Calibration methods and systems for additive manufacturing

Abstract

Methods for calibrating a digital light processing additive manufacturing system. A method for scale factor calibration comprises: projecting calibration images of a calibration pattern by varying a projector's scaling factor; Capturing and analyzing the calibration images to determine dimensional attributes of the projected calibration pattern; Computing a calibration model fitting a relationship between the scaling factor and dimensional accuracy of the projector based on said dimensional attributes; Identifying a scaling factor where dimensional accuracy is optimal. A method for calibrating uniformity comprises: projecting a uniform pattern; capturing and analyzing a uniformity calibration image of said uniform pattern to determine a calibration intensity distribution of calibration image pixels; computing a correction map, wherein each correction map pixel is associated with a correction intensity determined using a tunable calibration factor and a calibration intensity of a corresponding calibration image pixel, wherein said tunable calibration factor depends on the calibration intensity distribution.

Description

[0001] CALIBRATION METHODS AND SYSTEMS FOR ADDITIVE

[0002] MANUFACTURING

[0003] CROSS REFERENCE TO REEATED APPEICATIONS

[0004] This application claims priority from US Provisional Patent Application No. 63 / 661,072 filed on June 18th, 2024, which is incorporated herein by reference in its entirety.

[0005] TECHNOLOGICAL FIEED

[0006] The present disclosure relates to the field of additive manufacturing, known also as three-dimensional printing.

[0007] BACKGROUND

[0008] Additive manufacturing is a technology that enables the creation of three- dimensional (3D) objects by layering materials, such as polymers, based on digital models. This process allows for complex designs that are often difficult or impossible to achieve with traditional manufacturing methods, making it a versatile tool in various industries.

[0009] Digital Light Processing (DLP) 3D printers are a type of additive manufacturing system. In most common DLP systems, a light projector selectively projects images (light patterns) into a light-curable resin situated above a build window. The projected image light simultaneously photocures resin at selected positions to form a cured layer above the build window. The process repeats so as to cure a plurality of layers of resin, thereby producing a 3D object. The light is usually in the ultraviolet range (e.g., wavelengths in the range of 405nm to 365nm).

[0010] GENERAL DESCRIPTION

[0011] Calibration of DLP 3D printers may include at least two parts: calibration of a scale factor (“SF calibration”), and calibration of uniformity. Calibration of a scale factor may include correction of dimensional accuracy. In other words, calibration of a scale factor may ensure that an object with an intended shape (e.g., a square) of given intended dimensions (e.g., edge length) is actually manufactured by the 3D printer with said intended dimensions, and that there is no discrepancy between the intended and actual dimensions and / or shape of the printed object resulting in a distorted shape (e.g., a rectangle). For example, model data may specify a length of 10mm for a square’s edge. When uncalibrated, the edges along the X-axis may be printed with a length of 9.5mm, and the edges along the Y-axis may be printed with a length of 10.5mm. Calibration of a scale factor may be required, for example, after assembling the DLP 3D printer (e.g., after a maintenance operation) or after reconfiguring optical parameters of the DLP 3D printer (e.g., pixel size). Calibration of a scale factor usually includes setting a scale factor value of the DLP 3D printer. This scale factor value is a factor by which the DLP 3D printer scales model data so as to print models correctly (meeting required dimensional accuracy).

[0012] Calibration of uniformity may include correction of an intensity for one or more projector pixel, so the intensity is identical for each projector pixel. Calibration of uniformity usually includes setting duty-cycle values of projector pixels and setting an intensity of light provided by the light source. Calibration of uniformity may be required, for example, due to non-uniformity of light provided to the projector and / or due to manufacturing tolerances and limitations of the projector.

[0013] Calibration of a scale factor is required in order to manufacture objects accurately. Calibration of uniformity is required for accurately producing details finer than a projector pixel, and for uniform curing of resin, so as to avoid overcuring or insufficient curing. Insufficient curing may result, for example, in separation of layers, weakness of the manufactured object (i.e., excessive susceptibility to deformation), and disruption of the manufacturing process due to weak adhesion to the build head, and inaccurate manufacturing (i.e., inaccurate dimensions). Overcuring may result, for example, in inaccurate manufacturing (e.g., low dimensional accuracy).

[0014] Methods for calibration of DLP 3D printers, as known in the art, use direct measurement apparatus. That is, an optical comparator is used for SF calibration, and a photometer is used for uniformity calibration. The direct measurement apparatus can measure only one point for each measurement. This results in time-consuming calibration processes.

[0015] Furthermore, specialized equipment is required. Calibration must be performed in a dedicated laboratory, and cannot be performed in the field (e.g., at a customer’s workshop). The calibration thus necessitates expansive logistics and a long period of inactivity of the DLP 3D printer.

[0016] Uniformity calibration as known in the art, suffers from inaccuracy due to a small number of measurements, requiring interpolation in order to map all the projector pixels. For example, 230 points may be measured, while the projector may have hundreds of thousands of pixels (e.g., about 900,000). Each measurement averages thousands of pixels of the projector (e.g., about 4,000), thus leading to information loss.

[0017] Furthermore, uniformity calibration according to methods known in the art results in loss of brightness. Intensities of projector pixels are set to a lowest level measured, by reducing a duty cycle of micromirrors of the DLP projector. This results in energy inefficiency of the printer, as a large portion of the light provided by the light source is dissipated instead of being projected, requiring the light source to provide more power in order to compensate for the loss. Another consequence is that the lifetime (total operation time in printing conditions) of the light source and of the micromirrors is also reduced.

[0018] Calibration of DLP printers may include other parts such as distortion calibration, focus calibration, detection of particles in the optical path, detection of inactive pixels.

[0019] The present disclosure aims to provide calibration methods and / or systems that improve existing methods and / or systems. The present disclosure aims to provide calibration methods that may take less time, be performed in the field, be more accurate, and provide better energy efficiency of the printer.

[0020] In a first aspect of the present invention, there is provided a method for calibrating a digital light processing additive manufacturing system. The additive manufacturing system including a build chamber and a projector configured to project images through a build window in the build chamber. The method includes: projecting at least one calibration image of a predefined calibration pattern by varying a scaling factor parameter of the projector over a plurality of scaling factor values within a scaling factor calibration range, wherein the at least one calibration image is projected onto a semi-transparent sheet positioned on the build window; capturing the at least one calibration image using the imaging device; analyzing the at least one captured image to determine dimensional attributes of the projected calibration pattern for the plurality of scaling factor values; computing a calibration model configured to fit a relationship between the scaling factor parameter and a dimensional accuracy of the projector assessed based on the dimensional attributes; identifying a scaling factor working point for which dimensional accuracy is optimal based on the calibration model; applying the identified scaling factor working point to the additive manufacturing system.

[0021] In addition to the above features, a method for calibrating a digital light processing additive manufacturing system, according to this aspect of the invention, can optionally include one or more of features (i) to (xv) below, in any technically possible combination or permutation: i. preliminarily calibrating the imaging device. ii. preliminarily calibrating the imaging device includes:

[0022] - disposing a calibration target comprising a calibration pattern on the build window.

[0023] - replacing a build platform of the additive manufacturing system with the imaging device and positioning the imaging device inside of the build chamber at a predetermined distance from the build window, so that a plane of focus of the imaging device coincides with the calibration sheet.

[0024] - imaging the calibration target so as to obtain an image of the calibration target.

[0025] - correcting any one of extrinsic and intrinsic parameters of the imaging device according to the image of the calibration target.

[0026] - removing the calibration target. iii. calibrating the imaging device includes correcting optical aberrations. iv. the scaling factor calibration range, of the projector, is 0.85 to 1.15. v. the calibration pattern includes any of a checkerboard and a rectangular dot grid. vi. analyzing the at least one captured image to determine dimensional attributes of the projected calibration pattern, for the plurality of scaling factor values, includes computing a set of characteristic points in the at least one calibration image. vii. for the at least one calibration image:

[0027] - computing coordinates of the characteristic points with reference to a X axis and a Y axis.

[0028] - computing expected coordinates of the characteristic points with reference to the X-axis and the Y-axis, based on the scaling factor value of the corresponding calibration image.

[0029] - computing for both axes, a linear regression of the coordinates of the characteristic points, based on corresponding expected coordinates so as to obtain an axial dimensional accuracy with respect to the X-axis and to the Y- axis for each of the at least one calibration image. viii. computing a calibration model includes:

[0030] - computing, for both axes, a linear regression of the axial dimensional accuracy based on the scaling factor values of the corresponding calibration images.

[0031] - identifying a scaling factor working point for which dimensional accuracy is optimal based on the calibration model, which includes: identifying a scaling factor working point with respect to the X-axis such that the axial dimensional accuracy with respect to the X-axis is equal to one, and a scaling factor working point with respect to the Y-axis, so that the axial dimensional accuracy with respect to the Y-axis is equal to one; and, computing a weighted average of the scaling factor working point with respect to the X-axis and a scaling factor working point with respect to the Y-axis, so as to obtain the scaling factor working point. ix. the imaging device is mechanically coupled to the build chamber. x. the imaging device is mechanically coupled to a build arm. xi. the mechanical coupling includes a track configured for enabling displacement of the imaging device according to a predefined path. xii. the predefined path is along an optical axis of the imaging device or perpendicular to an optical axis of the imaging device. xiii. the track is a track of a build platform, and wherein the method includes removing the build platform. xiv. the mechanical coupling includes a mount. xv. the mount is configured to be detachable from the build chamber.

[0032] In a second aspect of the present invention, there is provided a method for calibrating a digital light processing additive manufacturing system. The additive manufacturing system including a build chamber and a projector configured to project images on a build window in the build chamber. The method includes: projecting a uniform pattern onto a semi-transparent sheet, wherein a duty cycle of each micromirror of the projector is set to an identical predefined value; capturing a uniformity calibration image of the uniform pattern projected on the semi-transparent sheet using the imaging device; analyzing the captured uniformity calibration image to determine a calibration intensity distribution of calibration image pixels; computing a correction map, wherein each correction map pixel is associated with a correction intensity determined using a tunable calibration factor and a calibration intensity of a corresponding calibration image pixel, wherein the tunable calibration factor depends on attributes of the calibration intensity distribution; applying the correction map to further images projected by the projector.

[0033] In addition to the above features, a method for calibrating a digital light processing additive manufacturing system, according to this aspect of the invention, can optionally include one or more of features (i) to (xxv) below, in any technically possible combination or permutation: i. preliminary calibrating the imaging device, including:

[0034] - disposing a calibration target comprising a calibration pattern on the build window.

[0035] - replacing a build platform of the additive manufacturing system with the imaging device and positioning the imaging device inside of the build chamber at a predetermined distance from the build window so that a plane of focus of the imaging device coincides with the calibration target. - imaging the calibration target using the imaging device so as to obtain an image of the calibration target; correcting any one of extrinsic and intrinsic parameters of the imaging device according to the image of the calibration target; removing the calibration target. ii. calibration image pixels corresponding to a calibration intensity higher than a predefined threshold, are associated with a correction intensity lower than an uncorrected intensity. iii. computing a correction map includes:

[0036] - computing a reciprocal distribution being a distribution of reciprocal values of the calibration intensity distribution, and an average p and a standard deviation o of the reciprocal distribution.

[0037] - dividing values of the reciprocal distribution by the tunable calibration factor being p+Xo wherein X is a tunable standard variation multiplier.

