Patent Description:
In additive manufacturing processes such as electron-beam melting of a powder layer to create an article, there exist some challenges to diagnose aborted or failed builds or identify performance issues of an additive manufacturing device. Specifically, an expert has to manually diagnose a build or the device, which not only takes a lot of time but also requires human labor. In addition, finding a root cause of a failure of the additive manufacturing device is a difficult and time-consuming process. Thus, it may be necessary to diagnose a failure of the additive manufacturing device with reduced time and find an exact cause for the failure of the additive manufacturing device.

<CIT> relates to receiving sensor data for an operating manufacturing machine and producing an alert during manufacture of a part based at least in part on the sensor data.

<CIT> relates to systems and methods associated with optic train monitoring for an additive manufacturing machine <CIT> relates to a method for monitoring the quality of a work piece by using multivariate statistical process controls in the fabrication of objects using additive manufacturing processes.

The invention is defined by a system for diagnosing an additive manufacturing device, as defined in claim <NUM>; a method for diagnosing an additive manufacturing device, as defined in claim <NUM>; and a corresponding non-transitory machine readable media, as defined in claim <NUM>.

In an embodiment, a system for diagnosing an additive manufacturing device is provided. The system includes one or more processors, one or more non-transitory memory modules communicatively coupled to the one or more processors and storing machine-readable instructions. The machine-readable instructions, when executed, cause the one or more processors to: determine parameters associated with at least one subsystem of the additive manufacturing device, the parameters being related to a build generated by the additive manufacturing device; compare the parameters with threshold values; and determine a failure mode, among a plurality of failure modes, associated with a subsystem of the at least one subsystem of the additive manufacturing device based on the comparison of the parameters with the threshold values.

In an embodiment, a method for diagnosing an additive manufacturing device is provided. The method includes determining parameters associated with at least one subsystem of an additive manufacturing device, the parameters being related to a build generated by the additive manufacturing device, comparing the parameters with threshold values, and determining a failure mode, among a plurality of failure modes, associated with a subsystem of the at least one subsystem of the additive manufacturing device based on the comparison of the parameters with the threshold values.

In an embodiment, a non-transitory machine readable media includes computer executable instructions, when executed by one or more processors, configured to cause the one or more processors to: determine parameters associated with at least one subsystem of an additive manufacturing device, the parameters being related to a build generated by the additive manufacturing device, compare the parameters with threshold values, and determine a failure mode, among a plurality of failure modes, associated with a subsystem of the at least one subsystem of the additive manufacturing device based on the comparison of the parameters with the threshold values.

These and other features, and characteristics of the present technology, as well as the methods of operation and functions of the related elements of structure and the combination of parts and economies of manufacture, will become more apparent upon consideration of the following description and the appended claims with reference to the accompanying drawings, all of which form a part of this specification, wherein like reference numerals designate corresponding parts in the various figures. As used in the specification and in the claims, the singular form of 'a', 'an', and 'the' include plural referents unless the context clearly dictates otherwise.

The present disclosure generally relates to devices, systems, and methods for diagnosing an additive manufacturing device. The systems and methods determine parameters associated with at least one subsystem of the additive manufacturing device, compare the parameters with threshold values, and determine a failure mode, among a plurality of failure modes, associated with a subsystem of the at least one subsystem of the additive manufacturing device based on the comparison of the parameters with the threshold values. The analyzed output is visualized through a display device indicating current health status of the additive manufacturing device or subsystems thereof. Accordingly, the present disclosure provides users with a visually interactive interface that provides guided steps in diagnosing build or module level issues in a matter of a few minutes.

Additive manufacturing systems may use an electron-beam or a laser beam to manufacture builds. Additive manufacturing systems may include multiple electron-beam guns or laser designs. Electron-beam additive manufacturing, which may also be known as electron-beam melting (EBM), is a type of additive manufacturing (3D printing) process that is typically used for metallic articles. EBM utilizes a raw material in the form of a metal powder or a metal wire, which is placed under a vacuum (e.g., within a vacuum sealed build chamber). Generally speaking, the raw material is fused together from heating via an electron beam.

Systems that utilize EBM generally obtain data from a 3D computer-aided design (CAD) model and use the data to place successive layers of the raw material using an apparatus to spread the raw material, such as a powder distributor. The successive layers are melted together utilizing a computer-controlled electron beam. As noted above, the process takes place under vacuum within a vacuum sealed build chamber, which makes the process suited to manufacture parts using reactive materials having a high affinity for oxygen (e.g., titanium). In embodiments, the process operates at higher temperatures (up to about <NUM>,<NUM>) relative to other additive manufacturing processes, which can lead to differences in phase formation though solidification and solid-state phase transformation.

Direct metal laser melting (DMI,M) is an another additive manufacturing process that uses lasers to melt ultra-thin layers of metal powder to build a three-dimensional object. Objects are built directly from a file generated from CAD (computer-aided design) data. The use of a laser to selectively melt thin layers of tiny particles yields objects exhibiting fine, dense and homogeneous characteristics. The DMLM process begins with a roller spreading a thin layer of metal powder on the print bed. Next, a laser is directed based on the CAD data to create a cross-section of the object by completely melting metal particles. The print bed is then lowered so the process can be repeated to create the next object layer. After all the layers are printed, the excess unmelted powder is brushed, blown or blasted away. The object typically requires little, if any, finishing. Embodiments described herein are applicable to other additive manufacturing modalities employing other types of additive manufacturing devices beyond those disclosed herein.

<FIG> depicts an additive manufacturing device of the present disclosure, according to one or more embodiments shown and described herewith. As shown in <FIG>, an additive manufacturing system <NUM> includes at least a build chamber <NUM>, an imaging device <NUM>, and a control component <NUM>. The build chamber <NUM> defines an interior <NUM> that is separated from an exterior environment <NUM> via one or more chamber walls <NUM>. In some embodiments, at least a portion of the one or more chamber walls <NUM> of the build chamber <NUM> may include a window <NUM> therein. The imaging device <NUM> is generally located adjacent to the build chamber <NUM> in the exterior environment <NUM> (i.e., not located within the interior <NUM> of the build chamber <NUM>), and is arranged such that a field of view <NUM> of the imaging device <NUM> extends through the window <NUM> into the interior <NUM> of the chamber.

In some embodiments, the interior <NUM> of the build chamber <NUM> may be a vacuum sealed interior such that an article <NUM> formed within the build chamber <NUM> is formed under optimal conditions for EBM or DMI,M, as is generally understood. The build chamber <NUM> is capable of maintaining a vacuum environment via a vacuum system. Illustrative vacuum systems may include, but are not limited to, a turbo molecular pump, a scroll pump, an ion pump, and one or more valves, as are generally understood. In some embodiments, the vacuum system may be communicatively coupled to the control component <NUM> such that the control component <NUM> directs operation of the vacuum system to maintain the vacuum within the interior <NUM> of the build chamber <NUM>. In some embodiments, the vacuum system may maintain a base pressure of about <NUM>×<NUM>-<NUM> mbar or less throughout an entire build cycle. In further embodiments, the vacuum system may provide a partial pressure of He or other reactive or inert control gas to about <NUM>×<NUM>-<NUM> mbar during a melting process.

