WO2023059313A1

WO2023059313A1 - Hole size determination

Info

Publication number: WO2023059313A1
Application number: PCT/US2021/053516
Authority: WO
Inventors: Nathan Moroney; Ingeborg Tastl
Original assignee: Hewlett-Packard Development Company, L.P.
Priority date: 2021-10-05
Filing date: 2021-10-05
Publication date: 2023-04-13

Abstract

In one example in accordance with the present disclosure, a system is described. An example system includes a light sensor to measure a luminance of a 3D printed object with light projected behind the 3D printed object. The example system also includes a processor and a memory communicatively coupled to the processor. The memory stores executable instructions that when executed cause the processor to determine an average size of holes for a region of the 3D printed object based on the luminance measurement.

Description

HOLE SIZE DETERMINATION

BACKGROUND

[0001] Electronic technology has advanced to become virtually ubiquitous in society and has been used to enhance many activities in society. For example, electronic devices are used to perform a variety of tasks, including work activities, communication, research, and entertainment. Different varieties of electronic circuits may be utilized to provide different varieties of electronic technology.

BRIEF DESCRIPTION OF THE DRAWINGS

[0002] The accompanying drawings illustrate various examples of the principles described herein and are part of the specification. The illustrated examples are given merely for illustration, and do not limit the scope of the claims.

[0003] Fig. 1 is a block diagram of a system to determine an average size of holes for a region of a 3D printed object based on a luminance measurement, according to an example.

[0004] Fig. 2 illustrates a hexagonal packing of holes, according to an example.

[0005] Fig. 3 illustrates a square packing of holes, according to an example. [0006] Fig. 4 illustrates calibration data for a number of calibration samples, according to an example.

[0007] Fig. 5 illustrates calibration data and a fitted curve for a number of calibration samples, according to an example. [0008] Fig. 6 is a flow diagram illustrating a method for determining an average size of holes for a region of a 3D printed object based on a luminance measurement, according to an example.

[0009] Fig. 7 depicts a non-transitory machine-readable storage medium for determining an average size of holes for a region of a 3D printed object based on a luminance measurement, according to an example.

[0010] Throughout the drawings, identical reference numbers designate similar, but not necessarily identical, elements. The figures are not necessarily to scale, and the size of some parts may be exaggerated to more clearly illustrate the example shown. Moreover, the drawings provide examples and/or implementations consistent with the description; however, the description is not limited to the examples and/or implementations provided in the drawings.

DETAILED DESCRIPTION

[0011] Electronic devices may include memory resources and processing resources to perform computing tasks. For example, memory resources may include volatile memory (e.g., random access memory (RAM)) and non-volatile memory (e.g., read-only memory (ROM), data storage devices (e.g., hard drives, solid-state devices (SSDs), etc.) to store data and instructions. In some examples, processing resources may include circuitry to execute instructions. Examples of processing resources include a central processing unit (CPU), a graphics processing unit (GPU), or other hardware device that executes instructions, such as an application specific integrated circuit (ASIC).

[0012] An electronic device may be a device that includes electronic circuitry. For instance, an electronic device may include integrated circuitry (e.g., transistors, digital logic, semiconductor technology, etc.). Examples of electronic devices include computing devices, workstations, servers, laptop computers, desktop computers, smartphones, tablet devices, wireless communication devices, testing equipment, sensors, additive manufacturing devices, printing devices, smart appliances, robots, etc. [0013] In some examples, electronic devices may be used for estimating geometric properties of objects using transmitted light measurements. For example, a three-dimensional (3D) printed object may be generated with very fine features such as a set of holes, apertures, or perforations. These openings are referred to herein as “holes.” At high densities these features allow light to pass through the 3D printed object.

[0014] In some examples, the 3D printed object may have a given arrangement of holes. For example, a 3D printed object may have a number of holes arranged in a rectangular pattern, a hexagonal pattern, a random pattern (e.g., white noise) or other arrangement.