[0038] - multiplying the reciprocal distribution by a reference maximum value, rounding to a nearest integer, and clipping according to the reference maximum value, so as to obtain the correction map. iv. normalizing the calibration intensity distribution according to at least one of a maximum calibration intensity and a minimum calibration intensity. v. optimizing the tunable calibration factor. vi. the optimizing includes reducing a standard deviation of the calibration intensity distribution to below a predefined standard deviation threshold, and keeping an average of the calibration intensity distribution above a predefined average threshold. vii. the optimizing includes reducing a standard deviation of a calibration intensity histogram to below a predefined standard deviation threshold, and keeping an average of a calibration intensity histogram above a predefined average threshold. viii. the optimizing includes interpolating a plurality of verification measurements. ix. computing local averages of pixels of the captured calibration image, so as to correct defects. x. calibrating linearity of the imaging device. xi. an intensity resolution of the imaging device is between 8 and 16 bits. xii. indication of malfunctioning projector pixels or particles blocking a path of light emitted by the projector. xiii. a spatial resolution of the imaging device is higher than a spatial resolution of the projector, and wherein the method includes downsampling the captured calibration image. xiv. the imaging device is mechanically coupled to the build chamber. xv. the imaging device is mechanically coupled to a build arm. xvi. the mechanical coupling includes a track configured for enabling displacement of the imaging device according to a predefined path. xvii. the predefined path is along an optical axis of the imaging device, or perpendicular to an optical axis of the imaging device. xviii. the track is a track of a build platform, and wherein the method includes removing the build platform. xix. the mechanical coupling includes a mount. xx. the mount is configured to be detachable from the build chamber. xxi. computing a plurality of correction map for various values of the tunable calibration factor and identifying an optimal correction map based on manufacturing parameters of an object to be manufactured. xxii. selecting the optimal correction map for manufacturing the object. xxiii. the manufacturing parameters include any of power consumption of the additive manufacturing system, lifetime of the projector, available intensity range of projected images, and ability of curing of details finer than a projector pixel. xxiv. applying the correction map includes changing a duty cycle of projector pixels. xxv. indication of malfunctioning camera pixels.

[0039] In a third aspect of the present invention, there is provided a method for calibrating an additive manufacturing system. The additive manufacturing system including a build chamber and a projector configured to project images on a build window in the build chamber. The method includes: performing the method according to the first aspect of the present invention, so as to obtain scaling factor calibration; performing the method according to the second aspect of the present invention, so as to obtain uniformity calibration.

[0040] According to some embodiments of this aspect of the invention, the method includes focus calibration.

[0041] In a fourth aspect of the present invention, there is provided a method for calibrating an additive manufacturing system. The additive manufacturing system includes a build chamber and a projector configured to project images on a build window in the build chamber. The method includes projecting an image by the projector onto a semi-transparent sheet positioned on the build window. The method includes capturing the image by an imaging device, so as to obtain a captured image. The method includes analyzing intensity of pixels included in the captured image to determine malfunctioning projector pixels.

[0042] According to some embodiments of this aspect of the invention, the (projected) image is a dark image.

[0043] According to some embodiments of this aspect of the invention, the method includes darkening the additive manufacturing system.

[0044] According to some embodiments of this aspect of the invention, the imaging device is mechanically coupled to a build arm.

[0045] In a fifth aspect of the present invention, there is provided a maintenance protocol for an additive manufacturing system. The protocol includes performing a calibration method according to the fourth aspect of the presently disclosed subject matter, so as to determine any one of: a number of malfunctioning projector pixels, and a local density of malfunctioning projector pixels. The protocol includes replacing the projector according to any one of: the number of malfunctioning projector pixels, and the local density of malfunctioning projector pixels.

[0046] In a sixth aspect of the present invention, there is provided an additive manufacturing system. The additive manufacturing system includes a controller configured to communicate with an imaging device. The controller includes a non-transitory controller readable storage medium storing controller instructions, wherein the controller instructions are used for causing the controller to execute the method according to any one of the first, second, or third aspects of the present invention. In the present disclosure, the following terms and their derivatives may be understood according to the below explanations:

[0047] The terms “gray value” (also abbreviated “GV”) of a pixel of an image captured by an imaging device may represent an intensity of light of the pixel.

[0048] The term “mirror” may refer to an individually controllable micro-mirror within an array of micromirrors (the array of micromirrors also referred to as “DLP chip” or as “Digital Micromirror Device”) of the DLP 3D printer. Each mirror may be tilted independently either to direct light onto the projection surface (in 'on' position), or to direct light into a heat sink (in 'off position).

[0049] The term “projector pixel” may refer to a pixel in a projected image corresponding to one mirror in the array of micromirrors.

[0050] The term “duty cycle” may refer to a fraction of time a micro-mirror spends in the 'on' position (reflecting light towards the resin) within a specific period. Adjusting the duty cycle of each micro-mirror may allow for precise control over the amount of light that each projector pixel delivers to the resin during a specific period of time.

[0051] The term “sheet” may refer to a flat surface used as a screen to form an image from the projected light. It is noted that the sheet may be flexible or rigid.

[0052] The terms “X-axis” and “Y-axis” may refer to a horizontal axis and a vertical axis of images. The exact labeling of the axes as “X-axis” and “Y-axis” can depend on context, i.e., the “X-axis” may refer to the horizontal axis and “Y-axis” may refer to the vertical axis, or the “X-axis” may refer to the vertical axis and “Y-axis” may refer to the horizontal axis. The definition of an origin point of the axes can depend on context, and can be at a geometrical center of an image or can be at a corner of the image.

[0053] BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to better understand the subject matter disclosed herein, and to exemplify how it may be carried out in practice, embodiments will now be described, by way of nonlimiting examples only, with reference to the accompanying drawings, in which:

[0055] Figs. 1A-1C schematically illustrate various aspects of additive manufacturing systems according to embodiments of the present disclosure. Fig- 2 shows a flowchart illustrating a broad aspect of a method for calibrating an additive manufacturing system, according to embodiments of the present disclosure.

[0056] Fig- 3 shows a flowchart illustrating a method for calibrating an additive manufacturing system, according to embodiments of the present disclosure.

[0057] Figs. 4A-4B show coordinates of characteristic points, according to embodiments of the present disclosure.

[0058] Fig. 5 illustrates linear regression of axial dimensional accuracy, according to embodiments of the present disclosure.

[0059] Fig. 6 illustrates verification of axial dimensional accuracy, according to embodiments of the present disclosure.

[0060] Fig. 7 illustrates exemplary calibration patterns according to embodiments of the present disclosure.

[0061] Figs. 8A-8B illustrate positioning of imaging devices, according to embodiments of the present disclosure.

[0062] Fig. 9 shows a flowchart illustrating a broad aspect of a method for calibrating an additive manufacturing system, according to embodiments of the present disclosure.

[0063] Fig. 10 shows a flowchart illustrating a method for calibrating an additive manufacturing system, according to embodiments of the present disclosure.

[0064] Fig. 11 shows uncalibrated and calibrated intensity maps and histograms, according to embodiments of the present disclosure.

[0065] Fig. 12 shows intensity histograms corresponding to calibration according to different tunable calibration factors, according to embodiments of the present disclosure.

[0066] Figs. 13A-13B show scatter graphs of intensity average and standard variation corresponding to calibration according to different tunable calibration factors, according to embodiments of the present disclosure.

[0067] Fig. 14 illustrates linearity calibration of an imaging device, according to embodiments of the present disclosure.

[0068] Fig. 15 shows a comparison of calibrated intensity maps and histograms, as obtained by methods according to embodiments of the present disclosure, and as obtained by methods known in the art. Figs. 16A-16B show scater graphs of intensity average and standard variation corresponding to calibration according to different tunable calibration factors, according to embodiments of the present disclosure.

[0069] Fig. 17 shows a flowchart illustrating a broad aspect of a method for calibrating an additive manufacturing system, according to embodiments of the present disclosure.

[0070] DETAILED DESCRIPTION OF EMBODIMENTS

[0071] Described herein are some examples of systems and methods useful for calibrating additive manufacturing systems.

[0072] In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the subject mater. However, it will be understood by those skilled in the art that some examples of the subject mater may be practiced without these specific details. In other instances, well-known methods, procedures and components have not been described in detail so as not to obscure the description.

[0073] As used herein, the phrases “for example,” “such as”, “for instance” and variants thereof describe non-limiting examples of the subject matter.

[0074] Reference in the specification to “one example”, “some examples”, “another example”, “other examples, “one instance”, “some instances”, “another instance”, “other instances”, “one case”, “some cases”, “another case”, “other cases” or variants thereof means that a particular described feature, structure, or characteristic is included in at least one example of the subject mater, but the appearance of the same term does not necessarily refer to the same example.

[0075] It should be appreciated that certain features, structures, and / or characteristics disclosed herein, which are, for clarity, described in the context of separate examples, may also be provided in combination in a single example. Conversely, various features, structures, and / or characteristics disclosed herein, which are, for brevity, described in the context of a single example, may also be provided separately or in any suitable subcombination.

[0076] Unless specifically stated otherwise, as apparent from the following discussions, it is appreciated that throughout the specification discussions utilizing terms such as “computing”, “determining”, “running”, or the like, may refer to the action(s) and / or process(es) of any combination of software, hardware, and / or firmware. For example, these terms may refer in some cases to the action(s) and / or process(es) of a programmable machine, that manipulates and / or transforms data represented as physical, such as electronic quantities, within the programmable machine’s registers and / or memories, into other data similarly represented as physical quantities within the programmable machine’s memories, registers, and / or other such information storage, transmission, and / or display element(s).

[0077] A DLP 3D printer according to embodiments of the present disclosure is schematically illustrated in Fig. 1A. The DLP 3D printer 105 includes a build chamber 110 wherein a 3D object 115 may be manufactured. DLP 3D printer 105 includes a projector 120 to project light 122. DLP 3D printer 105 includes a build window 125 that enables passage of light 122 into light-curable resin above it (not shown), so as to photo-cure layers of resin one layer at a time. DLP 3D printer 105 includes a build platform 140 that is configured to carry an object (e.g. 3D object 115) during its manufacturing process. DLP 3D printer 105 includes an actuation system configured to displace the build platform within the build chamber such as a track 145 mechanically coupled to the build platform 140. Track 145 may be configured to move the build platform 140 vertically, closer and farther from build window 125 during the manufacturing process so as to position build platform 140 and / or 3D object 115 at a correct distance from build window 125. Build platform 140 comprises a build head and a build arm (not shown). The build arm is mechanically coupled to track 145, allowing the vertical motion of the build head. In some embodiments, the build head may be removable from the build arm.

[0078] The light projector in DLP 3D printers includes a light source (such as a plurality of light emitting diodes), and a plurality of “projector pixels”. Each projector pixel corresponds to a specific position within a layer to be cured. In other words, each layer is divided into a two-dimensional grid, and for each grid point, there is a corresponding projector pixel.

[0079] The projector pixels are implemented by micromirrors. Each micromirror may assume one of two states: an “on” state, where light from the light source is transmitted, thereby curing resin at the corresponding position, or an “off’ state, where light from the light source is blocked, thereby not curing resin at the corresponding position. By controlling a time fraction where a micromirror is in the “on” state (also known as the “duty cycle”), an intensity of a projector pixel is controlled. For example, a 50% duty cycle corresponds to an intensity of half of a maximum intensity. The method of controlling the duty cycle is also known as “pulse width modulation”, or PWM.

[0080] Light from one projector pixel can reach positions corresponding to nearby projector pixels. This enables manufacture of objects with features that are smaller than a nominal size of (i.e., finer than) a projector pixel (e.g., having thickness of less than an edge length of a micromirror).

[0081] The operation and structure of a 3D DLP printer according to embodiments of the present disclosure may be described in more detail.

[0082] Figs. 1B-1C schematically illustrate components of an exemplary DLP 3D printer 1100. The DLP 3D printer 1100 includes two subassemblies of electromechanical components that, when engaged in an engaged configuration, can execute a "bottom-up" DLP process. Generally, the DLP 3D printer 1100 includes a base assembly (e.g., base assembly 1102 in Fig. 1C) and a tray assembly (e.g., tray assembly 1104 shown in Fig. 1C)

[0083] A user may assemble tray assembly 1104 by inserting a separation membrane 1160 into a tray structure 1150 and engage tray assembly 1104 with base assembly 1102 before DLP printer 1100 executes the printing task. DLP 3D printer 1100 can then execute the printing task via an imbedded computational device running computer code (hereinafter the "controller"), which electronically actuates build platform / head 1106 (e.g., via a linear actuation system) and controls a projection system / projector 1120 and a pressure regulation system 1190 to selectively cure volumes of resin, and to separate these cured volumes of resin from the build window 1110 and separation membrane 1160.

