JP2018047606A - Molding apparatus, and molding method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a technique capable of continuing molding without generating lamination malfunctions even if a defect part occurs in a material image to improve the quality of a stereo article.SOLUTION: The molding apparatus for producing a stereo article by laminating a molding material on the basis of three-dimensional shape data of a stereo model includes: an image-forming part for forming a material image by disposing the molding material on the basis of slice data generated from the three-dimensional shape data; a transporting body transferred with the material image formed at the image forming part and transporting the material image; a laminating part for laminating the material image; an image acquisition part having a dark field illumination which irradiates the material image with a light, and an image sensor which receives a scattered light of the light by the molding material.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.

JP 2003-053846 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 a transfer failure when the particle image is formed and when the material image is transferred to the transport 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.

  The present invention has been made in view of the above-described circumstances, and even when a defective portion is generated in a material image, modeling can be continued without causing poor stacking and the quality of a three-dimensional object can be improved. The purpose is to provide technology.

The first aspect of the present invention is:
A modeling device that creates a three-dimensional object by stacking modeling materials based on the 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;
The material image formed by the image forming unit is transferred, and a transport body that transports the material image;
A laminating portion for laminating the material images;
Dark field illumination that irradiates light on the material image, and an image acquisition unit that includes an image sensor that receives the scattered light of the light from the modeling material;
A modeling apparatus is provided.

The second aspect of the present invention is:
A modeling method by a modeling device that creates a three-dimensional object by laminating modeling materials based on the three-dimensional shape data of a three-dimensional model,
The modeling apparatus is
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;
The material image formed by the image forming unit is transferred, and a transport body that transports the material image;
A stacking unit for stacking the material images transported by the transport body;
An image acquisition unit for acquiring information on the material image on the carrier;
A discarding unit for discarding the material image on the carrier;
Have
A comparison step for comparing information of the material of interest image acquired by the image acquisition unit and the slice data of interest which is the original data of the material of interest image;
A determination step of determining whether the material image of interest is a material image that can be laminated based on the comparison result of the comparison step;
When a negative determination is made in the determination step, a control step of discarding the target material image by the discard unit and re-forming the target material image by the image forming unit based on the target slice data;
The modeling method characterized by including this is provided.

The third aspect of the present invention is:
A modeling method for manufacturing a three-dimensional object by stacking modeling materials based on the three-dimensional shape data of a three-dimensional model,
An image forming step of forming a material image by arranging a modeling material based on slice data generated from the three-dimensional shape data;
An information acquisition step of irradiating the material image with light and acquiring information of the material image from the irregularly reflected light;
A comparison step of comparing the material image information with the slice data;
A determination step of determining whether or not the material image can be stacked based on a comparison result of the comparison step;
An image re-forming step of discarding the material image for which a negative determination has been made in the determining step, and re-forming the material image based on the slice data;
The modeling method characterized by including this is provided.

  ADVANTAGE OF THE INVENTION According to this invention, even when a defective part arises in a material image, modeling can be continued without producing stacking fault, and the technique which can improve the quality of a solid object can be provided.

The figure which shows typically the structure of the modeling apparatus which concerns on 1st Embodiment. The figure which shows typically the fusion | melting lamination | stacking of a material image. The figure which shows typically the defect image generation of a material image. The figure for demonstrating a bright field and a dark field. The schematic diagram which shows the illumination optical system for acquisition of a material image. The figure which shows the defect form image of a material image. The block diagram of a modeling controller. The block diagram of the control 1 of image defect acquisition and recovery. The figure explaining the defect image comparison algorithm of a control block diagram. The flowchart 1 of the defect image acquisition of a control block diagram, and a recovery process. The flowchart 2 of the defect image acquisition of a control block diagram, and a recovery process. The figure which shows typically the structure of the modeling apparatus which concerns on 2nd Embodiment. The block diagram of control 2 of image defect acquisition and recovery. The flowchart 3 of the defect image acquisition of a control block diagram, and a recovery process.

