Complex feature cloning in additive manufacturing datasets

A computer-implemented method for generating a manufacturing dataset having cloned geometric features for an additive manufacturing process includes identifying, via a processor, a repeating design feature in a first manufacturing dataset that defines a three-dimensional workpiece. The method further includes determining, via the processor, a processing cost or benefit for cloning the repeating design feature, and generating, via the processor, a second dataset having with repeating design feature isolated responsive to the determined processing benefit. The method includes generating, via the processor, a third dataset from the first manufacturing dataset. The third dataset that replaces the repeating design feature with a reference to the second dataset having the repeating design feature.

BACKGROUND

Exemplary embodiments pertain to the art of additive manufacturing, and more particularly to optimizing additive manufacturing datasets with complex feature cloning.

Additive manufacturing processes such as selective laser sintering (SLS) use a high power laser (for example, a carbon dioxide laser) to fuse small particles of plastic, metal, ceramic, or glass powders into a mass that has a desired three-dimensional shape. The laser selectively fuses powdered material by scanning cross-sections generated from a three dimensional (3-D) digital description (model) of the part (for example from a CAD file or scan data) on the surface of a powder bed. After each cross-section is scanned, the powder bed is lowered by one layer thickness, a new layer of material is applied on top, and the process is repeated until the part is completed. The model of the part is defined with one or more datasets that define workpiece geometry at each cross-section. The dataset describes the surface geometry of each respective layer with a raw unstructured triangulated surface by the unit normal and vertices of triangles using a 3-D Cartesian coordinate system. The dataset can become extremely complex in terms of file size when the workpiece features are highly complex.

Some workpieces may include several repeating units of design features. When the repeated design features include geometric details, considerable computational resources may be needed. The overall size of the dataset used to manufacture the part can become computationally difficult implement because of the volume of data. For example, when an additive manufacturing software slices a 3-D digital part model for an additive manufacturing build, it must assess every surface individually. Large parts with multiple complex features can become too cumbersome to processes.

It may be advantageous to optimize the additive manufacturing process with a system that makes repeated use of workpiece feature data (cloning). It may be advantageous to configure the system to identify features that repeat, determine whether a cloning process optimizes the dataset, and output new simplified datasets using one or more clones of the repeating feature to save processing resources.

BRIEF DESCRIPTION

Disclosed is a computer-implemented method for generating a manufacturing dataset having cloned geometric features for an additive manufacturing process. The method includes identifying, via a processor, a repeating design feature in a first manufacturing dataset that defines a three-dimensional workpiece. The method further includes determining, via the processor, a processing cost or benefit for cloning the repeating design feature, and generating, via the processor, responsive to the determined processing benefit. The second dataset has the repeating design feature isolated. The method includes generating, via the processor, a third dataset from the first manufacturing dataset. For each instance of the repeating design feature, the third dataset replaces the repeating design feature with a reference to the second dataset having the repeating design feature.

Also disclosed is a system for generating a manufacturing dataset having cloned geometric features for an additive manufacturing process. The system includes a processor configured to identify a repeating design feature in a first manufacturing dataset that defines a three-dimensional workpiece. The system also determines a processing cost or benefit for cloning the repeating design feature, and generates a second dataset with the repeating design feature isolated, responsive to the determined processing benefit. The system generates a third dataset from the first manufacturing dataset. For each instance of the repeating design feature, the third dataset replaces the repeating design feature with a reference to the second dataset having the repeating design feature.

A computer program product for generating a manufacturing dataset having cloned geometric features for an additive manufacturing process is also disclosed. The computer program product includes a computer readable storage medium storing program instructions. The program instructions are executable by a processor to cause the processor to perform a method. The method includes identifying, via a processor, a repeating design feature in a first manufacturing dataset that defines a three-dimensional workpiece. The method further includes determining, via the processor, a processing cost or benefit for cloning the repeating design feature, and generating, via the processor, responsive to the determined processing benefit. The second dataset has the repeating design feature isolated. The method includes generating, via the processor, a third dataset from the first manufacturing dataset. For each instance of the repeating design feature, the third dataset replaces the repeating design feature with a reference to the second dataset having the repeating design feature.