[0084] DLP 3D printer 1100, in executing the printing task, proceeds through multiple physical arrangements of the components in order to cure a build (e.g., a set of physical, 3D objects) from the resin contained within tray assembly 1104. In a lamination phase, DLP 3D printer 1100 reduces the pressure (i.e., draws a vacuum / evacuates fluid from) in the interstitial layer between separation membrane 1160 and build window 1110, thereby fully laminating separation membrane 1160 against build window 1110 and preventing formation of bubbles or wrinkles that may disrupt the reference surface for DLP 3D printer 1100. During the lamination phase, DLP 3D printer 1100 can photo-cure a selective volume of resin above the laminated surface of separation membrane 1160. Subsequent to completion of photocuring a selective volume of resin, DLP 3D printer 1100 can execute a separation process including a pressurization phase, a retraction phase, and a relamination phase. In the pressurization phase, DLP 3D printer 1100 injects fluid into the interstitial region between separation membrane 1160 and build window 1110, thereby generating separation between separation membrane 1160 and build window 1110 in order to reduce adhesion forces (e.g., Stefan adhesion, suction forces) between the newly cured layer of the build and build window 1110. In the retraction phase, DLP 3D printer 1100 actuates build platform 1106 upward and away from build window 1110 to separate separation membrane 1160 from build window 1110, to peel separation membrane 1160 from the newly cured layer of the build, and to make space to photo-cure a successive layer of resin. In the re-lamination phase, DLP 3D printer 1100 evacuates fluid from the interstitial region between separation membrane 1160 and build window 1110 in order to peel separation membrane 1160 from the newly cured layer of the build, and to re-laminate the separation membrane 1160 against the build window 1110 in preparation for curing a successive layer of the build. Thus, DLP 3D printer 1100 can repeat this process cycle to cure successive layers of the resin, thereby constructing three-dimensional object(s).

[0085] Base assembly 1102 acts as the primary assembly of a 3D printer. Base assembly 1102 includes projection system 1120, a window platform (e.g., window platform 1132 shown in Fig. 1C), build window 1110, a fluid distribution port 1140 and / or a fluid distribution channel 1142, a gasket system, a pressure regulation system 1190, a tray seat 1130, a build platform 1106, and a controller. Base assembly 1102 can be a free-standing structure that may be placed on a level surface for best printing results. The free-standing structure of base assembly 1102 links the aforementioned components in a calibrated arrangement that ensures consistent alignment between projection system 1120 and build window 1110 and parallelism between the reference plane of build window 1110, the surface of retractable build platform 1106, and the focal plane(s) of projection system 1120. The structure of base assembly 1102 can be manufactured from any rigid material that does not significantly deform under the weight of base assembly 1102 or the stresses involved during repetitive build cycles. Base assembly 1102 can also include a build chamber, into which tray assembly 1104 may be loaded (e.g., via engagement with tray seat 1130), and a hatch to provide access to this build chamber. Base assembly 1102 can further include systems configured to control the environment within the build chamber (e.g., such as pressure regulation system 1190 and / or one or more heating elements).

[0086] Projection system 1120 is upward facing, is housed in base assembly 1102, and can include one or more projectors configured to project electromagnetic radiation in an emissive spectrum, which can include the ultraviolet (hereinafter “UV”), visible, or near infrared (hereinafter “NIR”) spectrum. The projection system can emit electromagnetic radiation in one or more wavelength bands tuned to the chemical and physical properties of the resin and its specific curing process. For example, projection system 1120 (e.g., a digital UV projector) can project electromagnetic radiation in an emissive spectrum of 300 to 450 nanometers. Projection system 1120 is electrically coupled to the controller, receives potentially software-modified frames (also referred to herein as images) corresponding to full or partial cross-sections of a three-dimensional model of the build, and projects electromagnetic radiation through build window 1110 and separation membrane 1160 in the engaged configuration (and during the photocuring phase) to selectively photo-cure volumes of the resin according to build settings and the received frames.

[0087] In some embodiments, projection system 1120 further includes a set of light sources, such as projectors or other electromagnetic emitting devices. In this variation, each irradiation source of projection system 1120 can define a projective area within build window 1110 in order to maintain a higher resolution across build window 1110 via tiling or stitching techniques. Additionally, or alternatively, each light source can define a separate emissive spectrum enabling projection system 1120 to project electromagnetic radiation within multiple combinations of spectral bands.

[0088] Generally, window platform 1132 extends upwards from tray seat 1130 of base assembly 1102 and is configured to align within a tray aperture 1152 of tray assembly 1104 when DLP 3D printer 1100 is in the engaged configuration. Window platform 1132 is a rigid structure that encompasses projection system 1120 and defines an opening above the upward facing projection system 1120 that is spanned by the build window 1110. The upper surface of window platform 1132 defines a horizontal reference plane which is coincident with the upper surface of build window 1110 and the primary focal plane of projection system 1120. System 1100 can include a window platform 1132 of a shape that corresponds to a shape of tray aperture 1152, enables engagement with separation membrane 1160, and is configured to define fluid distribution ports 1140 and / or fluid distribution channels 1142 around the build window 1110 and within the interstitial region. For example, the upper surface of window platform 1132 can define a circular shape, a rectangular shape, or any other shape, depending on the desired shape of the tray aperture 1152. In an additional example, window platform 1132 may be of any size larger than the build region of the DLP 3D printer 1100 and / or the dimensions of builds to be manufactured by the DLP 3D printer 1100. Window platform 1132 may have filleted corners and edges around the upper surface of window platform 1132 to prevent tearing of separation membrane 1160 as it is tensioned over window platform 1132. Window platform 1132 defines an opening that is spanned or partially spanned by build window 1110. Generally, the shape and size of the opening defined by window platform 1132 roughly corresponds to the shape and size of the upper surface of build window 1110 in order to maximize utilization of the build region of DLP 3D printer 1100.

[0089] Build window 1110 is mounted to window platform 1132 such that the upper surface of build window 1110 is approximately flush with the upper surface of window platform 1132 and further defines the horizontal reference plane for builds manufactured in DLP 3D printer 1100. Build window 1110 is arranged above projection system 1120 and aligned with the projection area of projection system 1120, such that the focal plane of projection system 1120 coincides with the upper surface of separation membrane 1160 laminated over build window 1110. Generally, build window 1110 is substantially transparent (e.g., exhibiting greater than 85% transmittance) to the emissive spectrum of the projection system, and thus passes electromagnetic radiation output by projection system 1120 into the resin above build window 1110 and separation membrane 1160. Build window 1110 also functions as a rigid support and reference surface for separation membrane 1160 and a layer of resin arranged thereover. Build window 1110 is statically mounted to base assembly 1102, via window platform 1132, that can include projection system 1120, build platform 1106, fluid distribution port 1140, pressure regulation system 1190, and / or the build chamber to ensure repeatable, accurate alignment between build window 1110 and the rest of base assembly 1102. The interface between rigid window platform 1132 and build window 1110 is also gas-impermeable such that a pressure gradient, such as 300 kPa, can be sustained across build window 1110.

[0090] Build window 1110 is manufactured from a pane of transparent, rigid glass, such as amorphous / silicate or crystalline / ceramic glass. In particular, build window 1110 can be both transparent to ultraviolet (or other) light output by projection system 1120 and can be substantially rigid, hard, and temperature-stable to form a robust, flat reference surface that supports separation membrane 1160 and that may exhibit minimal deflection or deformation during multiple build cycles, thereby yielding high and consistent build quality. In one variation, build window 1110 is transmissive to infrared (hereinafter “IR”) radiation such that a thermographic sensor positioned below build window 1110 can accurately measure the temperature of the resin during the photocuring phase of the build cycle.

[0091] Base assembly 1102 includes the one or more fluid distribution ports 1140 configured to fluidically (i.e., pneumatically or hydraulically) couple pressure regulation system 1190 to the interstitial region between separation membrane 1160 and build window 1110, thereby enabling pressure regulation system 1190 to inject and / or evacuate fluid from the interstitial region while DLP 3D printer 1100 is in the engaged configuration. Pressure regulation system 1190 can be configured to pressurize and / or depressurize by injecting and / or evacuating fluid from the interstitial region. More specifically, pressure regulation system 1190 may be: fluidically coupled to fluid distribution port 1140; configured to inject fluid into the interstitial region to separate separation membrane 1160 from build window 1110 in the engaged configuration and during a pressurization phase; and configured to evacuate fluid from the interstitial region to laminate separation membrane 1160 to build window 1110 in the engaged configuration and during a lamination phase.

[0092] As indicated hereinabove, base assembly 1102 also includes a vertically mobile build platform 1106 to which a first layer of the build adheres and from which the build is suspended toward build window 1110 during the build cycle. More specifically, base assembly 1102 can include a build platform 1106 defining a planar surface opposite and substantially parallel to the upper surface of build window 1110, and a linear actuation system (including a single linear actuator or multiple timed linear actuators) configured to vertically translate build platform 1106 relative to build window 1110. In one implementation, DLP 3D printer 1100 can include a build platform 1106 defining negative features, such as channels or through holes to improve the flow of resin out from under build platform 1106 during advancement of build platform 1106 into the resin reservoir and to facilitate the removal of the build from build platform 1106 after completion of the build.

[0093] Build platform 1106 is a vertically actuating surface opposite the build window 1110. DLP 3D printer 1100 can include a linear actuation system (with increments as small as e.g., 0.11 microns) mechanically coupled to the build platform 1106. Additionally, during actuation of the linear actuation system, the controller can track forces applied by the linear actuation system to build platform 1106 (e.g., based on a current draw of the linear actuation system or by sampling a force sensor or strain gauge coupled to the build platform 1106), and implement closed-loop techniques to control movement of the linear actuation system in order to achieve a particular distribution of separation forces between the newly cured layer of the build and separation membrane 1160 (e.g., to sweep this separation force along a predefined force profile once per layer). Thus, during the build cycle the linear actuation system lowers build platform 1106 to specific heights above separation membrane 1160 such that photocured resin adheres to the build surface of build platform 1106 facing the window. As DLP 3D printer 1100 selectively cures successive layers of the build according to the printing task, DLP 3D printer 1100 can retract build platform 1106 upward by a first distance in order to separate the current layer of the build from separation membrane 1160 and then advance build platform 1106 downward-by a second distance less than or equal to the first distance, in preparation for curing a successive layer of the build.

[0094] Base assembly 1102 of DLP 3D printer 1100 can include a controller that controls the electromechanical components of DLP 3D printer 1100. Generally, the controller is an imbedded computer system that sends instructions to projection system 1120, pressure regulation system 1190, and the linear actuation system coupled to build platform 1106, to execute the printing task. In one implementation, the controller controls and receives instructions from a user interface, which can be a touchscreen or a set of buttons, switches, knobs, etc. Alternatively, the controller can communicate with and receive instructions from an external computational device. In another implementation, the controller is connected to a network, such as the Internet, and is configured to receive instructions over the network. Additionally, the controller can send commands, in the form of digital and / or analog electrical signals, in order to actuate various electromechanical components of the system, such as the magnetic locking mechanism, a door hatch release to the build chamber, the purge valves, and / or lighting elements within the build chamber. Furthermore, the controller can receive data from sensors integrated with system 1100 and execute feedback control algorithms based on these data in order to modify the function of projection system 1120, pressure regulation system 1190, and / or the linear actuation system.

[0095] As described above, DLP 3D printer 1100 can include an interchangeable separation membrane 1160. As shown in Fig. 1C, before DLP 3D printer 1100 executes the printing task, the user may fasten separation membrane 1160 between a lower member 1156 and an upper member 1155 of tray structure 1150. Once separation membrane 1160 is fastened between upper member 1155 and lower member 1156 of the build tray, the user may lower tray assembly 1104 over the upper surface of window platform 1132 and build window 1110. The tray then kinematically aligns with reference features 1134 of base assembly 1102, thereby engaging with base assembly 1102. In a passive lamination variation, when tray assembly 1104 and base assembly 1102 are engaged, separation membrane 1160 is tensioned flush against the surface of build window 1110 and covers fluid distribution channel 1142. In this variation, separation membrane 1160 also forms a fluid-impermeable seal (within the maximum operating pressure of the system) against an interstitial gasket 1170 arranged along the edge of window platform 1132 or with the edge of window platform 1132 itself. In an active lamination gasket variation of the system, separation membrane 1160 is positioned just above build window 1110 upon kinematic alignment of tray assembly 1104 with tray seat 1130 of base assembly 1102.