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 an additive manufacturing technique (AM technique), that is, an apparatus for forming a three-dimensional object (three-dimensional object) by two-dimensionally arranging modeling materials and laminating them in layers.
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 (image for one layer) formed of a modeling material based on slice data is referred to as a “material image”. A “material image” is a layer of particles formed by combining one or more material images 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 excluding the support body 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, 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 includes a transport body 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 by the heater plate 43, the material image 50 is thermally welded to the upper surface of the modeled object 51 on the stage 49. 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.
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.
(Dark field and bright field)
4A to 4C show a state in which the material image 50 of the thin layer is transferred onto the conveyance body 42. FIG. 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 material surface 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 dark field illumination)
A countermeasure for increasing the contrast with the carrier 42 will be described with reference to FIGS. 4B and 4C.
In the present embodiment, as shown in FIGS. 4B and 4C, the illumination light source is irradiated at a shallow angle with respect to the material surface, so that scattered light is incident on the sensor. This is called dark field illumination. This irradiation angle is desirably 45 ° or less. This is called the Brewster angle. In general, dark field illumination is used for detecting minute objects in thin layer pieces with a microscope or the like. Also, white is often used as the wavelength of the light source, but a blue light source is more effective in order to improve the scattering characteristics. The wavelength is 470 to 405 nm or the like.
FIG. 4B shows the direction of reflected light when illumination is performed with the surface of the material image 50 being smooth. In the form of FIG. 4B, since the surface of the conveyance body 42 is configured to be smooth so that the surface of the material image 50 is smooth, scattered light does not enter the sensor, but is scattered at the edge portion of the material image 50. It enters the sensor as light.
On the other hand, FIG. 4C shows the direction of reflected light when illumination is performed with the surface of the material image 50 being a rough surface. In the form of FIG. 4C, the reflected light on the entire surface of the material image 50 becomes scattered light and enters the sensor.
By using this illumination method, it is possible to easily detect a material image composed 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 dark field illumination will be described.
FIG. 5 is a schematic diagram showing a mode in which the material image 50 is transferred onto the carrier 42 and the illumination light source 45 irradiates the surface of the material image 50 at an angle of 45 ° or less with respect to the surface of the material image 50. It is.
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 scattered light when the illumination light source 45 irradiates the surface of the material image 50 is comprised so that it may inject into the line sensor 44a via the lens 44b. In this way, the material image 50 is detected.
The illumination light source 45 and the line sensor 44a 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. In the reduction optical system, a CMOS sensor chip having a light receiving portion of 7 μm square can be used if a 21 μm pixel can be captured by a 3 × zoom lens. This is equivalent to a sensor chip equivalent to 1200 dpi and is easily available because it has the same specifications as those used in industrial applications (scanners and copiers).

(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 a line sensor, a CCD or CMOS is a common device, and of course, an 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 unit, determination unit) 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 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)
A defect detection algorithm for each layer will be described with reference to the flowchart shown in FIG.
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 (material image of interest) 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 they match, the process proceeds to step 106 and proceeds to the lamination process.
If it is determined in step 105 that they do not match (determination result is negative), the process proceeds to step 107.

((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.

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 (target 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 (target 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 part which has the dark field illumination which irradiates light to a conveyance body, and the image sensor which light-receives (photographs) the scattered light of the light by 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.

Second Embodiment
(Configuration of modeling equipment)
With reference to FIG. 12A and 12B, the structure of the modeling apparatus 100 which concerns on 2nd Embodiment is demonstrated. FIG. 12A is a diagram schematically illustrating a configuration of the modeling apparatus 100 according to the present embodiment. FIG. 12B is a diagram schematically illustrating an enlarged configuration of the material image detection portion. FIG. 13 is a configuration block diagram of defect image acquisition and recovery control of the modeling apparatus 100 according to the present embodiment.
This embodiment differs from the first embodiment in that the area sensor 52 is used as the material image detection means, and the comparison processing of the acquired image is fed back to the control unit 60 and performed by software processing. is there. In the present embodiment, the image detection by the area sensor 52 is performed in a state where the transport body 42 is stopped. Moreover, the illumination light source (not shown) which illuminates the material image at the time of image detection by the area sensor 52 only needs to be able to illuminate the entire material image. In the present embodiment, configurations and processes different from those in the first embodiment will be described, and descriptions of configurations and processes similar to those in the first embodiment will be omitted.

(Defect image acquisition and recovery control 2)
A mechanism for detecting and recovering each defect form in the material image will be described below with reference to FIG.
In the present embodiment, an area sensor 52 is connected to the control unit 60 as means for detecting an image defect in the transfer lamination unit 40. The area sensor 52 may be connected to a control unit 60 (such as a personal computer) via an interface such as a camera link via a grabber board. When the material images are successively stacked, the image detection by the area sensor 52 is performed by the transport body 42 for transporting the preceding material image to the stacking position and applying heat by the heater plate 43 to stack the material images. Perform at the timing of stopping. The image detection may be performed at a position where the area sensor 52 faces the stop position of the corresponding material image. In the present embodiment, the control unit 60 includes a comparison unit 13 that compares the image acquired by the area sensor 52 with slice data. The comparison unit 13 is connected to the 3D data slicer 61 in the control unit 60.

In the present embodiment, the image comparison algorithm is a layer-by-layer comparison, but a line-by-line comparison may be performed, and in that case, it may be considered the same as in FIG.
Further, as an image comparison algorithm, a two-dimensional image comparison algorithm such as MMR (Modified Modified READ) used in a facsimile or the like, a hash function, or the like may be used. However, in this case, since the defect determination is essential before the material image passes through the cleaner 46, the position of the cleaner 46 needs to be examined. Further, since the slice data is accumulated in the storage, the slice data that is the original data of the discarded material image can be formed relatively quickly.