DETAILED DESCRIPTION

FIG. 1is a diagram of an exemplary computing environment100, according to an embodiment. Computing environment100includes a machine102for additive manufacturing. Machine102may be, for example, a selective laser sintering (SLS) machine, a fused deposition modeling (FDM) machine, a stereolithography (SLA) machine, or another machine for additive manufacturing processes. Machine102may include an integrated computer or be operatively connected to an auxiliary computer104. Computer104and machine102may include similar computing features as computer500depicted with respect toFIG. 5.

FIG. 2is a flow diagram of a method for generating a dataset configured for use in machine102. Embodiments described herein may generate datasets having cloned geometric features. The generated datasets may be significantly smaller than the original manufacturing datasets having multiple instances the repeating features. Referring now toFIG. 2, at step202, a system processor (e.g., processor501inFIG. 5) may identify a repeating design feature in a first manufacturing dataset that defines a three-dimensional workpiece.

According to one embodiment, processor501is configured to identify the repeating design feature in the first manufacturing dataset based on user input indicative of the repeating design feature. According to another embodiment, processor501is further configured to identify the repeating design feature in the first manufacturing dataset without user input indicative of the repeating design feature. Accordingly, processor501is configured evaluate the dataset for blocks of surface data having multiple surfaces with a particular size oriented with adjacent surfaces in a similar fashion. For instance, processor501may identify three surfaces A, B, and C, all adjacent to one another, and determine that three other surfaces D, E, and F, are adjacent to one another with an identical orientation with respect to one another as surfaces A, B, and C, but differing only in a relative location in the workpiece. Stated in a different way, processor501determines that surfaces A, B, and C are likely identical repeats of surfaces D, E, and F.

Processor501may determine whether the surfaces are actually identical. In other embodiments, processor501may map a surface A, to a surface D, and evaluate a difference in area between A and D. When the area difference is less than a predetermined threshold (e.g., 0.02%, etc.), processor501may map and compare similar adjacent surfaces in the detected instances of similar blocks of surface data, until processor501determines within a predetermined threshold of error that A, B, and C identify a complete feature, and D, E, and F, are a repeat instance of that complete feature. In some embodiments, processor501can identify a feature by surfaces positively matched to other surfaces in the workpiece. In other embodiments, processor may use splines, line lengths, relative distances between feature vertices, or other methods to match portions of repeating features.

At step204, processor501may determine a processing cost or benefit for cloning the repeating design feature with respect to an unaltered (complete) manufacturing file. An example of a manufacturing file may be, for example, a .STL or the like. The processing cost or benefit is used to determine whether cloning the repeating feature will benefit the final output dataset by simplifying the instructions for building the part in machine102. If the final instructions are larger with the cloned feature removed then the cloning is said to have a cost greater than the benefit. The processing cost or benefit is based on a geometric complexity of the repeating design feature indicated by three-dimensional surface information, and the design feature is repeated in a Z-axis respective to a Cartesian coordinate system. Processor501may compare the cost or benefit to a predetermined threshold value. For example, 20%, 30%, etc. According to one embodiment, the processing cost or benefit is based on a geometric complexity of the repeating design feature indicated by three-dimensional surface information. For example, a simple boss repeated twenty times in the X-Y plane of a Cartesian coordinate system may offer limited benefit compared to a complex geometry that is repeated, as shown inFIG. 3.

Referring briefly toFIG. 3, exemplary workpiece302having a cloned feature304is depicted, according to an embodiment. Cloned feature304is repeated multiple times in the Z-direction of part302. The geometry of the repeating design features304can take considerable computing power and storage to represent the complex 3-D surfaces and shapes. By multiplying repeating design feature304multiple times as show in workpiece302, a dataset may become too large to manage by processor501. Repeating feature304is repeated in the Z direction with respect to a flat orientation of workpiece302.

Returning again toFIG. 2, processor501may determine a processing data requirement indicative of a volume of data storage necessary for the repeating design feature304. Processor501may generate a count of a number of times the repeating design feature is repeated in the first manufacturing dataset, and evaluate a modified sample of the first manufacturing dataset having the repeating design feature removed. For example, a modified dataset may include design feature304removed, and instead include one or more placeholders that position that instance of the repeating feature relative to the workpiece. Processor501may generate placeholder(s) for each respective instance of the repeated design feature.