[0096] Before or after tray assembly 1104 is engaged with base assembly 1102, resin is loaded into the volume defined by upper member 1155 of the build tray and separation membrane 1160. If the resin is not sensitive to oxygen and / or ambient air, the resin may be poured directly into the build tray. However, if the resin is sensitive to oxygen, humidity, and / or ambient air, the resin can be injected into a tray assembly 1104 through a sealed port in a sealed build chamber after the build chamber has been filled with an inert fluid. Thus, after loading is complete, the build volume defined by the inner surface of the build tray is at least partially occupied by a volume of resin. The resin is in contact with the upper surface of separation membrane 1160 and the inner surface of the upper member of tray assembly 1104. However, the resin does not come into contact with build window 1110 underneath separation membrane 1160.

[0097] Scaling factor calibration

[0098] Referring to Fig. 2, a flowchart generally illustrating some embodiments of a method 200 for calibrating an additive manufacturing system, according to embodiments of the present disclosure, is shown. The additive manufacturing system may include a build chamber and may include a projector. The projector may be configured to project images through a build window positioned in the build chamber. As an example, the additive manufacturing system may be as schematically illustrated in Fig. 1A and / or in Figs. 1B- 1C. In examples including a separation membrane over the build window like those illustrated in Figs. 1B-1C, the projector may be configured for projecting images through the build window and the separation membrane, i.e., a focal plane of the projector may coincide with an upper surface of the separation membrane laminated over the build window.

[0099] In a first step 210, method 200 may include projecting a plurality of calibration images. The images may be images of a predefined calibration pattern, for example a checkerboard pattern or a grid of dots, for instance, a rectangular grid of dots. Calibration patterns are further discussed hereinbelow in relation to Fig. 7. For example, the method may include projecting between 5 to 50 calibration images, preferably between 10 to 20.

[0100] Method 200 may include setting a distinct scaling factor parameter of the projector for each (or most) of the images. In other words, method 200 may include projecting the plurality of calibration images, by varying a scaling factor parameter of the projector over a plurality of scaling factor values, within a scaling factor calibration range. In some embodiments, the scaling factor calibration range may be 0.85 to 1. 15.

[0101] In a further step 220, method 200 may include capturing the plurality of calibration images using an imaging device positioned inside of the build chamber. As explained hereinbelow, the imaging device may be integrated in the additive manufacturing system or removably positioned. The imaging device may be preliminarily calibrated. In a further step 230, method 200 may include analyzing the plurality of captured images to determine dimensional attributes of the projected calibration pattern for the plurality of scaling factor values. Generally, as described hereinbelow, analyzing the plurality of captured images may include finding a set of representative points. Examples for characteristic points may include points of center of mass of rectangles included in a checkerboard pattern of the captured calibration images, points on edges of the checkerboard pattern, vertex points of polygons, and center points of dots. In some embodiments, computerized vision procedures (e.g., edge finding, shape recognition) may be applied to the plurality of the captured images, so as to obtain the characteristic points.

[0102] Generally, dimensional attributes may include differences and / or ratios, between expected coordinates of the characteristic points to actual (i.e., as found) coordinates of the characteristic points. For example, as described hereinbelow, the dimensional attributes may include a scale factor, with reference to the X-axis and with reference to the Y-axis. In other words, the dimensional attributes may include a ratio of actual coordinates to expected coordinates (of the characteristic points), with reference to the X-axis and with reference to the Y-axis.

[0103] In a further step 240, method 200 may include computing a calibration model configured to fit a relationship between the scaling factor parameter and a dimensional accuracy of the projector assessed based on the dimensional attributes. Dimensional accuracy may be closeness of the dimensional attributes to ideal (i.e., desired) dimensional attributes. Ideal dimensional attributes may be, for example, scale factors being one. Computing a calibration model may include, for example, a regression analysis according to scale factors as a function of the scaling factor parameter.

[0104] Method 200 may include identifying a scaling factor working point (step 250) for which dimensional accuracy is optimal (i.e. satisfies a predefined criteria), based on the calibration model. For example, as described hereinbelow, the scaling factor working point may be a value of the scaling factor parameter, where an average of the scale factor with reference to the X-axis and the scale factor with reference to the Y-axis, may be one. In a different example, the scaling factor working point may be a value of the scaling factor parameter, where one of the scale factor with reference to the X-axis and the scale factor with reference to the Y-axis, may be one. In a further step 260, method 200 may include applying the identified scaling factor working point to the additive manufacturing system. For example, as indicated hereinabove, applying the identified scaling factor working point may include setting a scale factor value of the additive manufacturing system. Further, the method may include a step of using the additive manufacturing system to manufacture objects.

[0105] Referring to Fig. 3, a flowchart illustrating more details of a method 300 for calibrating an additive manufacturing system, according to embodiments of the present disclosure, is shown. The additive manufacturing system may include a build chamber and may include a projector. The projector may be configured to project images on a build window positioned in the build chamber. As an example, the additive manufacturing system may be as schematically illustrated in Fig. 1A and / or in Figs. 1B-1C. In examples including a separation membrane over the build window like the ones illustrated in Figs. 1B-1C, the projector may be configured for projecting images through the build window and the separation membrane i.e. a focal plane of the projector may coincide with an upper surface of the separation membrane laminated over the build window.

[0106] In a step 301, method 300 may include positioning an imaging device inside the build chamber. The positioning may be so as to allow imaging of a plurality of calibration images i.e. images projected through the build window onto a sheet positioned on the build window. That is, to capture a plurality of calibration images. In some embodiments, method 300 may include aligning an optical axis of the imaging device and of the projector. In some embodiments, method 300 may include adjusting a position of the imaging device so that a focal plane of the projector is conjugated on the focal plane of the imaging device. In some embodiments, method 300 may include orienting the imaging device so that image axes of the imaging device are aligned with the image axes of the projector. In other words, the X axis and Y axis (i.e., axes along the width and height) of an image captured by the imaging device and of an image projected by the projector, may coincide. In yet other words, a mid-height line and a mid-width line, of an image captured by the imaging device, may be, respectively, optical conjugates of a mid-height line and a mid-width line of an image projected by the projector. Positioning of the imaging device is further discussed hereinbelow in relation to Figs. 8A-8B. In a further step 302, method 300 may include calibrating the imaging device. An imaging device may be, for example, a camera. Calibrating the imaging device may include a step 304 of positioning (disposing) a calibration target. The calibration target may be, for example, a sheet or a plaque. The calibration target may be disposed on the build window. The calibration target may include a calibration pattern, for example a checkerboard pattern or a grid of dots, for instance a rectangular grid of dots. In some embodiments, the calibration target may comprise a rectangular grid of dots, where the actual size of a dot may vary based on the number of pixels of the imaging device and the selected field of view. For instance, a dot size may be of at least 9 camera pixels arranged as a 3 x 3 square, i.e., an edge of an image of a dot may extend along at least three pixels, thereby ensuring accurate detection and correct processing of the image. Distance between the dots may be of at least 4 pixels (of the imaging device) apart to prevent detection and processing of two consecutive dots as a single dot. The number of dots appearing on the calibration target may be dependent on the field of view of the camera. As the number of dots increases, calibration of the camera may be improved. In some embodiments, the grid layout may be such as the dots are arranged along vertical and horizontal lines that are perpendicular one to the other (e.g., as illustrated in Fig.7). In some embodiments, the calibration target may be printed, for instance by laser printing. Calibrating the imaging device may further include a step 306 of imaging the calibration target, so as to capture at least one image of the calibration target. The at least one image of the calibration target may be processed (e.g., by computer vision software) so as to find parameters of the imaging device, such as position, orientation, and scaling of images captured by the imaging device. In some embodiments, calibrating the imaging device may further include correcting extrinsic and intrinsic parameters of the imaging device. Extrinsic parameters may include perspective distortions (i.e., position and / or orientation) of the imaging device relative to the build window. Intrinsic parameters may include pixel size, focal distance, field of view, spatial resolution, and / or optical aberrations (e.g. non-linear distortion).

[0107] Calibrating the imaging device may be required, for example, as the positioning of the imaging device for calibrating the additive manufacture system may be subject to variations, such as precision of the positioning method. Method 300 may include a further step 308 of removing the calibration target from the build window.

[0108] The method may further include a step 309 of positioning (disposing) a semitransparent sheet. The sheet may be disposed on the build window. The semi-transparent sheet may be uniform (e.g., light transmission uniformity through the screen should be better than 98%). In some embodiments, the sheet may be Lambertian. That is, the sheet may scatter (i.e., transmit or reflect) ultraviolet light, so that the radiance (power per area per solid angle) emitted from the sheet may be identical for every angle of view (i.e., for every solid angle).

[0109] In some embodiments, the semi-transparent sheet may be substituted by a light sensitive sheet i.e., a sheet able to convert the projected light into visible light (i.e., fluorescent). For example, the sheet may be a polymeric sheet impregnated with a fluorescent dye. A fluorescent light sensitive sheet may be required for calibrating the additive manufacturing system, for example, if the imaging device is not configured to image ultraviolet radiation. In some embodiments, the light sensitive sheet may be configured to scatter UV light instead or in conjunction with its fluorescence capability.

[0110] In some embodiments, positioning a sheet may include replacing a separation membrane (e.g., separation membrane 1160 illustrated in Fig. IB) with the sheet.

[0111] In a further step 310, method 300 may include setting a scaling factor parameter of the projector. In a further step 320, method 300 may include projecting a calibration image onto the semi-transparent sheet. In some embodiments, the pattern of the projected calibration image may be similar and / or identical to the pattern of the calibration target used for calibrating the camera. In a further step 330, method 300 may include capturing the calibration image using the imaging device positioned inside the build chamber. Steps 310, 320 and 330 of scaling factor parameter of the projector, projecting a calibration image, and capturing the calibration image, may be repeated so as to obtain a plurality of captured images, for instance a series of between 5 and 50 images, or between 10 and 20 images.

[0112] In a further step 340, method 300 may include analyzing the plurality of captured calibration images to determine dimensional attributes of the projected calibration pattern for the plurality of scaling factor values. Analyzing the plurality of captured images may include a preliminary step of applying a correction resulting from the camera calibration. Analyzing the plurality of captured calibration images may include a step 345 of computing a set of characteristic points in the plurality of calibration images. That is, finding a set of representative points that may be used for indicating a scale factor. The characteristic points may be, for example, center of mass of the dots included in the grid of dots pattern of the captured calibration images, or vertex points of rectangles of a checkerboard pattern of the captured calibration images. Computing a set of characteristic points may include computing coordinates of the characteristic points with reference to an X axis and a Y axis.

[0113] Analyzing the plurality of captured images may include a further step 347 of computing expected coordinates of the characteristic points, with reference to the X-axis and the Y-axis, based on the scaling factor value of the corresponding calibration image. Computing expected coordinates may use data obtained in the preliminary calibration of the imaging device.

[0114] Figs. 4A-4B show graphs of coordinates of characteristic points, in comparison to coordinates of corresponding expected points, as computed in a test implementation of the method 300. Each graph includes datasets for a scaling factor of 1.1 and a scaling factor of 0.9. Fig. 4A shows a comparison of the X- coordinates, and Fig. 4B shows a comparison of the Y-coordinates.

[0115] Analyzing the plurality of captured images may include a further step 349 of computing, for both axes, a linear regression. The linear regression may be of the coordinates of the characteristic points, based on corresponding expected coordinates. Computing the linear regression may be performed so as to obtain an axial dimensional accuracy with respect to the X-axis and to the Y-axis, for each of the plurality of calibration images. In other words, for each captured image, a linear regression of coordinates of the characteristic points as a function of corresponding expected coordinates is computed, so as to find an actual scaling factor corresponding an expected scaling factor.