In other embodiments, the build chamber <NUM> may be provided in an enclosable chamber provided with ambient air and atmosphere pressure. In yet other embodiments, the build chamber <NUM> may be provided in open air.

The build chamber <NUM> generally includes within the interior <NUM> a powder bed <NUM> supporting a powder layer <NUM> thereon, as well as a powder distributor <NUM>. In some embodiments, the build chamber <NUM> may further include one or more raw material hoppers 140a, 140b that maintain raw material <NUM> therein. In some embodiments, the build chamber <NUM> may further include an emitter <NUM>. The build chamber <NUM> may further include other components, particularly components that facilitate EBM or DMI,M, including components not specifically described herein.

The powder bed <NUM> is generally a platform or receptacle located within the interior <NUM> of the build chamber <NUM> that is arranged to receive the raw material <NUM> from the one or more raw material hoppers 140a, 140b. The powder bed <NUM> is not limited in size or configuration by the present disclosure, but may generally be shaped and sized to hold an amount of the raw material <NUM> from the raw material hoppers 140a, 140b in the form of the powder layer <NUM>, one or more portions of article <NUM>, and/or unfused raw material <NUM>, as described in greater detail herein.

In some embodiments, the powder bed <NUM> may include a movable build platform <NUM> supported by a lifting component <NUM>. The movable build platform <NUM> may generally be a surface within the powder bed <NUM> that is movable by the lifting component <NUM> in a system vertical direction (e.g., in the +y/-y directions of the coordinate axes of <FIG>) to increase and/or decrease a total volume of the powder bed <NUM>. For example, the movable build platform <NUM> within the powder bed <NUM> may be movable by the lifting component <NUM> in a downward direction (e.g., toward the -y direction of the coordinate axes of <FIG>) so as to increase the volume of the powder bed <NUM>. In addition, the movable build platform <NUM> may be movable by the lifting component <NUM> to add each successive powder layer <NUM> to the article <NUM> being formed, as described in greater detail herein.

The lifting component <NUM> is not limited by the present disclosure, and may generally be any device or system capable of being coupled to the movable build platform <NUM> and movable to raise or lower the movable build platform <NUM> in the system vertical direction (e.g., in the +y/y directions of the coordinate axes of <FIG>). In some embodiments, the lifting component <NUM> may utilize a linear actuator type mechanism to effect movement of the movable build platform <NUM>. Illustrative examples of devices or systems suitable for use as the lifting component <NUM> include, but are not limited to, a scissor lift, a mechanical linear actuator such as a screw based actuator, a wheel and axle actuator (e.g., a rack and pinion type actuator), a hydraulic actuator, a pneumatic actuator, a piezoelectric actuator, an electromechanical actuator, and/or the like. In some embodiments, the lifting component <NUM> may be located within the build chamber <NUM>. In other embodiments, the lifting component <NUM> may be only partially located within the build chamber <NUM>, particularly in embodiments where it may be desirable to isolate portions of the lifting component <NUM> that are sensitive to the harsh conditions (high heat, excessive dust, etc.) within the interior <NUM> of the build chamber <NUM>.

The powder distributor <NUM> is generally arranged and configured to lay down and/or spread a layer of the raw material <NUM> as the powder layer <NUM> in the powder bed <NUM> (e.g., on start plate or build platform <NUM> within the powder bed). That is, the powder distributor <NUM> is arranged such that movement of the powder distributor <NUM> is in a horizontal plane defined by the x-axis and the z-axis of the coordinate axes depicted in <FIG>. For example, the powder distributor <NUM> may be an arm, rod, or the like that extends a distance in the z direction of the coordinate axes of <FIG> over or above the powder bed <NUM> (e.g., from a first end to a second end of the powder bed <NUM>). In some embodiments, the length of the powder distributor <NUM> may be longer than a width of the build platform <NUM> such that the powder layer <NUM> can be distributed on each position of the build platform <NUM>. In some embodiments, the powder distributor <NUM> may have a central axis in parallel with a top surface of the build platform <NUM> (e.g., generally parallel to the +x/-x axis of the coordinate axes of <FIG>). One or more motors, actuators, and/or the like may be coupled to the powder distributor <NUM> to effect movement of the powder distributor <NUM>. For example, a rack and pinion actuator may be coupled to the powder distributor <NUM> to cause the powder distributor <NUM> to move back and forth over the powder bed in the +x/-x directions of the coordinate axes of <FIG>, as indicated by the double sided arrow depicted above the powder distributor <NUM> in <FIG>. In some embodiments, movement of the powder distributor <NUM> may be continuous (e.g., moving without stopping, other than to change direction). In other embodiments, movement of the powder distributor <NUM> may be stepwise (e.g., moving in a series of intervals). In yet other embodiments, movement of the powder distributor <NUM> may be such that a plurality of interruptions occur between periods of movement.

As described in greater detail herein, the powder distributor may further include one or more teeth (e.g., rake fingers or the like) that extend from the powder distributor <NUM> into the raw material <NUM> from the raw material hoppers 140a, 140b to cause disruption of the raw material <NUM> when the powder distributor <NUM> moves (e.g., to distribute the raw material <NUM>, to spread the powder layer <NUM>, etc.).

In embodiments, the powder distributor <NUM> includes a plurality of rake teeth <NUM> extending from a bottom surface B of the powder distributor <NUM> (e.g., extending generally towards the -y direction of the coordinate axes of <FIG>). In some embodiments, the rake teeth <NUM> may extend in a direction that is substantially perpendicular to a plane of the build platform <NUM> (e.g., perpendicular to the plane formed by the x-axis and z-axis of the coordinate axes depicted in <FIG>). In another embodiment, the rake teeth <NUM> may be slanted with respect to the build platform <NUM>. An angle a of the slanted rake teeth <NUM> with respect to a normal to the build platform may be any value, and in some embodiments is between about <NUM> and about <NUM>°.

In some embodiments, each one of the plurality of rake teeth <NUM> may be a metal foil or a metal sheet. The total length of the plurality of rake teeth <NUM> may be longer than a width of the build platform <NUM> in order to make it possible to distribute powder on each position of the build platform <NUM>. The rake teeth <NUM> may be shaped and sized to rake through the raw material <NUM> to distribute the powder layer <NUM> on the build platform <NUM>. Some embodiments may not include rake teeth <NUM>.