[0015] In some examples, a large 3D printed object may be covered with sub-millimeter perforations. In this case, the 3D printed object may have tens of thousands of holes. These holes may be used to achieve functional characteristics, such as flow rates for blended paper pulp mold. Printing these types of parts with 3D printers can be an efficient process in terms of costs, customizability, speed, etc. However, for quality control, modeling and other purposes, the geometric properties of the printed holes may be informative. For example, in an example scenario, some 3D print processes may include additional materials, such as support structures or surrounding powder, which may obstruct the printed holes. Thus, the performance of a 3D printed object may be enhanced by determining whether all of the holes of 3D printed object part have been cleared of any excess material.

[0016] In another example scenario, the printed geometric properties of these holes may be quantified for part inspection and process control purposes. For example, determining whether the average hole size is greater than, less than or equal to specified hole sizes may assist in verifying that a 3D printed object conforms to specified hole parameters.

[0017] The examples described in this specification are provided for determining the average hole size of a 3D printed object in an efficient manner using optical measurements. For a given 3D printed object, the geometric properties of these holes may be quickly and accurately assessed based on an optical measurement. For example, using optical measurements, holes in a 3D printed part may be determined to be present or determined to be filled with material (e.g., powder). If the holes exist, the examples described herein may be used to determine an average hole size (e.g., diameter) for a given region of the 3D printed object.

[0018] In some examples, transmission measurements, such as luminance, luminance factor, or lightness (referred to collectively herein as “luminance”) may provide a fast and accurate estimation of hole properties for a 3D printed object. For well-formed holes, a monotonic relationship exists between the measured optical properties of the 3D printed object and the hole diameters of the 3D printed object. For filled holes, there is a corresponding reduction in optical properties relative to a baseline relationship.

[0019] The present specification describes examples of a system. The system includes a light sensor to measure a luminance of a 3D printed object with light projected behind the 3D printed object. The system also includes a processor and a memory communicatively coupled to the processor. The memory stores executable instructions that when executed cause the processor to determine an average size of holes for a region of the 3D printed object based on the luminance measurement.

[0020] In another example, the present specification also describes a method that includes measuring the luminance of a 3D printed object with light projected behind the 3D printed object. The method also includes determining an average size of holes for a region of the 3D printed object based on the luminance measurement and calibration data.

[0021] In yet another example, the present specification also describes a non-transitory computer-readable storage medium comprising instructions executable by a processor to receive, from a light sensor, a luminance measurement of a 3D printed object with light projected behind the 3D printed object. The instructions are also executable by the processor to determine an average hole size in the 3D printed object based on the luminance measurement. The instructions are further executable by the processor to determine variance of the 3D printed object from specifications for the 3D printed object based on the average hole size. [0022] As used in the present specification and in the appended claims, the term “processor” may be a processor resource, a controller, an applicationspecific integrated circuit (ASIC), a semiconductor-based microprocessor, a central processing unit (CPU), and a field-programmable gate array (FPGA), and/or other hardware device that executes instructions.

[0023] As used in the present specification and in the appended claims, the term “memory” may include a computer-readable storage medium, where the computer-readable storage medium may contain, or store computer-usable program code for use by or in connection with an instruction execution system, apparatus, or device. The memory may take many types of memory including volatile memory (e.g., RAM) and non-volatile memory (e.g., ROM).

[0024] T urning now to the figures, Fig. 1 is a block diagram of a system 100 to determine an average size of holes for a region of a 3D printed object 104 based on a luminance measurement 108, according to an example. In this example, the system 100 includes a light source 102, a light sensor 106, and an electronic device 110 to estimate an average size of holes in a 3D printed object 104.

[0025] In some examples, the light source 102 may include a device that emits light. In some examples, the light source 102 may emit light with given properties (e.g., wavelengths, intensity, etc.). In some examples, the light source 102 may include a light guide to direct the light emitted from the light source 102. For example, the light guide may include a lens, aperture, or other component to direct, focus, or adjust the light emitted by the light source 102. [0026] The system 100 may include a light sensor 106. The 3D printed object 104 may be positioned between the light source 102 and the light sensor 106 such that light from light source 102 may pass through holes in the 3D printed object 104 and project onto the light sensor 106. In some examples, the light sensor 106 may be an optical luminance measurement device. For example, the light sensor 106 may obtain a luminance measurement 108 of the light emitted by the light source 102 and filtered by the 3D printed object 104.