[0116] It is noted that if the linear function (obtained from the linear regression) crosses the origin of the axes, processing a single image may be sufficient.

[0117] Method 300 may include a further step 350 of computing a calibration model configured to fit a relationship between the scaling factor parameter and a dimensional accuracy of the projector assessed based on the dimensional attributes. Computing a calibration model may include computing, for both axes, a linear regression of the axial dimensional accuracy based on the scaling factor values of the corresponding calibration images. In other words, computing a linear regression of actual scaling factors as a function of corresponding expected scaling factors.

[0118] Method 300 may include a further step 360 of identifying a scaling factor working point for which dimensional accuracy is optimal, based on the calibration model. Identifying a scaling factor working point for which dimensional accuracy is optimal, based on the calibration model, may include identifying a scaling factor working point with respect to the X-axis such that the axial dimensional accuracy with respect to the X-axis is equal to one. Identifying a scaling factor working point for which dimensional accuracy is optimal, based on the calibration model, may include a scaling factor working point with respect to the Y-axis so that the axial dimensional accuracy with respect to the Y-axis is equal to one. In other words, from the results of the linear regression of measured scaling factors as a function of corresponding expected scaling factors, expected scaling factor values where a measured scaling factor is equal one is computed, for each axis. In yet other words, an expected scaling factor where SF™es, SF™es= 1 is computed.

[0119] Identifying a scaling factor working point for which dimensional accuracy is optimal, based on the calibration model, may include a further step 367 of computing a weighted average of the (axial) scaling factor working point with respect to the X-axis and the (local) scaling factor working point with respect to the Y-axis, so as to obtain a (global) scaling factor working point. In some embodiments, the weighted average may be replaced by a nonlinear function of the scaling factor working point with respect to the X-axis and the scaling factor working point with respect to the Y-axis.

[0120] Fig. 5 shows a graph of actual (measured) scaling factors (axial dimensional accuracy) in comparison to corresponding expected scaling factors, and a linear regression of the actual scaling factors as a function of the corresponding expected scaling factors, as computed in the test implementation of method 300. The graph contains datasets for both the X-axis and the Y-axis. Expected scaling factors where SF™es, SF™es= 1 are indicated by arrows.

[0121] Method 300 may further include a step 370 of applying the identified scaling factor working point to the additive manufacturing system. For example, as indicated hereinabove, applying the identified scaling factor working point may include setting a scale factor value of the additive manufacturing system for either the X-axis, Y-axis, or both.

[0122] Method 300 may further include a step 380 of removing the semi-transparent sheet from the build window, and a step 390 of removing the imaging device 390 from the build chamber, in preparation for manufacturing objects. If the calibration target is not interfering with the semi-transparent sheet (i.e., blocking some of the scattered ultraviolet light), step 308 of removing the calibration target from the build window may be performed together with step 380 of removing the semi-transparent sheet from the build window.

[0123] In some embodiments, method 300 may include a step 395 of verifying the calibration and the axial dimensional accuracy. Verifying the calibration may include: projecting a single calibration image, capturing the single calibration image, and analyzing the captured single calibration image. Step 395 of verifying the calibration may be performed after step 370 of applying the identified scaling factor working point. Method 300 may include a further step of performing additive manufacturing using the calibrated additive manufacturing system.

[0124] Fig- 6 is a graph illustrating verification of axial dimensional accuracy and shows coordinates of characteristic points in comparison to coordinates of corresponding expected points, and a linear regression, as computed in the test implementation of method 300. As shown in Fig. 5, before calibrating, the measured scale factors were about 0.982 and 0.981. As shown in Fig. 6, after calibrating, the measured scale factors were 0.999 and 1.0015 for the Y-axis and the X-axis respectively (slope factors of the regression). The measured scale factors after calibrating are improved (i.e. closer to one) than before calibrating.

[0125] Fig. 7 illustrates exemplary calibration pattern images projected by a projector according to some embodiments of the present disclosure. The exemplary calibration patterns are a checkerboard pattern 700 including a plurality of squares (such as square 710), and a rectangular grid of dots 750. The exemplary calibration patterns images illustrated in Fig. 7 may be projected by a projector before calibration in accordance with the methods presently disclosed. As shown, the projector before calibration may have an actual scale factor value for the X-axis greater than one. That is, for example, squares may be projected as rectangles, and dots (such as dot 760) may be positioned so as to have a distance along the X-axis greater than a distance along the Y-axis. Vertical lines 720, 770, and 780 may facilitate visual perception of the irregularity of the projected calibration pattern images. Vertical lines 720 may indicate measured widths of projected squares. Vertical lines 770 may indicate measured positions (along the X-axis) of projected dots. Vertical lines 780 (dashed) may indicate correct positions (along the X-axis) of projected dots.

[0126] Figs. 8A-8B schematically illustrate positioning of imaging devices, according to embodiments of the present disclosure. Fig. 8A illustrates embodiments in which the imaging device may be fixedly coupled to the build chamber and Fig. 8B illustrates embodiments in which the imaging device may be movably coupled to the build chamber.

[0127] In some embodiments, the mechanical coupling may include a mount. Fig. 8A illustrates an additive manufacturing system 800 that may have a build chamber 810, a projector 820, a build window 825, and a platform track 845 to displace a build platform 840. An imaging device 830 may be mechanically coupled to the build chamber 810 by a mount 835. Mount 835 may include, for example, a frame including a plurality of beams. Mount 835 may be monolithic (i.e., consist of a single piece) or may require assembly (be non-monolithic), e.g., the plurality of beams may include catches or may be mechanically coupled by screws.

[0128] In some embodiments, mount 835 may be integrated with the build chamber 810, i.e., be inseparable from the build chamber 810. In some other embodiments, mount 835 may be configured to be detachable from the build chamber 810. In some embodiments, mount 835 may rest on a frame of the build window 825 (not shown). In some embodiments, mount 835 may rest against walls of the build chamber 810. Mount 835 may include flexible elements (e.g., wave springs or flexible shoulders) in order to apply force on the walls of the build chamber 810, so as to create sufficient friction for holding mount 835.

[0129] In some embodiments, the build chamber may include attachment facilities, configured to attach mount 835 to the build chamber 810. Examples include ridges, notches, threaded holes, and magnets. Mount 835 may include attaching elements configured to match the attachment facilities. Examples include clamps, screws, magnetizable material, and / or magnets.

[0130] In some embodiments, the mechanical coupling may include an actuation system configured to displace the imaging device within the build chamber. Fig. 8B illustrates an additive manufacturing system 850 that may have a build chamber 860, a projector 870, a build window 875, and a platform track 895 to displace a build platform (build platform not shown). An imaging device 880 may be mechanically coupled to build chamber 860 by a track 885. Track 885 may be configured to enable displacement of the imaging device according to a predefined path. In some embodiments, the predefined path may be along an optical axis of imaging device 880 and / or perpendicular to an optical axis of imaging device 880, for example, vertically or horizontally. In some embodiments, the path may be complex, for example, it may include curved segments. In some embodiments, a method for calibrating additive manufacturing system 850 (e.g., methods 200, 300 described hereinabove and / or methods 900, 1000 described hereinbelow) may include moving imaging device 880 so as to position imaging device 880 at a predetermined distance from build window 875 (e.g., so as a surface above the build window 875 onto which images are to be projected is positioned at a plane of focus of imaging device 880).

[0131] In some embodiments, the additive manufacturing system may include an actuation system of the build platform (e.g., a linear actuator such as a track or multiple linear actuators) configured to vertically translate the build platform relative to build window 825 and track 885 may be the actuation system (e.g. the track) 895 of the build platform. In some embodiments, a method for calibrating the additive manufacturing system 850 (e.g., methods 200, 300 described hereinabove and / or methods 900, 1000 described hereinbelow) may include removing the build platform. In other words, a method for calibrating the additive manufacturing system 850 may include replacing the build platform with imaging device 880.

[0132] In some embodiments, the build platform may be mechanically coupled to track 895 through a build arm. A method for calibrating the additive manufacturing system 850 (e.g., methods 200, 300 described hereinabove and / or methods 900, 1000 described hereinbelow) may include attaching the imaging device 880 to the build arm. In some embodiments, a method for calibrating the additive manufacturing system 850 may include removing the build platform from the build arm, e.g., unscrewing a build screw and / or removing a build head, that may attach the build platform to the build arm.

[0133] Uniformity calibration

[0134] Referring to Fig. 9, a flowchart illustrating a broad aspect of a method 900 for calibrating an additive manufacturing system, according to embodiments of the present disclosure, is shown. The additive manufacturing system may include a build chamber and may include a projector. The projector may be configured to project images through a build window positioned in the build chamber. As an example, the additive manufacturing system may be as schematically illustrated by Fig. 1A and / or in Figs. 1B-1C. In examples including a separation membrane over the build window like the ones illustrated in Figs. 1B-1C, the projector may be configured for projecting images through the build window and the separation membrane, i.e., a plane of focus of the projector may coincide with an upper surface of the separation membrane laminated over the build window.

[0135] In a first step 910, method 900 may include projecting an intended (i.e., precalibrated) uniform pattern through the build window onto a semi-transparent sheet. The semi-transparent sheet may be positioned on top of the build window. Projecting the uniform pattern may be performed by setting an identical, predefined, duty cycle for each micromirror of the projector (e.g., via the digital micromirror device [DMD] of the projector). In some embodiments, the identical duty cycle may be set to a maximum (e.g., 100%) so that each micromirror remains in the “on” position on a specific period. In a further step 920, method 900 may include capturing a uniformity calibration image of the uniform pattern projected on the semi-transparent sheet using an imaging device positioned inside the build chamber. The imaging device may be preliminarily calibrated. Furthermore, the mathematical function between UV intensity of the projector (or duty cycle of the micromirrors of the projector) and the camera gray value output may be determined, as described hereinbelow. This mathematical function may be linear or nonlinear. In a further step 930, method 900 may include analyzing the captured uniformity calibration image, to determine a calibration intensity distribution of calibration image pixels. Method 900 may further include a step 940 of computing a correction map, wherein each correction map pixel may be associated with a correction intensity. The correction intensities may be determined using a tunable calibration factor and a calibration intensity of a corresponding calibration image pixel. The tunable calibration factor may depend on attributes of the calibration intensity distribution.

[0136] Method 900 may further include a step 950 of applying the correction map to further images projected by the projector.

[0137] Referring to Fig. 10, a flowchart illustrating a method 1000 for calibrating an additive manufacturing system, according to embodiments of the present disclosure, is shown. The additive manufacturing system may include a build chamber and may include a projector. The projector may be configured to project images on a build window positioned in the build chamber. As an example, the additive manufacturing system may be as schematically illustrated by Fig. 1A and / or in Figs. 1B-1C. In examples including a separation membrane over the build window like the ones illustrated in Figs. 1B-1C, the projector may be configured for projecting images through the build window and the separation membrane i.e., a plane of focus of the projector may coincide with an upper surface of the separation membrane laminated over the build window.

[0138] Method 1000 may include a step 1005 of positioning an imaging device inside the build chamber. The positioning may be so that a surface located directly above the build window, onto which images are to be projected, is positioned at a plane of focus of the imaging device. In some embodiments, method 1000 may include preliminarily calibrating the imaging device by aligning an optical axis of the imaging device and of the projector. In some embodiments, method 1000 may include adjusting a position of the imaging device so that a focal plane of the projector is conjugated on the focal plane of the imaging device. In some embodiments, method 1000 may include orienting the imaging device so that image axes of the imaging device are aligned with the image axes of the projector. In other words, the X axis and Y axis (i.e., axes along the width and height) of an image captured by the imaging device and of an image projected by the projector, may coincide. In yet other words, a mid-height line and a mid-width line, of an image captured by the imaging device, may be optical conjugates of a mid-height line and a mid- width line of an image projected by the projector. Positioning of the imaging device is discussed hereinabove in relation to Figs. 8A-8B.