It should be understood that while the powder distributor <NUM> described herein generally extends a distance in the x direction of the coordinate axes depicted in <FIG> and moves in the +x/-x directions of the coordinate axes depicted in <FIG> to spread the powder layer <NUM> as described above, this is merely one illustrative example. Other configurations are also contemplated. For example, the powder distributor <NUM> may rotate about an axis to spread the powder layer <NUM>, may articulate about one or more joints or the like to spread the powder layer <NUM>, and/or the like without departing from the scope of the present disclosure.

In some embodiments, a cross section of the powder distributor <NUM> may be generally triangular, as depicted in <FIG>. However, it should be understood that the cross section may be any shape, including but not limited to, circular, elliptical, quadratic, rectangular, polygonal or the like. A height of the powder distributor <NUM> may be set in order to give the powder distributor <NUM> a particular mechanical strength in the system vertical direction (e.g., along the +y/-y axis of the coordinate axes of <FIG>). That is, in some embodiments, the powder distributor <NUM> may have a particular controllable flex in the system vertical direction. The height of the powder distributor may also be selected taking into account that the powder distributor <NUM> pushes an amount of the raw material <NUM>. If the height of the powder distributor <NUM> is too small, the powder distributor <NUM> can only push forward a smaller amount relative to a higher power powder distributor <NUM>. However, if the height of the powder distributor <NUM> is too high, the powder distributor <NUM> may complicate the powder catching from a scree of powder, (e.g., the higher the height of the powder distributor <NUM>, the more force may be required in order to catch a predetermined amount of powder from the scree of powder by moving the powder distributor <NUM> into the scree of powder and letting a predetermined amount of powder fall over the top of the powder distributor <NUM> from a first side in the direction of travel into the scree of powder to a second side in the direction of the build platform <NUM>). In still yet other embodiments, the height of the powder distributor <NUM> may be such that areas adjacent to both a leading edge and a trailing edge of the powder distributor <NUM> are within a field of view <NUM> of the imaging device <NUM>, as described herein.

In some embodiments, the powder distributor <NUM> may be communicatively coupled to the control component <NUM>, as depicted by the dashed line in <FIG> between the powder distributor <NUM> and the control component <NUM>. As used herein, the term "communicatively coupled" generally refers to any link in a manner that facilitates communications. As such, "communicatively coupled" includes both wireless and wired communications, including those wireless and wired communications now known or later developed. As the powder distributor <NUM> is communicatively coupled to the control component <NUM>, the control component <NUM> may transmit one or more signals, data, and/or the like to cause the powder distributor <NUM> to move, change direction, change speed, and/or the like. For example, a "reverse direction" signal transmitted by the control component <NUM> to the powder distributor <NUM> may cause the powder distributor <NUM> to reverse the direction in which it is moving (e.g., reverse movement in the +x direction to movement in the -x direction).

Each of the raw material hoppers 140a, 140b may generally be containers that hold an amount of the raw material <NUM> therein and contain an opening to dispense the raw material <NUM> therefrom. While <FIG> depicts two raw material hoppers 140a, 140b, the present disclosure is not limited to such. That is, any number of raw material hoppers may be utilized without departing from the scope of the present disclosure. Further, while <FIG> depicts the raw material hoppers 140a, 140b as being located within the interior <NUM> of the build chamber <NUM>, the present disclosure is not limited to such. That is, the raw material hoppers 140a, 140b may be located outside or partially outside the build chamber <NUM> in various other embodiments. However, it should be understood that if a raw material hopper is located outside or partially outside the build chamber <NUM>, one or more outlets of the raw material hoppers that supply the raw material <NUM> may be selectively sealed when not distributing the raw material <NUM> in order to maintain the vacuum within the build chamber <NUM>.

The shape and size of the raw material hoppers 140a, 140b are not limited by the present disclosure. That is, the raw material hoppers 140a, 140b may generally have any shape and or size without departing from the scope of the present disclosure. In some embodiments, each of the raw material hoppers 140a, 140b may be shaped and or sized to conform to the dimensions of the build chamber <NUM> such that the raw material hoppers 140a, 140b can fit inside the build chamber. In some embodiments, the raw material hoppers 140a, 140b may be shaped and sized such that a collective volume of the raw material hoppers 140a, 140b is sufficient to hold an amount of raw material <NUM> that is necessary to fabricate the article <NUM>, which includes a sufficient amount of material to form each successive powder layer <NUM> and additional material that makes up the unfused raw material <NUM>.

The raw material hoppers 140a, 140b may generally have an outlet for ejecting the raw material <NUM> located within the raw material hoppers 140a, 140b such that the raw material <NUM> can be spread by the powder distributor <NUM>, as described herein. In some embodiments, such as the embodiment depicted in <FIG>, the raw material <NUM> may freely flow out of the raw material hoppers 140a, 140b under the force of gravity, thereby forming piles or scree of raw material <NUM> for the powder distributor <NUM> to spread. In other embodiments, the outlets of the raw material hoppers 140a, 140b may be selectively closed via a selective closing mechanism so as to only distribute a portion of the raw material <NUM> located within the respective raw material hoppers 140a, 140b at a particular time. The selective closing mechanisms may be communicatively coupled to the control component <NUM> such that data and/or signals transmitted to/from the control component <NUM> can be used to selectively open and close the outlets of the raw material hoppers 140a, 140b.

The raw material <NUM> contained within the raw material hoppers 140a, 140b and used to form the article <NUM> is not limited by the present disclosure, and may generally be any raw material used for EBM or DMI,M now known or later developed. Illustrative examples of raw material <NUM> includes, but is not limited to, pure metals such as titanium, aluminum, tungsten, or the like; and metal alloys such as titanium alloys, aluminum alloys, stainless steel, cobalt-chrome alloys, cobalt-chrome-tungsten alloys, nickel alloys, and/or the like. Specific examples of raw material <NUM> include, but are not limited to, Ti6Al4V titanium alloy, Ti6Al4V ELI titanium alloy, Grade <NUM> titanium, and ASTM F75 cobalt-chrome (all available from Arcam AB, Mölndal, Sweden). Another specific example of raw material <NUM> is INCONEL® alloy <NUM> available from Special Metals Corporation (Huntington WV).

In embodiments, the raw material <NUM> is pre-alloyed, as opposed to a mixture. This may allow classification of EBM or DMI,M with selective laser melting (SLM), where other technologies like selective laser sintering (SLS) and direct metal laser sintering (DMI,S) require thermal treatment after fabrication. Compared to selective laser melting (SLM) and DMLS, EBM has a generally superior build rate because of its higher energy density and scanning method.