[0027] Some examples of the light sensor 106 include a spectrophotometer or incident luminance meter with a controlled light source. The light sensor 106 measures the relative amount of light transmitted by the 3D printed object 104. For example, a spectrophotometer may measure the percent of light transmittance through the 3D printed object 104 with respect to a calibration standard. Similarly, a luminance meter may measure relative luminance of light transmitted through the 3D printed object 104.

[0028] In some examples, the light sensor 106 may also include a still camera. For example, a digital still camera may be calibrated to obtain luminance measurements 108 of the 3D printed object 104.

[0029] In some examples, luminance may be measured in candelas per meter squared but both normalized transmittance and normalized luminance will vary from 0 for an opaque sample to 1 for a perfectly clear sample. As used herein, a luminance measurement 108 may include a normalized transmittance measured by a spectrophotometer, a normalized luminance measured by a luminance meter, or other measurement of the amount of light that passes through the 3D printed object 104. It should be noted that while the examples of spectrophotometer and luminance meter are described, the light sensor 106 may include other light sensing devices (e.g., optical cameras, photodiode, etc.). [0030] The system 100 also includes an electronic device 110. As used herein, examples of an electronic device 110 may include computing devices, workstations, servers, laptop computers, desktop computers, smartphones, tablet devices, wireless communication devices, testing equipment, sensors, additive manufacturing devices, smart appliances, printing devices, robots, or other devices having memory resources and processing resources.

[0031] The electronic device 110 includes a processor 112. The processor 112 of the electronic device 110 may be implemented as dedicated hardware circuitry or a virtualized logical processor. The dedicated hardware circuitry may be implemented as a central processing unit (CPU). A dedicated hardware CPU may be implemented as a single to many-core general purpose processor. A dedicated hardware CPU may also be implemented as a multi-chip solution, where more than one CPU are linked through a bus and schedule processing tasks across the more than one CPU. [0032] In some examples, a memory 114 may be implemented in the electronic device 110. The memory 114 may be dedicated hardware circuitry to host instructions for the processor 112 to execute. In another implementation, the memory 114 may be virtualized logical memory. Analogous to the processor 112, dedicated hardware circuitry may be implemented with dynamic randomaccess memory (DRAM) or other hardware implementations for storing processor instructions. Additionally, the virtualized logical memory may be implemented in an abstraction layer which allows the instructions to be executed on a virtualized logical processor, independent of any dedicated hardware implementation.

[0033] The electronic device 110 may also include instructions. The instructions may be implemented in a platform specific language that the processor 112 may decode and execute. The instructions may be stored in the memory 114 during execution. In some examples, the instructions may include average hole size determination instructions 116, according to the examples described herein.

[0034] In some examples, the average hole size determination instructions 116 may cause the processor 112 to determine an average size of holes for a region of the 3D printed object 104 based on the luminance measurement 108. For example, the processor 112 may estimate an average hole diameter for the region of the 3D printed object 104 based on the luminance measurement 108. [0035] In some examples, a 3D printed object 104 may have a given hole design. For example, the digital file (e.g., computer-aided design (CAD) file) for the 3D printed object 104 may specify a given design for the holes of the 3D printed object 104. The hole design may include the size of the holes. In some examples, the holes may have a uniform size where each hole has the same diameter or other dimension. In some examples, the holes may have a non- uniform size where the diameter or other dimension of the holes differ. For example, holes in alternating rows may have different dimensions.

[0036] In some examples, the hole design may include a given hole spacing. For example, the holes of the 3D printed object 104 may be spaced apart from each other by a given distance. In some examples, the spacing of the holes may be random. In some examples, a first spacing may be used for holes in a first region, a second spacing may be used for holes in a second region, and so forth.

[0037] In some examples, the hole design may include a given fill pattern. For example, the holes may be located in a hexagonal pattern, as illustrated in Fig. 2. In another example, the holes may be located in a square or rectangular pattern, as illustrated in Fig. 3.