[0139] The method may further include a step 1007 of positioning a semi-transparent sheet. The semi-transparent sheet may be disposed on the build window. The semi-transparent sheet may be uniform. In some embodiments, the semi-transparent sheet may be Lambertian. That is, the sheet may scatter (i.e., transmit or reflect) ultraviolet light, so that the radiance (power per area per solid angle) emitted from the sheet may be identical for every angle of view (i.e., for every solid angle).

[0140] In some embodiments, the semi-transparent sheet may be substituted by a light sensitive sheet, i.e., a sheet able to convert the projected light into visible light (i.e., fluorescent). For example, the sheet may be a polymeric sheet impregnated with a fluorescent dye. A light sensitive sheet may be required for calibrating the additive manufacturing system, for example if the imaging device is not configured to image ultraviolet radiation. In some embodiments, the light sensitive sheet may be configured to scatter UV light instead or in conjunction with its fluorescence capability.

[0141] In some embodiments, positioning a sheet may include replacing a separation membrane (e.g., separation membrane 1160 illustrated in Fig. IB) with the sheet.

[0142] Method 1000 may further include a step 1010 of projecting a uniform pattern (i.e. pattern intended to be uniform) onto the semi-transparent sheet. Method 1000 may further include a step 1015 of capturing a uniformity calibration image of the uniform pattern using the imaging device.

[0143] In some embodiments, method 1000 may include a step 1017 of calibrating a linearity of the imaging device. Calibrating the linearity of the imaging device may include projecting a plurality of uniform pattern images on the build window and capturing a corresponding plurality of uniform pattern images with the imaging device. Each uniform pattern image may have a different projected intensity scale (i.e., intensity level). Calibrating the linearity of the imaging device may include measuring an average captured intensity for each uniform pattern image, and computing a linear regression based on the corresponding projected intensity scales.

[0144] Calibrating the imaging device may include correcting a linearity of the imaging device. In other words, this may include configuring the imaging device so as to reduce or eliminate nonlinearities in the average captured intensity as a function of the projected intensity scale. In some embodiments, the linearity of the imaging device may be configured so as to have a predefined slope of the average captured intensity as a function of the projected intensity scale. In some embodiments, the linearity of the imaging device may not be correctable, and computations performed in the course of method 1000 may be modified, in order to account for the nonlinearities. For example, the computations may include applying correction coefficients.

[0145] Method 1000 may include analyzing the captured uniformity calibration image, to determine a calibration intensity distribution of calibration image pixels.

[0146] Analyzing the captured uniformity calibration image may include a step 1020 of computing local averages of pixels of the captured uniformity calibration image. Computing local averages may be performed so as to correct defects. Defects may include particles blocking a path of light emitted by the projector (e.g., dust accumulated on an aperture of the imaging device and / or the build window), defects in the semi-transparent sheet (e.g., microscopic cuts and rips), defects in the imaging device (e.g., malfunctioning image sensor elements), and / or defects in the projector (e.g., malfunctioning projector pixels, deformed micromirrors, light source defects). Computing local averages may include, for example, computing a weighted average of matrices of nearby pixels of the captured uniformity calibration image. For example, 3x3 matrices or 5x5 matrices or larger matrices, such as 51x51 matrices. In some embodiments, computing local averages may include applying a low-pass filter (e.g., applying a convolution with a Gaussian or with a sine function). In some embodiments, computing local averages may include applying a median filter. In some embodiments, computing local averages may include applying computerized vision procedures in order to correct defects.

[0147] Analyzing the captured uniformity calibration image may include a step 1025 of downsampling the captured uniformity calibration image. Downsampling the uniformity captured calibration image may be required if the imaging device has a higher spatial resolution than a spatial resolution of the projector. The number of pixels in the captured uniformity calibration image may be higher than the number of projector pixels, requiring computing a mapping (the downsampling) from the captured uniformity calibration image to the calibration image pixels (that correspond to projector pixels). In some embodiments, the downsampling may be obtained from computing local averages 1020.

[0148] In some embodiments, analyzing the captured uniformity calibration image may include a step 1027 of normalizing the calibration intensity distribution according to at least one of a maximum calibration intensity and a minimum calibration intensity. In other words, a maximum captured intensity may be set to a reference maximum value, and / or a minimum captured intensity may be set to a reference minimum value. Normalizing the calibration intensity may further include averaging the reference maximum and minimum values of at least three captured uniformity calibration images.

[0149] In some embodiments, analyzing the captured uniformity calibration image may include a step of filtering the particles from the image. This may be done, for instance, by image processing, wherein defective calibration intensities (e.g. resulting from dust particles) are replaced with local average calibration intensities around them.

[0150] The reference maximum (vis-a-vis, minimum) value may be a maximum (minimum) intensity value obtainable by the imaging device. For example, the imaging device may provide intensity resolution of 8 bits, and the reference maximum and minimum values may be, respectively, 255 and 0. In some embodiments, the imaging device may provide intensity resolution between 8 bits to 16 bits, i.e., the intensity resolution may be any of 9 bits to 16 bits, inclusive. E.g., 9 bits or 12 bits or 16 bits. In an example where the imaging device may provide intensity resolution of 16 bits, the reference maximum and minimum values may be, respectively, 65535 and 0.

[0151] Method 1000 may include a step 1030 of computing a correction map, wherein each correction map pixel may be associated with a correction intensity. The correction intensities may be determined using a tunable calibration factor and a calibration intensity of a corresponding calibration image pixel. The tunable calibration factor may depend on attributes of the calibration intensity distribution.

[0152] Computing a correction map may include a step 1031 of computing a reciprocal distribution. The reciprocal distribution is a distribution of reciprocal values of the calibration intensity distribution. In other words, the reciprocal distribution is an image where each pixel’s value is a reciprocal value of a corresponding calibration image pixel. Computing a correction map may include a step 1032 of computing an average p and a standard deviation c of the reciprocal distribution.

[0153] The tunable calibration factor may be a sum of the average of the reciprocal distribution, and of the standard deviation of the standard deviation of the reciprocal distribution scaled. In other words, the tunable calibration factor may be p + Xo wherein X is a tunable standard deviation multiplier. In some embodiments, the range for the tunable standard deviation multiplier X may include negative values.

[0154] Computing a correction map may include a step 1033 of dividing values of the reciprocal distribution by the tunable calibration factor. Computing a correction map may include a step 1034 of multiplying the reciprocal distribution by a reference maximum value. Computing a correction map may include a step 1035 of rounding to a nearest integer, so as to obtain the correction map. The reference maximum value may be as described herein above.

[0155] Computing a correction map may include a step 1036 of clipping values of the reciprocal distribution. That is, a value of the reciprocal distribution, that may be higher than the reference maximum value, may be set to the reference maximum value. In other words, values of the reciprocal distribution may saturate at the reference maximum value. In yet other words, in terms of the calibration intensity distribution, calibration image pixels corresponding to a calibration intensity higher than a predefined threshold, may be associated with a correction intensity that is lower than an uncorrected intensity.

[0156] Method 1000 may include a step 1040 of applying the correction map to further images projected by the projector. Applying the correction map may include changing a duty cycle of projector micromirrors pixels (e.g., from 100% to 90%). In some embodiments, applying the correction map may include setting intensity values of projector pixels to corresponding intensity values of the correction map. In some other embodiments, applying the correction map may include dividing the correction map by the reference maximum value. The correction map may thus represent a (dimensionless) coefficient map. Applying the correction map may include multiplying intensity values of projector pixels by corresponding values of the correction map.

[0157] In some embodiments, method 1000 may include a step 1043 of verifying the uniformity calibration, in other words, verifying the uniformity of light projected by the projector. Verifying the uniformity calibration may include projecting a uniform pattern on the build window, may include capturing a uniformity calibration image, and may include analyzing the captured uniformity calibration image. Verifying the calibration may be performed after applying the correction map. In some embodiments, method 1000 may include a step 1045 of indicating a presence of bad projector pixels, that is, reporting a presence of malfunctioning projector pixels. In some embodiments, indicating a presence of bad projector pixels may use computation results obtained from computing local averages.

[0158] In some embodiments, method 1000 may include a step 1037 of optimizing the tunable calibration factor. That is, computing a tunable calibration factor that may best suit specific applications (i.e., specific manufacturing tasks). In other words, method 1000 may include finely adjusting the tunable calibration factor. Optimizing the tunable calibration factor may include reducing a cost function below a predefined threshold, and / or increasing a target function above a predefined threshold. In some embodiments, optimizing the tunable calibration factor may include reducing a standard deviation of the calibration intensity distribution to below a predefined standard deviation threshold, and keeping an average of the calibration intensity distribution above a predefined average threshold. An example of a cost function may be a standard deviation of the calibration intensity distribution, and an example of a target function may be the average of the calibration intensity distribution. That is, in some embodiments, optimizing the tunable calibration factor may include reducing a standard deviation of the calibration intensity distribution to below a predefined standard deviation threshold, and keeping an average of the calibration intensity distribution above a predefined average threshold. In other words, a standard deviation of an intensity map captured after performing calibration, may be lower than a predefined threshold. An average of the intensity map captured after performing calibration, may be higher than a predefined threshold.

[0159] Generally, any manufacturing parameters may be taken into account for the cost function and / or the target function. Examples of manufacturing parameters may include uniformity of projected images, intensity of projected images, power consumption of the additive manufacturing system, lifetime of the projector, available intensity range of projected images, and ability of curing of details finer than a projector pixel. As an example, a cost function may be power consumption of the additive manufacturing system or may be the available intensity range of projected images.

[0160] The advantages of finely adjusting the adjustable calibration setting are as follows: To achieve uniformity for all projected images, a higher standard deviation multiplier X may be preferable. On the other hand, production considerations might benefit from a lower standard deviation multiplier X value. Production considerations, that might benefit from a lower standard deviation multiplier X, may include intensity of projected images, power consumption of the additive manufacturing system, lifetime of the projector, available intensity range of projected images, and ability of curing of details finer than a projector pixel. Therefore, fine-tuning the calibration setting may help achieve a good balance between various production requirements.

[0161] In some embodiments, optimizing the tunable calibration factor may include optimizing high moments of the calibration intensity distribution, for example reducing a skewness (third central moment) of the calibration intensity distribution.

[0162] In some embodiments, optimizing the tunable calibration factor may include interpolating a plurality of verification measurements. That is, the standard deviation multiplier X may be set to different values. For each value of the standard deviation multiplier X, a uniform image may be projected, captured, and analyzed. The optimal value of the standard deviation multiplier X may be computed according to an interpolation (function fitting) of values obtained from analyzing the images.

[0163] In some embodiments, optimizing the tunable calibration factor may include computing a plurality of correction maps configured for providing optimal uniformity for different manufacturing parameters. In other words, method 1000 may include a step 1038 of compiling a library or a database of correction maps, for a plurality of manufacturing parameters.

[0164] In some embodiments, method 1000 may include selecting a correction map based on attributes of an object to be manufactured so as to provide optimal uniformity. In other words, given attributes of an object to be manufactured, a correction map may be computed or selected from a library / database, so as to provide optimal uniformity for manufacturing the object. Attributes of the object may include, for example, a size of the object, a resin from which the object may be manufactured, infill ratio, tolerances, and / or desired mechanical properties. It is noted that selecting a correction map may not necessarily require compiling a library or a database (i.e., step 1038). Method 1000 may include a step 1050 of removing the semi-transparent sheet from the build window. Method 1000 may include a step 1055 of removing the imaging device from the build chamber, in preparation for manufacturing objects.

[0165] In some embodiments, method 1000 may include calibrating a geometry of the imaging device. Calibrating a geometry of the imaging device may be performed according to the calibration of the imaging device as described hereinabove in relation to method 300. In summary, calibrating a geometry of the imaging device may include replacing (if needed) a build platform with the imaging device, and positioning the imaging device inside the build chamber, at a predetermined distance from the build window. It is noted that the positioning of the imaging device for the geometrical calibration may fulfil the step of positioning an imaging device (i.e., as described hereinabove in relation to step 1005) for the uniformity calibration. Calibrating a geometry of the imaging device may include disposing a calibration target comprising a calibration pattern on the build window and imaging the calibration target using the imaging device, so as to obtain an image of the calibration target. Calibrating a geometry of the imaging device may include correcting any one of extrinsic and intrinsic parameters of the imaging device according to the image of the calibration target. Calibrating a geometry of the imaging device may include removing the calibration target and disposing a semi-transparent sheet on the build window.