In the example ofFIG. 3, the modified dataset may include a series of points or locations for orienting each of the design features304. In the example of workpiece302, the entire part can be represented using the repeating features exclusively, without any underlying structure between them. According to other embodiments, the modified dataset may be a workpiece body with the repeating features removed, and in their place, a point location or “placeholder” for each instance. Processor501may determine the processing cost or benefit by comparing the processing cost or benefit for cloning the repeating design feature304based on the processing data requirement, the count of the number of instances of the repeating design feature304, and the modified sample of the first manufacturing dataset.

At step206, processor501may generate a second dataset having the repeating design feature isolated responsive to the determined processing benefit. The second dataset includes information for the repeated design feature304in isolation, as shown inFIG. 3.

At step208, processor501may generate a third dataset from the first manufacturing dataset. The third dataset replaces the repeating design feature with a reference to the second dataset that includes only the repeating design feature in isolation.

According to one embodiment, processor501may determine whether the repeating design feature is coterminous with an adjacent instance of the repeating design feature, and determine an overlap distance for the repeating design feature and the adjacent instance of the repeating design feature. In one aspect, processor501may request user input regarding the overlap distance, and receive the user input with which processor502determines the overlap. Processor501may generate the third dataset, responsive to determining that the repeating design feature is coterminous with the adjacent instance of the repeating design feature. The repeating design feature is coterminous with the adjacent instance when a first instance connects to a second instance in a particular way (at a specific point relative to the instance), and the same relative point in the second instance connects to the next sequential instance at the same relative point. The third dataset includes a reference location for the reference to the second dataset having the repeating design feature based on the overlap distance. The reference location may be, for example, Cartesian coordinates respective to an orienting location of the workpiece. In one embodiment, processor501is configured to generate the third dataset based on one or more user inputs indicative of an additive manufacturing process, an additive manufacturing material, and a finish tolerance.

FIG. 4is side view of the exemplary workpiece ofFIG. 3according to an embodiment. As shown inFIG. 4, a portion of repeating feature304is coterminous with an adjacent clone shown immediately to the left. Clones402may be a series of repeating design features, where each respective portion of the clone is coterminous with the next adjacent clone in the same way as the previous instance.

Embodiments described herein may provide optimized additive engineering 3-D part build files by reducing the volume part information needed to build the part. In some instances, embodiments may optimize additive manufacturing of parts with multiple features that were cumbersome or impossible to processes by additive manufacturing machines due to part complexity.

FIG. 5is a system for generating a dataset having cloned geometric features according to an embodiment.FIG. 5illustrates a block diagram of an exemplary computing environment and computer system500for use in practicing the embodiments described herein. The environment and system described herein can be implemented in hardware, software (e.g., firmware), or a combination thereof. In an exemplary embodiment, a hardware implementation may include a microprocessor of a special or general-purpose digital computer, such as a personal computer, workstation, minicomputer, or mainframe computer. Computer500therefore can embody a general-purpose computer. In another exemplary embodiment, the implementation can be part of a mobile device, such as, for example, a mobile phone, a personal data assistant (PDA), a tablet computer, etc.

As shown inFIG. 5, the computer500includes processor501. Computer500also includes memory502communicatively coupled to processor501, and one or more input/output adapters503that may be communicatively coupled via system bus505. Memory502may be communicatively coupled to one or more internal or external memory devices via a storage interface508. Communications adapter516may communicatively connect computer500to one or more networks506. System bus505may communicatively connect one or more user interfaces via input/output (I/O) adapter503. I/O adapter503may connect a plurality of input devices504to computer500. Input devices may include, for example, a keyboard, a mouse, a microphone, a sensor, etc. System bus505may also communicatively connect one or more output devices507via I/O adapter503. Output device507may include, for example, a display, a speaker, a touchscreen, etc.

Processor501is a hardware device for executing program instructions (aka software), stored in a computer-readable memory (e.g., memory502). Processor501can be any custom made or commercially available processor, a central processing unit (CPU), a plurality of CPUs, for example, CPU501a-501c, an auxiliary processor among several other processors associated with the computer500, a semiconductor based microprocessor (in the form of a microchip or chip set), or generally any device for executing instructions. Processor501can include a cache memory522, which may include, but is not limited to, an instruction cache to speed up executable instruction fetch, a data cache to speed up data fetch and store, and a translation lookaside buffer (TLB) used to speed up virtual-to-physical address translation for both executable instructions and data. Cache memory522may be organized as a hierarchy of more cache levels (L1, L2, etc.).