[0038] Referring briefly to Fig. 2, the 3D printed object 204 in this example includes holes 220 in a hexagonal packing 224. In this case, a row-A 226a has a number of holes 220. The holes 220 of row-B 226b are offset from row-A 226a. The holes 220 of row-C 226c are aligned with row-A 226a and the holes 220 of row-D 226d are aligned with row-B 226b. This pattern may be repeated for a number of rows. In some examples, the dimensions (e.g., diameter) of the holes 220 in a given row may differ from holes 220 in other rows in a repeating pattern. In other examples, the dimensions (e.g., diameter) of the holes 220 may be the same.

[0039] Referring now to Fig. 3, the 3D printed object 304 in this example includes holes 320 in a square packing 330. In this case, the holes in each row (e.g., row-A 326a, row-B 326b, row-C 326c) are aligned in a grid pattern.

[0040] Referring again to Fig. 1 , the processor 112 may determine the average hole size in a region of the 3D printed object 104 by comparing the measured luminance to calibration data. In some examples, the calibration data may be generated using a number of 3D printed objects having fixed and varying properties. For example, a number of 3D printed objects may have a given fill pattern (e.g., hexagonal, square, rectangular, etc.) and spacing, but the size of the holes may vary. For instance, the diameter of the holes of the calibration 3D printed objects may vary from 0.5mm to 0.8mm in 0.05mm steps. [0041] In some examples, the calibration data may be based on calibration samples with a given thickness. With light transparency measurements, the results are dependent on the thickness of the material through which the light passes. In the case of the 3D printed object 104, light may not pass through the printed material, but light will pass through the holes in the 3D printed object 104. As light passes through the holes, the light interacts with the material forming the holes. Thus, the thickness of a material impacts the amount of light that passes through holes in the material, where a thicker material allows less light to pass than a thinner material. In some examples, calibration data sets may be generated for given thicknesses. For example, each of the calibration 3D printed objects may have the same thickness, but the hole size may vary. Thus, multiple sets of calibration data may be generated for materials with different thicknesses. In this manner, the thickness of the 3D printed object 104 may be determined and a corresponding calibration data set may be selected for that thickness.

[0042] In some examples, the size of the holes in the 3D printed objects used to generate the calibration data (referred to herein as calibration 3D printed objects) may be verified using a measurement device. For example, upon printing a given calibration 3D printed object, a representative number of holes may be measured using a measurement device (e.g., a scanning conformal microscope). Measuring the actual size of a number of holes in the calibration 3D printed object may provide a ground truth for the calibration data. This ground truth may identify and account for deviations in hole sizes due to the 3D printing process. In other words, the holes may have a specified size in the CAD file for a 3D printed object. However, the actual hole size may differ from the specified size. By measuring the size of the holes in the calibration 3D printed objects, the actual hole size is used. It should be noted that in some examples, the calibration 3D printed objects may be printed with the same material used to print the 3D printed object 104 to ensure similar printing characteristics.

[0043] In some examples, the calibration data set may be created using a material and/or manufacturing process that ensures that individual holes correspond to the defined hole sizes within small tolerances. In this case, measurement of individual holes in the calibration objects may be avoided. Instead, the defined hole sizes of the calibration objects may be used along with luminance measurements of the calibration objects to form the calibration data set. [0044] For each calibration 3D printed object, a luminance measurement 108 may be obtained. For example, the same type of light sensor and light source that is used to obtain the luminance measurement 108 for a tested 3D printed object 104 may be used to obtain multiple luminance measurements for the calibration 3D printed objects. For example, a first luminance measurement may be obtained and recorded for a first calibration 3D printed object, a second luminance measurement may be obtained and recorded for a second calibration 3D printed object, and so forth. Fig. 4 illustrates an example of calibration data for a number of calibration samples. Fig. 5 illustrates an example of calibration data for a number of calibration samples with a corresponding fitted curve. [0045] Referring momentarily to Fig. 4, calibration data 432 for a number of calibration samples 433 is plotted on an XY plane. In this example, the calibration samples 433 include a number of 3D printed objects that are printed with different hole diameters. The measured average hole diameters 434 (measured in microns in this example) are plotted on the X-axis. Each calibration sample 433 is illuminated and a luminance measurement 436 is obtained using a light sensor, as described above. The luminance measurement 436 for each calibration sample 433 is plotted on the Y-axis. It should be noted that in the example of Fig. 4, there is a linear relationship in the luminance measurements 436 over the range of measured average hole diameters 434. [0046] In this example, the calibration data 432 includes a mapping of luminance measurements to hole diameters. For example, a measured luminance measurement corresponds to a given hole diameter.