[0166] Figs. 11-14 show results of test implementation of method 1000.

[0167] Fig. 11 shows an example of intensity images and histograms for an additive manufacturing system, when uncalibrated and when calibrated. One intensity map was captured when the additive manufacturing system was uncalibrated, and one intensity map was captured when the additive manufacturing system was calibrated.

[0168] An intensity image when uncalibrated 1710 is divided between four regions 1712, 1714, 1716, and 1718. Pixels in each region 1712, 1714, 1716, and 1718 has, respectively, an intensity of about 0.875, 0.825, 0.775, and 0.725. An intensity image when calibrated 1720 is divided between two regions 1724 and 1726. Pixels in each region 1724 and 1726 has, respectively, an intensity range of 0.775, and 0.725. An area (that corresponds to a number of pixels) between regions represents pixels having an intensity being intermediate between intensities that correspond to two adjacent regions. An area (that corresponds to a number of pixels) included in region 1726 is larger than a total area included in the four principal regions 1712, 1714, 1716, and 1718. An area included in region 1726 is about the same as the area included in region 1714. Region 1714 includes less area than region 1724. The intensity map when calibrated 1720 is divided between less regions than intensity map when uncalibrated 1250. Further, there is less area between regions.

[0169] A histogram 1730 shows for each intensity (“gray value”), the number of pixels having this intensity. The histogram 1730 includes a data set corresponding to the intensity image when uncalibrated 1733, and a data set corresponding to the intensity image when calibrated 1735. The data set corresponding to the intensity map when calibrated 1735 has a number of intensity values, that correspond to a nonzero number of pixels, less than the data set corresponding to the intensity map when uncalibrated 1733. In other words, a standard deviation of the data set corresponding to the intensity map when uncalibrated 1733, is less than a standard deviation of the data set corresponding to the intensity map when uncalibrated 1733.

[0170] As indicated by the intensity images 1710, 1720 and the histogram 1730, the uniformity of images projected by the uncalibrated additive manufacturing system is low, and may be insufficient for manufacturing. Conversely, uniformity of images projected by the calibrated additive manufacturing system is high, and may be sufficient for manufacturing.

[0171] Fig. 12 shows calibrated intensity histograms corresponding to calibration according to different tunable calibration factors. The tunable calibration factors were of the form / r + ACT as described hereinabove. Values for the standard deviation multiplier were X = -0.5, 0.5, 1, 2, 3, 5, 7, 10.

[0172] Fig. 13A shows a scatter graph of a standard deviation of calibrated intensity, as a function of the standard deviation multiplier X. A dashed line, representing a fit of the data set for a function, is illustrated.

[0173] The standard deviation decreases in proportion to a fourth power of the standard deviation multiplier X, shifted by 9.26. It is noted that for X = 0.5, the histograms illustrated in Fig. 12 have the highest peak (largest number of projector pixels having the same intensity). The corresponding standard deviation is, however, not the minimal for X = 0.5, as the distribution is more skewed (larger third central moment) than for higher values of X.

[0174] Fig. 13B shows a scatter graph of an average of calibrated intensity, as a function of the standard deviation. A dashed line, representing a fit of the data set for a function, is illustrated. The average increases, roughly, logarithmically, with increase in the standard deviation.

[0175] Information obtained from the histograms, graphs, and fits, can be used, for example, to find an optimal tunable calibration factor, in order to e.g., efficiently manufacture objects and / or manufacture objects of high quality. For example, Figs. 13A- 13B provide functions governing relations between the standard deviation multiplier X, the average value of the calibrated intensity, and the calibrated standard deviation. The optimization may include solving equations that include these functions, so as to provide an optimal value for the tunable calibration factor.

[0176] Fig. 14 shows a graph illustrating calibrating the imaging device 1090. Shown in the graph are average captured intensities as a function of the projected intensity scales, and a linear regression. The coefficient of correlation is near unity, and thus applying corrections to the imaging device may not be required.

[0177] Figs. 15-16B show results of a test implementation of method 1000, in comparison to methods known in the art.

[0178] Fig. 15 shows intensity images 1501, 1502, histograms 1503, 1504, and projector pixel values in cut-throughs, through the middle of the intensity images 1501, 1502, along the X-axis 1505, 1506 and the Y-axis 1507, 1508. In other words, the cut-throughs 1505, 1506, 1507, and 1508 show values of projector pixels that are positioned in the mid-height or the mid-width of the intensity images 1501, 1502.

[0179] Intensity image 1501, histogram 1503, and cut-throughs 1505, 1507 correspond to calibrating the additive manufacturing system according to method 1000, whereas intensity image 1502, histogram 1504, and cut-throughs 1506, 1508 correspond to calibrating the additive manufacturing system according to a method known in the art. In other words, the left column of sub-figures corresponds to calibrating an additive manufacturing system according to method 1000, whereas the right column of sub-figures corresponds to calibrating the additive manufacturing system according to a method known in the art. Intensity image 1501 is uniform (except minor, marginal regions), whereas intensity image 1502 has a plurality of regions of different intensities. Correspondingly, histogram 1503 is narrower than histogram 1504. Further, cut-throughs 1505, 1507 show much less variation than cut-throughs 1506, 1508. Thus, the uniformity obtained by calibrating the additive manufacturing system according to method 1000 is better than the uniformity obtained by calibrating the additive manufacturing system according to a method known in the art.

[0180] Quantitative information is arranged in Table 1. Except if noted otherwise, the values are given according to normalization of the intensity of the projector pixels to a range of 0 to 255. The standard deviation of the histogram was required to be about 2.5. The standard deviation multiplier found to fulfil the requirement, was found to be X = 1.42.

[0181] Table 1

[0182] The information arranged in Table 1 shows an advantage of method 1000 over the methods known in the art, in all performance metrics. Further, the time required for performing calibration according to method 1000 was about 5 minutes, whereas the time required for performing calibration according to methods known in the art was about 50 minutes. Thus, method 1000 required only about 10% of the time required for performing calibration according to methods known in the art. Therefore, method 1000 may provide a substantial reduction in time required for performing calibration, implying reduced maintenance costs and time the additive manufacturing system is not operative. Further advantages include better accuracy (all the projector pixels are mapped), the ability to control intensity and uniformity so as to fit different materials, and usage of cheaper equipment.

[0183] Fig. 16A shows verification of calibration according to method 1000. The verification is of the calibration illustrated in Fig. 15 A scatter graph of a standard deviation of calibrated intensity, as a function of the standard deviation multiplier X, is shown. One dataset is for measured values (black, circular markers), and one dataset is for computed values (gray, triangular markers). Deviations between the datasets are small (after normalization, less than half a pixel), thus the method 1000 is verified to be reliable.

[0184] Fig. 16B shows a scatter graph of computed average calibrated intensity, as a function of the standard deviation multiplier X (method 1000). The intensity is given as a percentage of the intensity for X = 1.42. A dashed line, representing a fit of the dataset for a function, is illustrated. The computed average calibrated intensity decreases linearly with the standard deviation multiplier X.

[0185] Methods for calibrating an additive manufacturing system, according to the present disclosure, may combine scaling factor calibration and uniformity calibration. For example, a method may include performing any one of methods 200 or 300, and performing any one of methods 900 or 1000.

[0186] In some embodiments, focus calibration may additionally be performed. Generally, predefined images may be projected on the sheet, may be captured by the imaging device, and / or may be processed, so as to determine whether the focus is properly set, i.e., that the projector is focused on the build window. In other words, a deviation of a focal plane of the projector, from a design focal plane (i.e., the build window), may be computed according to the projected images. For example, an average sharpness of a projected image may be measured, and / or distortion of predefined shapes may be measured.

[0187] Referring to Fig. 17, a flowchart illustrating a broad aspect of a method 2000 for calibrating an additive manufacturing system, according to embodiments of the present disclosure, is shown. The method 2000 may be for detecting malfunctioning projector pixels.

[0188] The additive manufacturing system may include a build chamber and may include a projector. The projector may be configured to project images through a build window positioned in the build chamber. As an example, the additive manufacturing system may be as schematically illustrated by Fig. 1A and / or in Figs. 1B-1C. In examples including a separation membrane over the build window like the ones illustrated in Figs. 1B-1C, the projector may be configured for projecting images through the build window and the separation membrane, i.e., a plane of focus of the projector may coincide with an upper surface of the separation membrane laminated over the build window.

[0189] Generally, when a projector pixel is malfunctioning, the associated micromirror seizes, and stays in the “on” state. In other words, a malfunctioning projector pixel uncontrollably provides 100% illumination.

[0190] The method 2000 may include a step 2200 of projecting an image by the projector. The image may be projected onto a semi-transparent sheet positioned on the build window.

[0191] The method 2000 may include a step 2300 of capturing the projected image by an imaging device, so as to obtain a captured image.

[0192] The method 2000 may include a step 2400 of processing the captured image, so as to indicate malfunctioning projector pixels. In other words, step 2400 may include analyzing intensity of pixels included in the captured image to determine malfunctioning projector pixels. Examples of processing the captured image may include, but not limited to: computing the ANSI contrast ratio, and counting a number of malfunctioning projector pixels according to a number of pixels in the captured image being brighter than a threshold value.

[0193] In some embodiments, the method 2000 may include a step 2100 of darkening the additive manufacturing system. The step 2100 may include providing a dark environment so as to enable capturing high-quality images of images projected by the projector. In other words, the step 2100 may be performed so as to enable capturing images having high signal -to-noise ratio (SNR), of images projected by the projector.

[0194] In some embodiments, step 2100 may include positioning the additive manufacturing system in a dark room. In some embodiments, step 2100 may include covering the additive manufacturing system in an opaque cover (e.g., a dark fabric sheet, an opaque box).

[0195] It is noted that depending on the additive manufacturing system and / or on the imaging device, step 2100 may not be included in the method 2000.

[0196] In some embodiments, the image projected by the projector may be a dark image. That is, an image ideally being completely dark. In other words, an image of no light. In some embodiments, the image projected by the projector may include dark portions, i.e., only a part of the image projected by the projector may be dark.

[0197] In some embodiments, the method 2000 may include any one of: a step 2050 of positioning the semi-transparent sheet on the build window, a step 2070 of positioning the imaging device in the build window, a step 2450 of removing the semi-transparent sheet from the build window, and / or a step 2470 of removing the imaging device from the build window.

[0198] In some embodiments, the imaging device may be mechanically coupled to a build arm, as described hereinabove.

[0199] Method 2000 may be incorporated as parts of maintenance procedures for additive manufacturing systems. For example, the maintenance procedures may include performing the method 2000, and replacing the projector according to a number of projector pixels that may be found malfunctioning (e.g., if more than 10 projector pixels may be found malfunctioning). In some embodiments, the projector may be replaced according to a local density of malfunctioning projector pixels. The local density may be defined as a number of malfunctioning projector pixels per a predefined range of projector pixels, e.g., any 5 malfunctioning pixels per a 5-by-5 square of adjacent pixels.

[0200] Additive manufacturing systems may be configured to implement methods according to the present disclosure. A controller of the additive manufacturing system may be configured to communicate with an imaging device, so as to implement methods according to the present disclosure. For example, the additive manufacturing system may have a communication port for communicating with an imaging device. The controller may be configured to provide instructions, and receive captured images, from the imaging device. The controller may be configured to communicate with a user, that is, to receive instructions from the user (e.g., to perform specific calibration, to capture images), and to provide indications to the user (e.g., that an imaging device may be connected, that the calibration target may be disposed on the build window, or that an error has occurred). In some embodiments, the controller may be configured to process received captured images. In some other embodiments, the controller may be configured to communicate with a remote computer in order to communicate with a user, communicate with the imaging device, and / or to process received captured images.