The emitter <NUM> is generally a device that emits an electron beam (e.g., a charged particle beam), such as, for example, an electron gun, a linear accelerator, or the like. The emitter <NUM> generates an energy beam <NUM> that may be used for melting or fusing together the raw material <NUM> when spread as the powder layer <NUM> on the build platform <NUM>. In some embodiments, the emitter <NUM> may include at least one focusing coil, at least one deflection coil and an electron beam power supply, which may be electrically connected to an emitter control unit. In one illustrative embodiment, the emitter <NUM> generates a focusable electron beam with an accelerating voltage of about <NUM> kilovolts (kV) and with a beam power in the range of about <NUM> kilowatts (kW) to about <NUM> kW. The pressure in the vacuum chamber may be in the range of about <NUM>×<NUM>-<NUM> mBar to about <NUM>×<NUM>-<NUM> mBar when building the article <NUM> by fusing each successive powder layer <NUM> with the energy beam <NUM>. The emitter <NUM> may sit in a gun vacuum chamber. The pressure in the gun vacuum chamber may be in the range of about <NUM>×<NUM>-<NUM> mBar to about <NUM>×<NUM>-<NUM> mBar. In some embodiments, the emitter <NUM> may emit a laser beam using direct metal laser melting (DMLM). The emitter <NUM> may emit laser to melt ultra-thin layers of metal powder to build a three-dimensional object. When using DMI,M, a gas flow may be provided over a build in contrast with electron beam melting manufacturing that requires a vacuum chamber.

In some embodiments, the emitter <NUM> may be communicatively coupled to the control component <NUM>, as indicated in <FIG> by the dashed line between the emitter <NUM> and the control component <NUM>. The communicative coupling of the emitter <NUM> to the control component <NUM> may provide an ability for signals and/or data to be transmitted between the emitter <NUM> and the control component <NUM>, such as control signals from the control component <NUM> that direct operation of the emitter <NUM>.

Still referring to <FIG>, the imaging device <NUM> is generally located in the exterior environment <NUM> outside the build chamber <NUM>, yet positioned such that the field of view <NUM> of the imaging device <NUM> is through the window <NUM> of the build chamber <NUM>. The imaging device <NUM> is generally positioned outside the build chamber <NUM> such that the harsh environment within the interior <NUM> of the build chamber <NUM> does not affect operation of the imaging device <NUM>. That is, the heat, dust, metallization, x-ray radiation, and/or the like that occurs within the interior <NUM> of the build chamber <NUM> will not affect operation of the imaging device <NUM>. In embodiments, the imaging device <NUM> is fixed in position such that the field of view <NUM> remains constant (e.g., does not change). Moreover, the imaging device <NUM> is arranged in the fixed position such that the field of view <NUM> of the imaging device <NUM> encompasses an entirety of the powder bed <NUM>. That is, the imaging device <NUM> is capable of imaging the entire powder bed <NUM> within the build chamber <NUM> through the window <NUM>.

In some embodiments, the imaging device <NUM> is a device particularly configured to sense electromagnetic radiation, particularly heat radiation (e.g., thermal radiation) that is generated by the various components within the powder bed <NUM> (e.g., the powder layer <NUM>, the raw material <NUM>, and/or the article <NUM>). Thus, the imaging device <NUM> may generally be a device particularly tuned or otherwise configured to obtain images in spectra where heat radiation is readily detected, such as the visible spectrum and the infrared spectrum (including the far infrared and the near infrared spectrum). As such, one illustrative example of a device particularly tuned or otherwise configured to obtain images in spectra where heat radiation includes, but is not limited to, an infrared camera. In some embodiments, the imaging device <NUM> may be a camera that is sensitive within a range of wavelengths of about <NUM> micrometer(µm) to about <NUM>, including about <NUM>, about <NUM>, about <NUM>, about <NUM>, about <NUM>, about <NUM>, about <NUM>, about <NUM>, about <NUM>, about <NUM>, about <NUM>, about <NUM>, about <NUM>, about <NUM>, or any value or range between any two of these values (including endpoints). As such, the imaging device <NUM> is suitable for imaging temperatures which occur during EBM or DMLM of the powder layer <NUM>. In some embodiments, the wavelength sensitivity of the imaging device <NUM> may be selected in accordance with the type of raw material used. Illustrative examples of suitable devices that may be used for the imaging device <NUM> include, but are not limited to, an IR-camera (Infrared-camera), NIR-camera (Near Infrared-camera), a VISNIR-camera (Visual Near Infrared-camera), a CCD camera (Charged Coupled Device-camera), and a CMOS-camera (Complementary Metal Oxide Semiconductor-camera).

In some embodiments, the imaging device <NUM> may be an area scan camera that is capable of providing data specific to one or more regions of interest within the field of view <NUM>, including regions of interest that move within the field of view <NUM>. That is, an area scan camera includes a matrix of pixels that allows the device to capture a 2D image in a single exposure cycle with both vertical and horizontal elements. Area scan cameras can further be used to obtain a plurality of successive images, which is useful when selecting regions of interest within the field of view <NUM> and observing a change in the regions of interest, as described in greater detail herein. Illustrative examples of such area scan cameras include those available from Basler AG (Ahrensburg, Germany), JAI Ltd. (Yokohama, Japan), National Instruments (Austin, TX), and Stemmer Imaging (Puchheim, Germany).

In some embodiments, the imaging device <NUM> may have a monochrome image sensor. In other embodiments, the imaging device <NUM> may have a color image sensor. In various embodiments, the imaging device <NUM> may include one or more optical elements, such as lenses, filters, and/or the like. In a particular embodiment, the imaging device <NUM> may include a Bayer filter. As is generally understood, a Bayer filter is a color filter array (CFA) for arranging RGB color filters on a square grid of photosensors to create a color image, such as a filter pattern of about <NUM>% green, about <NUM>% red, and about <NUM>% blue.

In some embodiments, the imaging device <NUM> may further be a device particularly configured to provide signals and/or data corresponding to the sensed electromagnetic radiation to the control component <NUM>. As such, the imaging device <NUM> may be communicatively coupled to the control component <NUM>, as indicated by the dashed lines depicted in <FIG> between the imaging device <NUM> and the control component <NUM>.

It should be understood that, by locating the imaging device <NUM> in the exterior environment <NUM> outside the interior <NUM> of the build chamber <NUM>, it is possible to easily retrofit existing build chambers having windows in the chamber walls <NUM> therein with a kit that includes the imaging device <NUM> so as to upgrade the existing build chambers with the capabilities described herein.

The control component <NUM> is generally a device that is communicatively coupled to one or more components of the additive manufacturing system <NUM> (e.g., the powder distributor <NUM>, the imaging device <NUM>, and/or the emitter <NUM>) and is particularly arranged and configured to transmit and/or receive signals and/or data to/from the one or more components of the additive manufacturing system <NUM>.