[0047] The example of Fig. 4 illustrates a method to generate calibration data 432 for a plurality of 3D printed objects. As discussed, the plurality of 3D printed objects may be formed with a number of holes with a given size, a given spacing, a given thickness, and a given fill pattern. In this example, the size of the holes is varied while the spacing and fill pattern remain fixed. In this example, generating the calibration data 432 may include determining a calibrated luminance measurement 436 for a calibrated average hole size (e.g., measured average hole diameter 434) for each of the plurality of 3D printed objects. [0048] In some examples, the calibration data 432 is generated for given conditions. For example, the same light source and light sensor may be used for each data sample. Furthermore, the placement of the 3D printed objects with regard to the light source and light sensor may be consistent for each luminance measurement. Other conditions that may be consistent may include the type of fill pattern (e.g., hexagonal), the spacing used for the holes, the aperture size for the light sensor, etc. Thus, a given set of calibration data 432 may include a single variable parameter (e.g., the hole diameters).

[0049] It should be noted that in other examples, different parameters may be variable. For example, a different set of calibration data may made where the hole diameters are kept constant, but the spacing between holes may change.

[0050] Referring now to Fig. 5, in this example, the calibration data 532 includes calibration samples 533 that are presented where the luminance measurement 536 is on the X-axis and the measured average hole diameters 534 (measured in microns in this example) are plotted on the Y-axis. In this example, a fitted curve 535 is plotted to fit the calibration samples 533 using the least square method. In this case, the formula for the fitted curve 535 is

Y = 924X + 325 . (1)

[0051] In Equation (1 ), Xis the luminance measurement 536 and Y is the estimated hole diameter.

[0052] Referring again to Fig. 1 , upon receiving a luminance measurement 108, the processor 112 may compare the luminance measurement 108 to the calibration data. The processor 112 may compare the luminance measurement 108 to the calibration data. The processor 112 may map the luminance measurement 108 to a calibrated luminance for a calibrated average hole size. [0053] In some examples, the relationship between luminance and hole diameter (for a given fill pattern) may be the basis of the calibration data for the estimation process. In some examples, the processor 112 may perform a linear interpolation using the luminance measurement 108 and the calibration data. For example, the processor 112 may determine which two calibrated luminance measurements the luminance measurement 108 falls between. Using these values, the average size of holes for the measured region of the 3D printed object 1P4 may be estimated according to the following equations for linear interpolation: y -y y -y

yi -yp

[0054] In Equations (2) and (3), y is the luminance measurement 1P8, yo is a first (e.g., lower) calibrated luminance measurement, yi is a second (e.g., upper) calibrated luminance measurement, xo is a first (e.g., lower) calibrated average hole diameter, xi is a second (e.g., upper) calibrated average hole diameter, and x is the estimated average hole diameter.

[0055] In an example, the values of Fig. 4 are used to estimate the average hole diameter for a 3D printed object 1 P4. In this example, the luminance measurement 1 P8 obtained by the light sensor 1 P6 is P.225. Thus, y = P.225. In this case, the luminance measurement 1P8 falls between the fourth and fifth calibration samples of the calibration data shown in Fig. 4. Using the calibrated values from the fourth and fifth calibration samples of Fig. 4, yo = 0.2P5, yi =

P.235, xo = 52P microns, and xi = 56P microns. Applying these values to

Equation (3) results in an estimated average hole diameter (x) of 547 microns. [0056] In some examples, the processor 112 may perform curve fitting of the calibration data to construct a model (e.g., a line or polynomial function) for the data points in the calibration data. For example, the processor 112 may obtain a linear (e.g., first degree polynomial) equation for the data points in the calibration data. In other examples, the processor 112 may obtain a higher- order polynomial equation (e.g., second degree, third degree, etc.) to better fit the calibration data. Once the equation for a curve filling the data is determined, the processor 112 may save the curve for future use in determining average hole sizes from luminance measurements 108. In some examples, a leastsquares method may be used to obtain a linear equation fitting the calibration data. The processor 112 may then apply a luminance measurement 108 as input to the calibration data model, which outputs the average diameter for the 3D printed object 104. An example of this fitted curve approach is described in Fig. 5 and Equation (1).

