14)

A modeling apparatus for producing a three-dimensional object by laminating modeling materials on a stage based on three-dimensional shape data of a three-dimensional model,
An image forming unit that forms a material image by arranging the modeling material based on slice data generated from the three-dimensional shape data;
A carrier for carrying the material image formed by the image forming means;
Illumination for irradiating the region on the carrier including the region carrying the material image with near-infrared light, and receiving the near-infrared light reflected by the material image and the carrier An image acquisition means having an image sensor;
A modeling apparatus comprising: The image acquisition means distinguishes between the carrier and the material image using the fact that the absorbance or reflectance with respect to the near-infrared light is different between the carrier and the material image, and provides information on the material image. The modeling apparatus according to claim 1, wherein the modeling apparatus is acquired. 3. The modeling apparatus according to claim 2, further comprising a filter that filters the reflected light using a first wavelength range in which the absorbance or the reflectance is different between the carrier and the material image as a pass band. The modeling apparatus according to claim 3, wherein the first wavelength region is a wavelength region in which the difference in absorbance or reflectance between the carrier and the material image is maximized. 5. The calculation device according to claim 3, further comprising a calculation unit that acquires a relationship between the absorbance or the reflectance of the carrier and the material image and a near-infrared region to obtain the first wavelength region. Modeling equipment. Comparison means for comparing information on the material image acquired by the image acquisition means with slice data that is the original data of the material image;
A determination unit that determines whether the material image is a material image that can be stacked based on a comparison result by the comparison unit;
The modeling apparatus according to claim 2, wherein the modeling apparatus includes: Obtain the relationship between the near-infrared region and the absorbance or reflectance of the material image with respect to the near-infrared light, determine the difference between the maximum and minimum values of the absorbance or reflectance of the material image, from the difference Thickness acquisition means for acquiring information on the thickness of the material image;
The modeling apparatus according to claim 1, wherein the modeling apparatus includes: A stage for laminating the material images carried on the carrier;
Moving means for moving the stage in the stacking direction of the material images;
Have
The moving means moves the stage to a position for laminating the material image on the carrier on the stage or a three-dimensional object on the stage based on an acquisition result by the thickness acquisition means. The modeling apparatus according to claim 7. A modeling apparatus for producing a three-dimensional object by laminating modeling materials on a stage based on three-dimensional shape data of a three-dimensional model,
An image forming unit that forms a material image by arranging the modeling material based on slice data generated from the three-dimensional shape data;
A carrier for carrying the material image formed by the image forming means;
Illumination that irradiates near infrared light to an area on the carrier including an area carrying the material image;
In a modeling method by a modeling apparatus having
Receiving, with an image sensor, the material image irradiated by the illumination and the reflected light of the near-infrared light from the carrier;
Using the fact that the absorbance or reflectance for the near-infrared light is different between the carrier and the material image, distinguishing the carrier and the material image, and obtaining information about the material image;
A modeling method comprising: The modeling apparatus is
A stage for laminating the material images carried on the carrier;
Moving means for moving the stage in the stacking direction of the material images;
Have
Obtain the relationship between the near-infrared region and the absorbance or reflectance of the material image with respect to the near-infrared light, determine the difference between the maximum and minimum values of the absorbance or reflectance of the material image, from the difference An obtaining step for obtaining information on the thickness of the material image;
A step of moving the stage by the moving means to a position for laminating the material image on the carrier on the stage or a three-dimensional object on the stage based on the acquisition result of the acquisition step;
The modeling method according to claim 9, further comprising: A manufacturing method of a three-dimensional object for producing a three-dimensional object by sequentially stacking modeling materials based on slice data of a three-dimensional model,
Arranging the modeling material on a carrier based on the slice data to form a material layer;
Irradiating the material layer with light containing near-infrared light to obtain near-infrared reflected light from the material layer and the carrier; and
Obtaining information on the material layer from the reflected light in the near infrared region;
The manufacturing method of the solid thing characterized by including.   The method for manufacturing a three-dimensional object according to claim 11, wherein the information is information relating to an arrangement of the material layer.   The method for manufacturing a three-dimensional object according to claim 11, further comprising a step of comparing the acquired information on the arrangement of the material layer and the slice data to determine whether the material layer can be laminated. The method for producing a three-dimensional object according to claim 13, comprising a step of laminating material layers determined to be capable of being laminated.