<FIG> is a block diagram of an exemplary system <NUM> according to one or more embodiments shown and described herein. According to the invention, the system <NUM> includes the additive manufacturing system <NUM>, and optionally server <NUM>, a user computing device <NUM>, and a mobile computing device <NUM>. The additive manufacturing system <NUM> may be communicatively coupled to the server <NUM>, the user computing device <NUM>, and the mobile computing device <NUM> by a network <NUM>. In embodiments, the network <NUM> may include one or more computer networks (e.g., a personal area network, a local area network, or a wide area network), cellular networks, satellite networks and/or a global positioning system and combinations thereof. Accordingly, the user computing device <NUM> can be communicatively coupled to the network <NUM> via a wide area network, via a local area network, via a personal area network, via a cellular network, via a satellite network, etc. Suitable local area networks may include wired Ethernet and/or wireless technologies such as, for example, wireless fidelity (Wi-Fi). Suitable personal area networks may include wireless technologies such as, for example, IrDA, Bluetooth®, Wireless USB, Z-Wave, ZigBee, and/or other near field communication protocols. Suitable cellular networks include, but are not limited to, technologies such as LTE, WiMAX, UMTS, CDMA, and GSM.

In embodiments, the additive manufacturing system <NUM> may transmit captured images and/or log files related to builds to the server <NUM>, the user computing device <NUM>, and/or the mobile computing device <NUM>. The log files may include a plurality of parameters that are output from a plurality of subsystems of the additive manufacturing system <NUM> such as a vacuum system, a beam system, a powder layering system, and the like. The plurality of parameters may be raw parameters output from the additive manufacturing system <NUM>, or parameters further processed based on machine operations. For example, parameters may be processed based on domain knowledge and or physics to generate new features and/or parameters. The image data and/or log files may be stored in the server <NUM>, the user computing device <NUM>, and/or the mobile computing device <NUM>.

The server <NUM> generally includes processors, memory, and chipsets for delivering resources via the network <NUM>. Resources may include providing, for example, processing, storage, software, and information from the server <NUM> to the user computing device <NUM> via the network <NUM>. The server <NUM> may store machine learning models or statistical models on parameters from the additive manufacturing system <NUM>.

The user computing device <NUM> generally includes processors, memory, and chipsets for communicating data via the network <NUM>. The details of the user computing device <NUM> will be described below with reference to <FIG>.

The mobile computing device <NUM> may be any device having hardware (e.g., chipsets, processors, memory, etc.) for communicatively coupling with the network <NUM>. Specifically, the mobile computing device <NUM> may include an antenna for communicating over one or more of the wireless computer networks described above. Moreover, the mobile computing device <NUM> may include a mobile antenna for communicating with the network <NUM>. Accordingly, the mobile antenna may be configured to send and receive data according to a mobile telecommunication standard of any generation (e.g., <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, etc.). Specific examples of the mobile computing device <NUM> include, but are not limited to, smart phones, tablet devices, e-readers, laptop computers, or the like. The mobile computing device <NUM> may have a display similar to the display device <NUM> of the user computing device <NUM> and display user interfaces, e.g., interfaces illustrated in <FIG>, <FIG>, and <FIG>.

The network <NUM> generally includes a plurality of base stations that are configured to receive and transmit data according to mobile telecommunication standards. The base stations are further configured to receive and transmit data over wired systems such as public switched telephone network (PSTN) and backhaul networks. The network <NUM> may further include any network accessible via the backhaul networks such as, for example, wide area networks, metropolitan area networks, the Internet, satellite networks, or the like. Thus, the base stations generally include one or more antennas, transceivers, and processors that execute machine readable instructions to exchange data over various wired and/or wireless networks.

Turning to <FIG>, the various internal components of the control component <NUM> depicted in <FIG> are shown. Particularly, <FIG> depicts various system components for collecting parameters and images for operating the additive manufacturing system <NUM>, analyzing parameters and image data and/or assisting with the control of various components of the additive manufacturing system <NUM> depicted in <FIG>.

As illustrated in <FIG>, the control component <NUM> may include one or more processing devices <NUM>, a non-transitory memory component <NUM>, network interface hardware <NUM>, device interface hardware <NUM>, and a data storage component <NUM>. A local interface <NUM>, such as a bus or the like, may interconnect the various components.

The one or more processing devices <NUM>, such as a computer processing unit (CPU), may be the central processing unit of the control component <NUM>, performing calculations and logic operations to execute a program. The one or more processing devices <NUM>, alone or in conjunction with the other components, are illustrative processing devices, computing devices, processors, or combinations thereof. The one or more processing devices <NUM> may include any processing component configured to receive and execute instructions (such as from the data storage component <NUM> and/or the memory component <NUM>).

The memory component <NUM> may be configured as a volatile and/or a nonvolatile computer-readable medium and, as such, may include random access memory (including SRAM, DRAM, and/or other types of random access memory), read only memory (ROM), flash memory, registers, compact discs (CD), digital versatile discs (DVD), and/or other types of storage components. The memory component <NUM> may include one or more programming instructions thereon that, when executed by the one or more processing devices <NUM>, cause the one or more processing devices <NUM> to complete various processes.

Still referring to <FIG>, the programming instructions stored on the memory component <NUM> may be embodied as a plurality of software logic modules, where each logic module provides programming instructions for completing one or more tasks.

Still referring to <FIG>, the network interface hardware <NUM> may include any wired or wireless networking hardware, such as a modem, LAN port, wireless fidelity (Wi-Fi) card, WiMax card, mobile communications hardware, and/or other hardware for communicating with other networks and/or devices. For example, the network interface hardware <NUM> may be used to facilitate communication between the additive manufacturing system <NUM> and external devices such as the server <NUM>, the user computing device <NUM>, the mobile computing device <NUM> and the like via a network <NUM> as shown in <FIG>.

The device interface hardware <NUM> may communicate information between the local interface <NUM> and one or more components of the additive manufacturing system <NUM> of <FIG>. For example, the device interface hardware <NUM> may act as an interface between the local interface <NUM> and the imaging device <NUM> of <FIG>, the powder distributor <NUM>, and/or the like. In some embodiments, the device interface hardware <NUM> may transmit or receive signals and/or data to/from the imaging device <NUM> of <FIG>.

Still referring to <FIG>, the data storage component <NUM>, which may generally be a storage medium, may contain one or more data repositories for storing data that is received and/or generated. The data storage component <NUM> may be any physical storage medium, including, but not limited to, a hard disk drive (HDD), memory, removable storage, and/or the like. While the data storage component <NUM> is depicted as a local device, it should be understood that the data storage component <NUM> may be a remote storage device, such as, for example, a server computing device, cloud based storage device, or the like. Illustrative data that may be contained within the data storage component <NUM> includes, but is not limited to, image data <NUM>, machine learning (ML) data <NUM>, and/or operation data <NUM>. The image data <NUM> may generally be data that is used by the control component <NUM> to recognize particular objects, determine one or more points on the powder layer <NUM> (<FIG>), monitor an amount of electromagnetic radiation at the one or more points, determine a change in electromagnetic radiation, and/or the like. For example, the control component <NUM> may access the image data <NUM> to obtain a plurality of images received from the imaging device <NUM>, determine an amount of electromagnetic radiation from the image data <NUM>, and generate one or more commands accordingly.