[0057] In the example of Fig. 1 , the memory 114 may store the calibration data. In some examples, the memory 114 may store a calibration curve (e.g., model) that provides an equation that fits the calibration data. The processor 112 may access the calibration data and/or calibration data model to estimate the average hole size of the 3D printed object 104 based on a luminance measurement 108. Thus, the memory 114 may include calibration data relating hole diameters to luminance values, which is used by the processor 112 in an estimation process for transforming new luminance measurements 108 to estimated average hole diameters.

[0058] In some examples, the processor 112 may send an instruction to the light sensor 106 to obtain a luminance measurement 108. In this case, the light sensor 106 may obtain the luminance measurement 108 for the 3D printed object 104 and may send the luminance measurement 108 back to the electronic device 110 for use by the processor 112. In some examples, the processor 112 may receive the luminance measurement 108 without issuing a command to the light sensor 106. For example, a user may control the light sensor 106 to obtain the luminance measurement 108, which may subsequently be provided to the electronic device 110.

[0059] In some examples, multiple regions or orientations of the 3D printed object 104 can be measured and their results can be averaged. For example, a first luminance measurement 108 may be obtained for a first region of the 3D printed object 104, a second luminance measurement 108 may be obtained for a second region of the 3D printed object 104, and so forth. These multiple luminance measurements 108 may be averaged together to obtain a representative luminance measurement for use by the processor 112 to determine the average hole diameter over the multiple regions. A similar process may be performed for multiple orientations of the 3D printed object 104 to obtain representative luminance measurement for the multiple orientations. For example, a first luminance measurement 108 may be obtained for a first orientation of the 3D printed object 104, a second luminance measurement 108 may be obtained for a second orientation of the 3D printed object 104, and so forth.

[0060] In some examples, the processor 112 may determine variance of the 3D printed object 104 from specifications for the 3D printed object 104 based on the average hole size. For example, the holes of the 3D printed object 104 may have specified parameters (e.g., diameter, hole spacing, etc.). The specified parameters may be determined using the CAD file used to generate the 3D printed object 104. For example, the specified hole sizes may be queried in the CAD file. The specifications for the 3D printed object 104 may include a specified hole size and a specified packing density for the holes in the 3D printed object, where the packing density is a number of holes per unit area. [0061] In some examples, the processor 112 may compare the estimated average hole size to the specified hole size of the 3D printed object 104. For example, after determining the estimated average hole size using the luminance measurement 108, the processor 112 may determine whether the estimated average hole size is less than or greater than the specified hole size.

[0062] In some examples, the processor 112 may identify variance from the specifications for the 3D printed object 104 in response to determining that determine that average hole size in the 3D printed object 104 is less than the specified hole size. For example, the processor 112 may determine that the estimated average hole size is less than a threshold amount from the specified hole size. In some examples, if the processor 112 determines that the average hole size in the 3D printed object 104 is less than the specified hole size, this may be due to hole sizes in the 3D printed object 104 being manufactured smaller than the specified hole size. For example, the 3D printing process may result in holes in the 3D printed object 104 being smaller than the holes specified in the CAD file for the 3D printed object 104. [0063] In some examples, the average hole size in the 3D printed object 104 may be less than the specified hole size when holes in the 3D printed object 104 are obstructed with a material. For example, powder residue from the 3D printing process may partially or completely fill a number of holes in the 3D printed object 104 due to inadequate cleaning of the 3D printed object 104. In this case, the luminance measurement 108 may be less than expected for the specified hole sizes due to the powder residue blocking light from passing through the holes.