(Flowchart 3 of defect image acquisition and recovery processing)
The defect detection algorithm of this embodiment is demonstrated along the flowchart shown in FIG.
In step 301, slice data is input to the image generation controller 10. In step 302, the slice data is developed into a raster image, and a material image is generated from the particle image. In step 303, the material image is detected and acquired by the area sensor 52. In step 304, the image acquired by the area sensor 52 is compared with the accumulated slice data. In step 305, it is determined whether the comparison results match or do not match. If it is determined in step 305 that they match, the process proceeds to step 306, and the process proceeds to the stacking process. If it is determined in step 305 that they do not match, the process proceeds to step 307.

((Image defect judgment 3))
In step 307, it is determined whether or not the mismatched portion is within the stackable standard. If it is determined in step 307 that the mismatched portion is within the stackable reference, the process proceeds to step 306 and the stacking process is started. If it is determined in step 307 that the mismatched portion is not within the stackable reference, the process proceeds to step 308 and the corresponding material image is discarded.
Then, it returns to step 301 and modeling is performed by repeating the above process.

  Even in the present embodiment, even when a defect occurs in the material image, it is possible to continue modeling without causing a stacking fault. Therefore, the quality of the modeled object can be improved.

DESCRIPTION OF SYMBOLS 1 ... Modeling apparatus, 34 ... Photosensitive drum, 42 ... Conveyance body, 43 ... Heater plate, 44 ... Line sensor, 45 ... Illumination light source, 46 ... Cleaner, 49 ... Stage, 50 ... Material image

Claims (11)

A modeling device that creates a three-dimensional object by stacking modeling materials based on the 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;
The material image formed by the image forming unit is transferred, and a transport body that transports the material image;
A laminating portion for laminating the material images;
Dark field illumination that irradiates light on the material image, and an image acquisition unit that includes an image sensor that receives the scattered light of the light from the modeling material;
A modeling apparatus comprising: The modeling apparatus according to claim 1, wherein the image acquisition unit acquires information related to the material image from the scattered light of the light based on the material image received by the image sensor. A discarding unit for discarding the material image;
A comparison unit that compares the material image information acquired by the image acquisition unit with the slice data that is the original data of the material image;
A determination unit that determines whether or not the material image can be stacked based on a comparison result by the comparison unit;
When a negative determination is made by the determination unit, the control unit causes the material image to be discarded by the discard unit, and the material image is re-formed by the image forming unit based on the slice data;
The modeling apparatus according to claim 2, comprising: The determination unit determines whether or not at least a part of the material image is missing, stretched, uneven, or distorted, and a determination result as to whether or not the material image is a material image that can be laminated. The modeling apparatus according to claim 3, wherein: A marker processing unit that performs a process of forming a marker made of a modeling material by the image forming unit, transferring the marker to the transport body, and transporting the marker;
The modeling apparatus according to claim 1, wherein the image acquisition unit detects the marker. A modeling method by a modeling device that creates a three-dimensional object by laminating modeling materials based on the three-dimensional shape data of a three-dimensional model,
The modeling apparatus is
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;
The material image formed by the image forming unit is transferred, and a transport body that transports the material image;
A stacking unit for stacking the material images transported by the transport body;
An image acquisition unit for acquiring information on the material image on the carrier;
A discarding unit for discarding the material image on the carrier;
Have
A comparison step for comparing information of the material of interest image acquired by the image acquisition unit and the slice data of interest which is the original data of the material of interest image;
A determination step of determining whether the material image of interest is a material image that can be laminated based on the comparison result of the comparison step;
When a negative determination is made in the determination step, a control step of discarding the target material image by the discard unit and re-forming the target material image by the image forming unit based on the target slice data;
A modeling method comprising: The modeling method according to claim 6, wherein the image acquisition unit includes dark field illumination that irradiates light to the material image, and an image sensor that receives the scattered light of the light from the modeling material. When there are a plurality of material images including a material image of interest on the carrier,
In the control step, only the target material image is discarded among the plurality of material images, and only the target material image is re-formed by the image forming unit based on the target slice data. Item 8. The modeling method according to Item 6 or 7. When there are a plurality of material images including a material image of interest on the carrier,
8. The control step, wherein the plurality of material images are discarded by the discard unit, and the plurality of material images are re-formed by the image forming unit based on the slice data. The molding method described in 1. When the material image of the next layer formed continuously with the material of interest material has the same shape as the material of interest material,
8. The control step according to claim 6, wherein, in the control step, the target material image is discarded by the discard unit, and the material image of the next layer is re-formed and added instead of the target material image. The modeling method described. A modeling method for manufacturing a three-dimensional object by stacking modeling materials based on the three-dimensional shape data of a three-dimensional model,
Arranging a modeling material based on slice data generated from the three-dimensional shape data to form a material image;
Irradiating the material image with light and obtaining information of the material image from the scattered light; and
A comparison step of comparing the material image information with the slice data;
A determination step of determining whether or not the material image can be stacked based on a comparison result of the comparison step;
Discarding the material image for which a negative determination has been made in the determining step, and re-forming the material image based on the slice data;
A modeling method comprising:

Images