Still referring to <FIG>, the ML data <NUM> may be data that is generated as a result of one or more machine learning processes or statistical modelling processes used to determine features of the powder layer <NUM> (<FIG>) from the image data <NUM>. Still referring to <FIG>, the operation data <NUM> may include parameters output from a plurality of subsystems from the additive manufacturing system <NUM>. For example, the operation data <NUM> may include parameters output from a vacuum system, a beam system, a powder layering system, and the like. Specifically, parameters for the beam system may include, but not be limited to, a maximum power supply voltage, a minimum power supply voltage, a filament burn time, an average preheat grid voltage, a grid voltage drop after arctrip, an average cathode power, an average effective work function, an average smoke counts, a smoke warning, an average column pressure, the number of arc trips, a maximum deviation in grid voltage, a grid voltage at 2mA, and the like. Parameters for the vacuum system may include, but not be limited to, a maximum chamber pressure, a minimum chamber pressure, a maximum column pressure, a minimum column pressure, a vacuum failure error, an average variation in chamber vacuum, a minimum helium supply line pressure, an average current in chamber turbo pump, an average current in column turbo pump, a turbo pump idle duration, an average internal circuit temperature, an average incoming cooling water temperature, and the like.

It should be understood that the components illustrated in <FIG> are merely illustrative and are not intended to limit the scope of this disclosure. More specifically, while the components in <FIG> are illustrated as residing within the control component <NUM>, this is a non-limiting example. In some embodiments, one or more of the components may reside external to the control component <NUM>.

<FIG> depicts the various internal components of the user computing device <NUM> depicted in <FIG>. As illustrated in <FIG>, the user computing device <NUM> may include one or more processing devices <NUM>, a non-transitory memory component <NUM>, network interface hardware <NUM>, a display device <NUM>, and a data storage component <NUM>. A local interface <NUM>, such as a bus or the like, may interconnect the various components. While <FIG> depicts the components of the user computing device <NUM>, the server <NUM> in <FIG> may have the same or similar components as illustrated in <FIG>.

The one or more processing devices <NUM>, such as a computer processing unit (CPU), may be the central processing unit of the user computing device <NUM>, performing calculations and logic operations to execute a program. The one or more processing devices <NUM>, alone or in conjunction with the other components, are illustrative processing devices, computing devices, processors, or combinations thereof. The one or more processing devices <NUM> may include any processing component configured to receive and execute instructions (such as from the data storage component <NUM> and/or the memory component <NUM>).

The memory component <NUM> may be configured as a volatile and/or a nonvolatile computer-readable medium and, as such, may include random access memory (including SRAM, DRAM, and/or other types of random access memory), read only memory (ROM), flash memory, registers, compact discs (CD), digital versatile discs (DVD), and/or other types of storage components. The memory component <NUM> may include one or more programming instructions thereon that, when executed by the one or more processing devices <NUM>, cause the one or more processing devices <NUM> to display information on the display device <NUM>, such as user interface illustrated in <FIG>, <FIG>, and <FIG>.

Still referring to <FIG>, the display device <NUM> may include any medium capable of transmitting an optical output such as, for example, a cathode ray tube, a light emitting diode (LED) display, an organic light emitting diode (OLED) display, a liquid crystal display, a plasma display, or the like. In embodiments, the display device <NUM> may be a touchscreen that, in addition to visually displaying information, detects the presence and location of a tactile input upon a surface of or adjacent to the display device <NUM>.

Still referring to <FIG>, the data storage component <NUM>, which may generally be a storage medium, may contain one or more data repositories for storing data that is received and/or generated. The data storage component <NUM> may be any physical storage medium, including, but not limited to, a hard disk drive (HDD), memory, removable storage, and/or the like. While the data storage component <NUM> is depicted as a local device, it should be understood that the data storage component <NUM> may be a remote storage device, such as, for example, a server computing device, cloud based storage device, or the like. The data storage component <NUM> includes failure mode data <NUM>, and optionally a data science model <NUM>, parameter data <NUM>.

The data science model <NUM> is a confidence model obtained from a trained machine learning model or a statistical model. The trained machine learning model or statistical model is a machine learning model or a statistical model trained based on log files including numerous parameters, a list of desired parameters and/or time series data. The data science model <NUM> may be developed based on four steps: data extractions, data transformation and compression, feature extractions, and feature selections. The data extractions may include extracting time series, events such as process start and end times, and status/error messages related to builds manufactured by the additive manufacturing system <NUM>. The feature extractions may extract features such as statistical features, transient behavior features, abnormal deviations/exceedances, domain (process and machine) based features, and the like related to builds manufactured by the additive manufacturing system <NUM>. The features selection may include selecting statistical analysis methods and machine learning classifiers or statistical model classifiers. The data science model <NUM> may include threshold parameters for determining a condition of subsystems of the additive manufacturing system <NUM>.

The parameter data <NUM> may include a plurality of parameters that are output from a plurality of subsystems of the additive manufacturing system <NUM> such as a vacuum system, a beam system, a powder layering system, and the like. The plurality of parameters may be raw parameters output from the additive manufacturing system <NUM>, or parameters further processed based on machine operations. The parameters may include parameters extracted from the image data <NUM> of the control component <NUM>.

The failure mode data <NUM> includes a plurality of failure modes. Each of the failure modes is associated with one or more of the subsystems of the additive manufacturing system <NUM>. The failure mode may include, but not be limited to, a rake stuck, a cathode contamination or damage, a vacuum failure and the like. The failure mode may include one or more root cause identifications. For example, the failure mode of the cathode contamination or damage may include root causes such as a cathode contamination, an arc trip, or a wrong position of the cathode, and the like. Each of the root causes may be associated with analysis of the parameters of the additive manufacturing system <NUM>. For example, if the parameters of additive manufacturing system <NUM> indicate lower brightness, a cathode contamination may be determined as a root cause. Each of the failure modes may be determined based on comparison of the parameters for the additive manufacturing system <NUM> and threshold parameters stored in the data science model <NUM>.

<FIG> depicts a flow chart for diagnosing an additive manufacturing device and providing diagnosis results, according to one or more embodiments show and described herewith.

In step <NUM>, a system may determine parameters associated with at least one subsystem of an additive manufacturing device. For example, the processor of the user computing device <NUM> may determine parameters associated with at least one subsystem of an additive manufacturing system <NUM>. The parameters may be related to a build generated by the additive manufacturing system <NUM>. For example, the parameters may be direct outputs of one or more subsystems of the additive manufacturing system <NUM>. As another example, the parameters may be new parameters further processed based on machine operations. Specifically, the new parameters may be generated by processing raw outputs based on domain knowledge and/or physics.

In embodiments, the processor of the user computing device <NUM> may receive a request for analyzing a log file for a build and extract parameters associated with subsystems of the additive manufacturing system <NUM> from the log file. For example, by referring to <FIG>, the display device <NUM> of the user computing device <NUM> may display a screen including an icon <NUM> for analyzing a new log file. In response to the activation of the icon <NUM>, a window <NUM> showing a list of log files is displayed. A user may select a log file <NUM> and request for analyzing the log file. In response, the processor of the user computing device <NUM> may extract parameters from the log file.