[0064] In some examples, the processor 112 may determine that the average hole size in the 3D printed object is greater than a specified hole size. In this case, if the 3D printed object 104 has holes that are too large, the estimated average hole size will be greater than what is specified in the CAD file. Thus the luminance measurement 108 may quickly indicate that the actual 3D printed object 104 deviates from the properties specified in the CAD file. [0065] Upon detecting variance in the 3D printed object 104, the processor 112 may perform an operation. For example, the processor 112 may issue a notification identifying the variance. In this manner, a user may become aware that the 3D printed object 104 deviates from specified parameters and can adjust the 3D printed object 104 accordingly. In other examples, the processor 112 may perform an automated operation to account for the variance.

[0066] As seen by these examples, the system 100 uses optical measurements to provide an efficient estimate of the geometric features of the 3D printed object 104. These examples may be used for an efficient quality control of 3D printed objects 104 containing a set of holes (e.g., screens). These examples provide an efficient, single-measurement approach that takes seconds to estimate the average hole size for a region of the 3D printed object 104. Furthermore, these examples may use an aperture on the light sensor 106 to define the region used for averaging the hole sizes.

[0067] Fig. 6 is a flow diagram illustrating a method 600 for determining an average size of holes for a region of a 3D printed object based on a luminance measurement, according to an example. In some examples, the method 600 may be performed by a processor, such as the processor 112 of Fig. 1 . [0068] At 602, luminance of a 3D printed object with light projected behind the 3D printed object may be measured. For example, a 3D printed object may be positioned between a light source and a light sensor. The light sensor may measure the amount of light that passes through the 3D printed object to obtain the luminance measurement. This luminance measurement may be provided to the processor. In some examples, the light sensor may be a spectrophotometer or incident luminance meter with a controlled light source.

[0069] At 604, an average size of holes for a region of the 3D printed object may be determined based on the luminance measurement and calibration data. Calibration data may be generated for a plurality of 3D printed objects. The plurality of 3D printed objects used for the calibration data may be formed with a number of holes with a given size, a given spacing, a given thickness, and a given fill pattern. In some examples, the hole sizes (e.g., diameter) of each calibration 3D printed object may be measured and averaged. The hole measurement may be referred to as a calibrated average hole size. A luminance measurement may be obtained for each of the calibration 3D printed objects.

Thus, a calibrated luminance may be determined for the calibrated average hole size for each of the plurality of 3D printed objects. In some examples, the calibrated luminance and calibrated average hole size may be recorded as the calibration data.

[0070] In some examples, the calibration data may include the calibrated luminance and calibrated average hole sizes for the plurality of calibration 3D printed objects. In some examples, the calibration data may include a calibration curve that is calculated to fit the calibration data. For example, data fitting may be used to determine a linear or other polynomial equation that models the calibration data.

[0071] In some examples, determining the average size of holes for a region of the 3D printed object may include comparing the luminance measurement to the calibration data. In an example approach, the luminance measurement may be applied to the calibration curve to calculate the average hole size for the observed region of the 3D printed object, as illustrated in Equation (1 ). In another example approach, linear interpolation may be performed on the calibration data using the luminance measurement, as described by Equations (2) and (3).

[0072] Fig. 7 depicts a non-transitory machine-readable storage medium 750 for determining an average size of holes for a region of a 3D printed object based on a luminance measurement, according to an example. To achieve its desired functionality, an electronic device includes various hardware components. Specifically, an electronic device includes a processor and a machine-readable storage medium 750. The machine-readable storage medium 750 is communicatively coupled to the processor. The machine-readable storage medium 750 includes a number of instructions 752, 754, 756 for performing a designated function. The machine-readable storage medium 750 causes the processor to execute the designated function of the instructions 752, 754, 756. The machine-readable storage medium 750 can store data, programs, instructions, or any other machine-readable data that can be utilized to operate the electronic device 110. Machine-readable storage medium 750 can store computer readable instructions that the processor of the electronic device 110 can process or execute. The machine-readable storage medium 750 can be an electronic, magnetic, optical, or other physical storage device that contains or stores executable instructions. Machine-readable storage medium 750 may be, for example, Random Access Memory (RAM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), a storage device, an optical disc, etc. The machine-readable storage medium 750 may be a non-transitory machine-readable storage medium 750, where the term “non-transitory” does not encompass transitory propagating signals.