[0201] Some generalizations may be described. In some embodiments, the build window may include only a diaphragm and / or a membrane. In other words, a rigid window (e.g., made of a glass) may be absent. In performing methods according to the present disclosure (e.g., methods 200, 300, 900, 1000), a calibration target and / or a semi-transparent sheet, may be positioned on the diaphragm or the membrane.

[0202] Generally, a semi-transparent sheet used in performing methods according to the present disclosure (e.g., methods 200, 300, 900, 1000), may not necessarily be a non-rigid film of material. In other words, in some embodiments, the semi-transparent sheet may be a slab of a rigid material, for example, a slab of a scintillating crystal, or a plate of a rigid polymer impregnated with a fluorescent ink.

[0203] Generally, an additive manufacturing system, that methods according to the present disclosure (e.g., methods 200, 300, 900, 1000) may be applicable to, may include an imageforming light source that may be distinct from a projector. In other words, in some embodiments, the images of light (light patterns), that the additive manufacturing system may apply to a light-curable resin, may be produced by a source that is distinct from a projector. For example, an LCD display and / or an L.E.D. array.

[0204] Methods according to the present disclosure may, in some settings / systems, be applied without modifications. In other words, changes implied by a type of the imageforming light source, may relate to underlying physical principles, that may be transparent (to an implementor of the method, such as a technician) when implementing the methods. For example, an intensity of a pixel may be controlled by a voltage applied to an L.E.D. included in the L.E.D. array, instead of applying pulse-width modulation to a micromirror (micromirrors may be absent). However, from the implementor’s point of view, there may be no difference in implementing the method, between an additive manufacturing system having a projector and an additive manufacturing system having an L.E.D. array. Having described and illustrated the principles of the disclosed technology with reference to the illustrated embodiments, it will be recognized that the illustrated embodiments can be modified in arrangement and detail without departing from such principles. Technologies from any example can be combined with the technologies described in any one or more of the other examples. It will be appreciated that procedures and functions such as those described with reference to the illustrated examples can be implemented in a single hardware or software module, or separate modules can be provided. The arrangements above are provided for convenient illustration, and other arrangements can be used.

Claims

CLAIMS:

1. A method for calibrating a digital light processing additive manufacturing system comprising a build chamber and a projector configured to project images through a build window in the build chamber, the method comprising:(a) projecting a plurality of calibration images of a predefined calibration pattern by varying a scaling factor parameter of the projector over a plurality of scaling factor values within a scaling factor calibration range, wherein said plurality of calibration images are projected onto a semi-transparent sheet positioned on the build window;(b) capturing said plurality of calibration images using an imaging device;(c) analyzing the plurality of captured images to determine dimensional attributes of the projected calibration pattern for said plurality of scaling factor values;(d) computing a calibration model configured to fit a relationship between the scaling factor parameter and a dimensional accuracy of the projector assessed based on said dimensional attributes;(e) identifying a scaling factor working point for which dimensional accuracy is optimal based on said calibration model;(f) applying the identified scaling factor working point to the additive manufacturing system.

2. The method according to claim 1 , comprising preliminarily calibrating said imaging device.

3. The method according to claim 2, wherein preliminarily calibrating said imaging device comprises:(a) disposing a calibration sheet comprising a calibration pattern on said build window;(b) replacing a build platform of the additive manufacturing system with the imaging device and positioning the imaging device inside of said build chamber at a predetermined distance from the build window so that a plane of focus of the imaging device coincides with the calibration sheet;(c) imaging said calibration sheet so as to obtain an image of said calibration sheet;(d) correcting any one of extrinsic and intrinsic parameters of the imaging device according to said image of said calibration sheet;(e) removing said calibration sheet.

4. The method according to claim 3, wherein said calibrating said imaging device comprises correcting optical aberrations.

5. The method according to any one of the preceding claims, wherein said scaling factor calibration range, of the projector, is 0.85 to 1.15.

6. The method according to any one of the preceding claims, wherein said calibration pattern comprises any of a checkerboard and a rectangular dot grid.

7. The method according to any one of the preceding claims, wherein said analyzing the plurality of captured images to determine dimensional attributes of the projected calibration pattern for said plurality of scaling factor values includes computing a set of characteristic points in said plurality of calibration images.

8. The method according to claim 7, further comprising, for said plurality of calibration images:(a) computing coordinates of said characteristic points with reference to a X axis and a Y axis;(b) computing expected coordinates of said characteristic points with reference to the X-axis and the Y-axis based on the scaling factor value of the corresponding calibration image;(c) computing for both axes, a linear regression of the coordinates of said characteristic points, based on corresponding expected coordinates so as to obtain an axial dimensional accuracy with respect to the X-axis and to the Y- axis for each of said plurality of calibration images.

9. The method according to claim 8, wherein:(a) computing a calibration model comprises computing, for both axes, a linear regression of the axial dimensional accuracy based on the scaling factor values of the corresponding calibration images;(b) identifying a scaling factor working point for which dimensional accuracy is optimal based on said calibration model, which comprises:i. identifying a scaling factor working point with respect to the X-axis such that the axial dimensional accuracy with respect to the X-axis is equal to one, and a scaling factor working point with respect to the Y-axis, so that the axial dimensional accuracy with respect to the Y-axis is equal to one; ii. computing a weighted average of said scaling factor working point with respect to the X-axis and a scaling factor working point with respect to the Y-axis, so as to obtain said scaling factor working point.

10. The method according to any one of the preceding claims, wherein said imaging device is mechanically coupled to a build arm.

11. The method according to claim 10, wherein said mechanical coupling comprises a track configured for enabling displacement of said imaging device according to a predefined path.

12. The method according to claim 11, wherein said predefined path is along an optical axis of said imaging device or perpendicular to an optical axis of said imaging device.

13. The method according to claim 12, wherein said track is a track of a build platform, and wherein the method comprises removing said build platform.

14. The method according to claim 10, wherein said mechanical coupling comprises a mount.

15. The method according to claim 14, wherein said mount is configured to be detachable from said build chamber.

16. A method for calibrating a digital light processing additive manufacturing system comprising a build chamber and a projector configured to project images on a build window in the build chamber, the method comprising:(a) projecting a uniform pattern onto a semi-transparent sheet, wherein a duty cycle of each micromirror of the projector is set to an identical predefined value;(b) capturing a uniformity calibration image of said uniform pattern projected on the semi-transparent sheet using an imaging device;(c) analyzing the captured uniformity calibration image to determine a calibration intensity distribution of calibration image pixels;(d) computing a correction map, wherein each correction map pixel is associated with a correction intensity determined using a tunable calibration factor and a calibration intensity of a corresponding calibration image pixel, wherein said tunable calibration factor depends on attributes of the calibration intensity distribution;(e) applying said correction map to further images projected by the projector.

17. The method according to claim 16, comprising preliminary steps of calibrating the imaging device, comprising:(a) disposing a calibration sheet comprising a calibration pattern on said build window;(b) replacing a build platform of the additive manufacturing system with the imaging device and positioning the imaging device inside of said build chamber at a predetermined distance from the build window so that a plane of focus of the imaging device coincides with the calibration sheet;(c) imaging said calibration sheet using said imaging device so as to obtain an image of said calibration sheet;(d) correcting any one of extrinsic and intrinsic parameters of the imaging device according to said image of said calibration sheet;(e) removing said calibration sheet.

18. The method according to any one of claims 16 to 17, wherein calibration image pixels corresponding to a calibration intensity higher than a predefined threshold, are associated with a correction intensity lower than an uncorrected intensity.

19. The method according to any one of claims 16 to 18, wherein said computing a correction map comprises:(a) computing a reciprocal distribution being a distribution of reciprocal values of said calibration intensity distribution, and an average / r and a standard deviation a of said reciprocal distribution;(b) dividing values of said reciprocal distribution by said tunable calibration factor being / r + Xa wherein X is a tunable standard variation multiplier;(c) multiplying said reciprocal distribution by a reference maximum value, rounding to a nearest integer, and clipping according to said reference maximum value, so as to obtain said correction map.

20. The method according to any one of claims 16 to 19, comprising normalizing said calibration intensity distribution according to at least one of a maximum calibration intensity and a minimum calibration intensity.

21. The method according to any one of claims 16 to 20, comprising optimizing said tunable calibration factor.

22. The method according to claim 21, wherein said optimizing comprises reducing a standard deviation of said calibration intensity distribution to below a predefined standard deviation threshold, and keeping an average of said calibration intensity distribution above a predefined average threshold.

23. The method according to any one of claims 21 to 22, wherein said optimizing comprises interpolating a plurality of verification measurements.

24. The method according to any one of claims 16 to 23, comprising computing local averages of pixels of said captured calibration image, so as to correct defects.

25. The method according to any one of claims 16 to 24, comprising calibrating linearity of said imaging device.

26. The method according to any one of claims 16 to 25, wherein an intensity resolution of said imaging device is between 8 and 16 bits.

27. The method according to any one of claims 16 to 26, comprising indication of malfunctioning projector pixels or particles blocking a path of light emitted by the projector.

28. The method according to any one of claims 16 to 27, wherein a spatial resolution of said imaging device is higher than a spatial resolution of said projector, and wherein the method comprises downsampling said captured calibration image.

29. The method according to any one of claims 16 to 28, wherein said imaging device is mechanically coupled to said build chamber.

30. The method according to claim 29, wherein said mechanical coupling comprises a track configured for enabling displacement of said imaging device according to a predefined path.

31. The method according to claim 30, wherein said predefined path is along an optical axis of said imaging device, or perpendicular to an optical axis of said imaging device.

32. The method according to claim 31, wherein said track is a track of a build platform, and wherein the method comprises removing said build platform.

33. The method according to claim 29, wherein said mechanical coupling comprises a mount.

34. The method according to claim 33, wherein said mount is configured to be detachable from said build chamber.

35. The method according to any one of claims 16 to 34, comprising computing a plurality of correction map for various values of the tunable calibration factor and identifying an optimal correction map based on manufacturing parameters of an object to be manufactured.

36. The method according to claim 35, comprising selecting the optimal correction map for manufacturing said object.

37. The method according to any one of claims 34 to 36, wherein said manufacturing parameters comprise any of power consumption of the additive manufacturing system, lifetime of the projector, available intensity range of projected images, and ability of curing of details finer than a projector pixel.

38. The method according to any one of claims 16 to 37, wherein applying said correction map comprises changing a duty cycle of projector pixels.

39. A method for calibrating an additive manufacturing system comprising a build chamber and a projector configured to project images on a build window in the build chamber, the method comprising:(a) performing the method according to any one of claims 1 to 15, so as to obtain scaling factor calibration; and(b) performing the method according to any one of claims 16 to 38, so as to obtain uniformity calibration.

40. The method according to claim 39, comprising focus calibration.

41. A method for calibrating a digital light processing additive manufacturing system comprising a build chamber and a projector configured to project images through a build window in the build chamber, the method comprising:(a) projecting an image by the projector onto a semi-transparent sheet positioned on the build window;(b) capturing said image by an imaging device, so as to obtain a captured image;(c) analyzing intensity of pixels included in said captured image to determine malfunctioning projector pixels.

42. The method according to claim 41, wherein said image is a dark image.

43. The method according to any one of claims 41 to 42, comprising darkening said additive manufacturing system.

44. The method according to any one of claims 41 to 43, wherein said imaging device is mechanically coupled to a build arm.

45. A maintenance protocol for an additive manufacturing system, the protocol comprises:(a) performing a calibration method according to any one of claims 41 to 44, so as to determine any one of: a number of malfunctioning projector pixels, and a local density of malfunctioning projector pixels; and(b) replacing said projector according to any one of: said number of malfunctioning projector pixels, and said local density of malfunctioning projector pixels.

46. An additive manufacturing system, comprising a controller configured to communicate with an imaging device, said controller comprising a non-transitory controller readable storage medium storing controller instructions, wherein the controller instructions are used for causing the controller to execute the method according to any one of the preceding claims.

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