Referring back to <FIG>, in step <NUM>, the processor of the user computing device <NUM> may compare the parameters with threshold values. The threshold values may be determined based on machine learning or statistical models based on good or bad data, extracted features, and desired modules. For example, by referring to <FIG>, a smoke error threshold value may be determined based on good or bad build data, and distinction among bad build data. Specifically, <FIG> illustrates a set of good builds <NUM> associated with smoke error parameters and two sets of bad builds <NUM> and <NUM> associated with smoke error parameters. The two sets of bad builds include a set of smoke issue related bad builds <NUM> and a set of non-smoke related bad builds <NUM>. A value <NUM> between a set of smoke error parameters for the smoke issue related bad builds <NUM> and a set of smoke error parameters for non-smoke related bad builds <NUM> is determined as a threshold value for detecting a smoke error. For example, the value <NUM> may be <NUM>. As another example, by referring to <FIG>, a rake failure detection threshold value may be determined based on good or bad build data, and distinction among bad build data. Specifically, <FIG> illustrates a set of good builds <NUM> associated with rake failure detection parameters and two sets of bad builds <NUM> and <NUM> associated with rake failure detection parameters. The two sets of bad builds include a set of rake issue related bad builds <NUM> and a set of non-rake related bad builds <NUM>. A value <NUM> between a set of rake failure detection parameters for the rake issue related bad builds <NUM> and a set of rake failure detection parameters for non-rake related bad builds <NUM> is determined as a threshold value for detecting a rake failure. For example, the value <NUM> may be <NUM>.

Referring back to <FIG>, in step <NUM>, the processor of the user computing device <NUM> may determine a failure mode, among a plurality of failure modes, associated with a subsystem of the at least one subsystem of the additive manufacturing system based on the comparison of the parameters with the threshold values. For example, the user computing device <NUM> may determine a smoke error parameter from the log file selected by the user. If the smoke error parameter is <NUM>, then the smoke error parameter for the build is greater than the threshold value (<NUM>) for a smoke error shown in <FIG>. Then, the processor of the user computing device <NUM> may determine that a smoke error occurred associated with a beam system of the additive manufacturing system <NUM>. As another example, the user computing device <NUM> may determine a rake failure detection parameter from the log file selected by the user. If the rake failure detection parameter is <NUM>, the rake failure detection parameter for the build is greater than the threshold value (<NUM>) for a rake failure detection shown in <FIG>. Then, the processor of the user computing device <NUM> may determine that a rake failure occurred associated with a powdering layer system of the additive manufacturing system <NUM>.

Referring to <FIG>, in step <NUM>, the processor of the user computing device <NUM> may determine a cause for a failure in the subsystem associated with the failure mode. For example, if the failure mode is a rake failure, the cause for the failure may include, but not be limited to, old software, part swelling, or loss of lubrication in the rake mechanism. In embodiments, the processor of the user computing device <NUM> may determine a cause for a failure based on parameters for the build. For example, if the rake current is relatively high, e.g., greater than <NUM> ampere, the processor of the user computing device <NUM> may determine that old software is the cause for the failure mode.

In step <NUM>, the processor of the user computing device <NUM> may display, on a screen, the failure mode and the subsystem associated with the failure mode. For example, by referring to <FIG>, the display device <NUM> of the user computing device <NUM> may display a page <NUM> showing windows for three subsystems of the additive manufacturing system <NUM>: a vacuum subsystem window <NUM>, a beam subsystem window <NUM>, and a powder layering subsystem window <NUM>. The processor of the user computing device <NUM> may determine that the failure mode of cathode contamination has occurred for a build. The display device <NUM> may display the failure mode <NUM> of cathode contamination in the beam subsystem window <NUM>. The indication and location of the failure mode <NUM> is not limited to the indication and location shown in <FIG>, and any other indication for the failure mode may be displayed at a different location.

Referring back to <FIG>, in step <NUM>, the processor of the user computing device <NUM> may display information about the build being analyzed on the display device <NUM>. For example, as shown in <FIG>, information about the build is displayed in a window <NUM>. The information about the build may include information about the machine (e.g., serial number, type of machine, etc.), information about the powder utilized, theme information, software information, information related to current layer thickness, information related to target z height, information related to last z height, build start time information, build stop time information, build name information, build envelope identification, heat build platform time, and process time. The vacuum subsystem window <NUM>, the beam subsystem window <NUM>, and the powder layering subsystem window <NUM> may include the status or failure mode of the vacuum subsystem, the beam subsystem, and the powder layering subsystem associated with the build.

Referring back to <FIG>, in step <NUM>, the processor of the user computing device <NUM> may display an icon proximate to the indication of the failure mode on the display device <NUM>. For example, as illustrated in <FIG>, an icon <NUM> is displayed on the page <NUM>. A user may click or select the icon <NUM> on the page <NUM> to receive additional information about the failure mode. The shape and location of the icon <NUM> is not limited to the icon <NUM> shown in <FIG>, and the icon <NUM> may have any other shape and may be located at different locations.

Referring back to <FIG>, in step <NUM>, in response to an activation of the icon <NUM>, the processor of the user computing device <NUM> may display, on the screen, the cause for the failure in the subsystem associated with the failure mode, and instructions for addressing the failure mode. For example, by referring to <FIG>, the display device <NUM> display a screen <NUM> showing a cause <NUM> for the failure mode, e.g., "Cathode Contaminated," and instructions <NUM> for addressing the failure mode, e.g., "Examine and change the cathode. " The user may follow the instructions <NUM> and change the cathode. Once the change is done, the user may check the box <NUM> to indicate that an action has been taken according to the instructions. The user may also input comments in the box <NUM> for future reference. The user computing device <NUM> may track the user's actions, e.g., based on an activation of the box <NUM> and determine whether the additive manufacturing system <NUM> operates without the corresponding failure after the action has been taken.

In embodiments, the diagnosing of the additive manufacturing system <NUM> may be implemented in real time while a build is being manufactured. For example, while the build is being manufactured, parameters such as on-machine sensor signals may be transmitted from the additive manufacturing system <NUM> to the user computing device <NUM> in real time, and the user computing device <NUM> analyzes the parameters to determine any failure mode. In some embodiments, the diagnosing of the additive manufacturing system <NUM> may be implemented after the build has been manufactured. For example, parameters such as on-machine sensor signals may be included in a log file and transmitted from the additive manufacturing system <NUM> to the server <NUM>. Subsequently, the user computing device <NUM> may retrieve the log file and extract parameters from the log file. Then, the user computing device <NUM> may analyze the extracted parameters to determine a failure mode, and display the failure mode. In some embodiments, after a build is completed, a log file for the build is automatically analyzed, and the analysis result is stored or provided to the user computing device <NUM>.