[0073] Referring to Fig. 7, luminance measurement instructions 752, when executed by the processor, may cause the processor to receive, from a light sensor, a luminance measurement of a 3D printed object with light projected behind the 3D printed object. Average hole size determination instructions 754, when executed by the processor, may cause the processor to determine an average hole size in the 3D printed object based on the luminance measurement. Variance determination instructions 756 when executed by the processor, may cause the processor to determine variance of the 3D printed object from specifications for the 3D printed object based on the average hole size.

[0074] In some examples, the specifications for the 3D printed object may include a specified hole size and a specified packing density for holes in the 3D printed object. In some examples, the specifications for the 3D printed object may be obtained from a CAD file of the 3D printed object.

[0075] In some examples, the instructions to determine the variance of the 3D printed object from specifications may include instructions executable by the processor to determine that the average hole size in the 3D printed object is less than a specified hole size. For example, the processor may obtain the specified hole size from the CAD file or other database. The processor may then determine whether the estimated average hole size is a threshold amount less than the specified hole size.

[0076] In some examples, the instructions to determine the variance of the 3D printed object from specifications may include instructions executable by the processor to determine that the average hole size in the 3D printed object is greater than a specified hole size. For example, the processor may obtain the specified hole size from the CAD file or other database. The processor may then determine whether the estimated average hole size is a threshold greater than the specified hole size.

Claims

CLAIMS What is claimed is:

1. A system, comprising: a light sensor to measure a luminance of a 3D printed object with light projected behind the 3D printed object; a processor; and a memory communicatively coupled to the processor and storing executable instructions that when executed cause the processor to: determine an average size of holes for a region of the 3D printed object based on the luminance measurement.

2. The system of claim 1 , wherein the instructions to determine the average size of holes for the region comprise executable instructions that when executed cause the processor to: estimate an average hole diameter for the region of the 3D printed object based on the luminance measurement.

3. The system of claim 1 , wherein the light sensor comprises a spectrophotometer or incident luminance meter with a controlled light source.

4. The system of claim 1 , wherein the instructions to determine the average size of holes for the region comprise executable instructions that when executed cause the processor to: compare the measured luminance to calibration data.

5. The system of claim 4, wherein the calibration data comprises a mapping of luminance measurements to hole diameters.

6. A method, comprising: measuring a luminance of a 3D printed object with light projected behind the 3D printed object; determining an average size of holes for a region of the 3D printed object based on the luminance measurement and calibration data.

7. The method of claim 6, further comprising: generating the calibration data for a plurality of 3D printed objects.

8. The method of claim 7, wherein the plurality of 3D printed objects are formed with a number of holes with a given size, a given spacing, a given thickness, and a given fill pattern.

9. The method of claim 7, wherein generating the calibration data comprises determining a calibrated luminance for a calibrated average hole size for each of the plurality of 3D printed objects.

10. The method of claim 6, wherein determining the size of the holes in the 3D printed object comprises: applying the luminance measurement to a calibration curve that maps the luminance measurement to an average hole diameter.

11 . The method of claim 6, wherein determining the size of the holes in the 3D printed object comprises: performing a linear interpolation of the calibration data using the luminance measurement.

12. A non-transitory computer-readable storage medium comprising instructions executable by a processor to: receive, from a light sensor, a luminance measurement of a 3D printed object with light projected behind the 3D printed object; determine an average hole size in the 3D printed object based on the luminance measurement; and determine variance of the 3D printed object from specifications for the 3D printed object based on the average hole size.

13. The non-transitory computer-readable storage medium of claim 12, wherein the specifications for the 3D printed object comprise a specified hole size and a specified packing density for holes in the 3D printed object.

14. The non-transitory computer-readable storage medium of claim 12, wherein the instructions to determine variance of the 3D printed object from specifications comprise instructions executable by the processor to: determine that the average hole size in the 3D printed object is less than a specified hole size.

15. The non-transitory computer-readable storage medium of claim 12, wherein the instructions to determine variance of the 3D printed object from specifications comprise instructions executable by the processor to: determine that the average hole size in the 3D printed object is greater than a specified hole size.