<FIG> depicts a flow chart for diagnosing an additive manufacturing device and providing diagnosis results, according to another embodiment show and described herewith.

In step <NUM>, a processor of the user computing device <NUM> may determine parameters associated with at least one subsystem of an additive manufacturing system <NUM>. The parameters may be related to a build generated by the additive manufacturing system <NUM>. For example, the parameters may be direct outputs of one or more subsystems of the additive manufacturing system <NUM>. As another example, the parameters may be new parameters further processed based on machine operations. Specifically, the new parameters may be generated by processing raw outputs based on domain knowledge and/or physics.

In step <NUM>, the processor of the user computing device <NUM> may compare the parameters with threshold values. The threshold values may be determined based on machine learning or statistical models on good or bad data, extracted features, and desired modules, as described above with reference to <FIG>.

In step <NUM>, the processor of the user computing device <NUM> may determine a failure mode, among a plurality of failure modes, associated with a subsystem of the at least one subsystem of the additive manufacturing system based on the comparison of the parameters with the threshold values. For example, the user computing device <NUM> may determine a grid voltage drop after arctrip parameter from the log file selected by the user. If the grid voltage drop after arctrip parameter is greater than a threshold value, the processor of the user computing device <NUM> may determine that a cathode error occurred associated with a beam system of the additive manufacturing system <NUM>. As another example, the user computing device <NUM> may determine a column pressure from the log file selected by the user. If the column pressure parameter is greater than a maximum column pressure threshold value, the processor of the user computing device <NUM> may determine that a vacuum failure occurred associated with a vacuum subsystem of the additive manufacturing system <NUM>.

In step <NUM>, the processor of the user computing device <NUM> may determine a cause for a failure in the subsystem associated with the failure mode. For example, if the failure mode is a cathode failure, the cause for the cathode failure may be cathode contamination, arc trip, a wrong position of the cathode, and the like. In embodiments, the processor of the user computing device <NUM> may determine a cause for a failure based on parameters for the build. For example, if the grid voltage dropped suddenly due to an arc trip, the processor of the user computing device <NUM> may determine that arc trip is the cause for the failure mode.

In step <NUM>, the processor of the user computing device <NUM> may display a plurality of tabs for a plurality of subsystems on the display device <NUM>. For example, by referring to <FIG>, a tab <NUM> for a vacuum subsystem, a tab <NUM> for a beam subsystem, and a tab <NUM> for a powder layering subsystem may be displayed on the display device <NUM>.

Referring back to <FIG>, in step <NUM>, the processor of the user computing device <NUM> receives an activation of one of the plurality of tabs. For example, a user may click or activate the tab <NUM> for the beam system.

In step <NUM>, the processor of the user computing device <NUM> displays one or more components for the subsystem associated with the activated tab. Each of the one or more components includes one or more features. For example, by referring to <FIG>, when the tab <NUM> is activated by the user, the display device <NUM> may display four components associated with the tab <NUM>: a high voltage power supply component <NUM>, a cathode health component <NUM>, a smoke component <NUM>, and a grid cup status component <NUM>. Each of the components may include and display one or more features. For example, the cathode health component <NUM> may include and display five features: a filament burn time, an average preheat grid voltage, a grid voltage drop after arctrip, an average cathode power, and an average effective work function.

Referring back to <FIG>, in step <NUM>, the processor of the user computing device <NUM> may display an actual parameter and a corresponding threshold value for each of the one or more features on the display device <NUM>. For example, as illustrated in <FIG>, for each of the five features including the filament burn time, the average preheat grid voltage, the grid voltage drop after arctrip, the average cathode power, and the average effective work function, actual parameters and corresponding threshold values are displayed. In embodiments, if an actual parameter is less than a minimum threshold, or is greater than a maximum threshold, the display device <NUM> may display a warning indicator proximate to the actuator parameter. For example, by referring to <FIG>, the actuator parameter for a grid voltage drop after arctrip is <NUM> volts whereas a maximum threshold value is <NUM> volts. The display device <NUM> may show a warning indication such as a check icon <NUM> next to the actual parameter because the actual parameter exceeds a maximum threshold value.

Referring back to <FIG>, in step <NUM>, the processor of the user computing device <NUM> may display, for the one or more features, a check icon and a graph icon. For example, by referring to <FIG>, a check icon <NUM> and a graph icon <NUM> are displayed next to the feature of grid voltage drop after arctrip in the window for the cathode health component <NUM>.

Referring back to <FIG>, in step <NUM>, the processor of the user computing device <NUM> may display information of the failure mode associated with the feature in response to activation of the explanation icon. For example, in response to the activation of the check icon <NUM> in <FIG>, the display device <NUM> may display a window <NUM> stating explanation of the failure mode as shown in <FIG>.

Referring back to <FIG>, in step <NUM>, the processor of the user computing device <NUM> may display a graph illustrating a changing value of the parameter for the feature corresponding to the activated graph icon in response to activation of the graph icon. For example, in response to the activation of the graph icon <NUM> in <FIG>, the display device <NUM> may display a graph <NUM> illustrating a changing value of a preheat grid voltage for a cathode with respect to a height of a build as shown in <FIG>.

It should now be understood that that the devices, systems, and methods described herein diagnose an additive manufacturing device. The systems and methods determine parameters associated with at least one subsystem of the additive manufacturing device, compare the parameters with threshold values, and determine a failure mode, among a plurality of failure modes, associated with a subsystem of the at least one subsystem of the additive manufacturing device based on the comparison of the parameters with the threshold values. The analyzed output is visualized through a display device indicating current health status of the additive manufacturing device or subsystems thereof. Accordingly, the present disclosure provides users with visually interactive interface that provides guided steps in diagnosing build/module level issues in a matter of few minutes.

Claim 1:
A system (<NUM>) for diagnosing an additive manufacturing device (<NUM>), the system (<NUM>) comprising:
the additive manufacturing device (<NUM>);
one or more processors (<NUM>);
one or more non-transitory memory modules (<NUM>) communicatively coupled to the one or more processors (<NUM>) and configured to store machine-readable instructions that, when executed, cause the one or more processors (<NUM>) to:
determine parameters associated with at least one subsystem of the additive manufacturing device (<NUM>), the parameters being related to a build generated by the additive manufacturing device (<NUM>); and
compare the parameters with threshold values; characterized in that the one or more non-transitory memory modules (<NUM>) communicatively coupled to the one or more processors (<NUM>) are configured to store machine-readable instructions that, when executed, cause the one or more processors (<NUM>) to:
determine a failure mode, among a plurality of failure modes included in failure mode data (<NUM>) included in a data storage component (<NUM>) of the system (<NUM>), the failure mode being associated with a subsystem of the at least one subsystem of the additive manufacturing device (<NUM>) based on the comparison of the parameters with the threshold values.