Processor501may be disposed in communication with one or more memory devices (e.g., RAM509, ROM510, one or more external databases521, etc.) via a storage interface508. Storage interface508may also connect to one or more memory devices including, without limitation, one or more databases521, and/or one or more other memory drives (not shown) including, for example, a removable disc drive, etc., employing connection protocols such as serial advanced technology attachment (SATA), integrated drive electronics (IDE), IEEE-1394, universal serial bus (USB), fiber channel, small computer systems interface (SCSI), etc. The memory drives may be, for example, a drum, a magnetic disc drive, a magneto-optical drive, an optical drive, a redundant array of independent discs (RAID), a solid-state memory device, a solid-state drive, etc. Variations of memory devices may be used for implementing, for example, list all databases from other figures.

Memory502can include random access memory (RAM)509and read only memory (ROM)510. RAM509can be any one or combination of volatile memory elements (e.g., DRAM, SRAM, SDRAM, etc.). ROM510can include any one or more nonvolatile memory elements (e.g., erasable programmable read only memory (EPROM), flash memory, electronically erasable programmable read only memory (EEPROM), programmable read only memory (PROM), tape, compact disc read only memory (CD-ROM), disk, cartridge, cassette or the like, etc.). Moreover, memory502may incorporate electronic, magnetic, optical, and/or other types of non-transitory computer-readable storage media. Memory502may also be a distributed architecture, where various components are situated remote from one another, but can be accessed by processor501.

The instructions in memory502may include one or more separate programs, each of which may include an ordered listing of computer-executable instructions for implementing logical functions. In the example ofFIG. 5, the instructions in memory502may include an operating system511. Operating system511can control the execution of other computer programs and provides scheduling, input-output control, file and data management, memory management, and communication control and related services.

The program instructions stored in memory502may further include application data512, and for a user interface513.

Memory502may also include program instructions for a additive manufacturing cloning engine514, configured for identifying, via processor501, a repeating design feature in a first manufacturing dataset that defines a three-dimensional workpiece. The method further includes determining, via processor501, a processing cost or benefit for cloning the repeating design feature, and generating, via processor501, a second dataset having with repeating design feature isolated responsive to the determined processing benefit. The program instructions may be further configured for generating, via processor501, a third dataset from the first manufacturing dataset. The third dataset replaces the repeating design feature with a reference to the second dataset having the repeating design feature.

I/O adapter503can be, for example but not limited to, one or more buses or other wired or wireless connections. I/O adapter503may have additional elements (which are omitted for simplicity) such as controllers, microprocessors, buffers (caches), drivers, repeaters, and receivers, which may work in concert to enable communications. Further, I/O adapter503may facilitate address, control, and/or data connections to enable appropriate communications among the aforementioned components.

I/O adapter503can further include a display adapter coupled to one or more displays. I/O adapter503may be configured to operatively connect one or more input/output (I/O) devices507to computer500. For example, I/O503may connect a keyboard and mouse, a touchscreen, a speaker, a haptic output device, or other output device. Output devices507may include but are not limited to a printer, a scanner, and/or the like. Other output devices may also be included, although not shown. Finally, the I/O devices connectable to I/O adapter503may further include devices that communicate both inputs and outputs, for instance but not limited to, a network interface card (NIC) or modulator/demodulator (for accessing other files, devices, systems, or a network), a radio frequency (RF) or other transceiver, a telephonic interface, a bridge, a router, and the like.

According to some embodiments, computer500may include a mobile communications adapter523. Mobile communications adapter523may include GPS, cellular, mobile, and/or other communications protocols for wireless communication.

In some embodiments, computer500can further include communications adapter516for coupling to a network506.

Network506can be an IP-based network for communication between computer500and any external device. Network506transmits and receives data between computer500and devices and/or systems external to computer500. In an exemplary embodiment, network506can be a managed IP network administered by a service provider. Network506may be a network internal to an aircraft, such as, for example, an avionics network, etc. Network506may be implemented in a wireless fashion, e.g., using wireless protocols and technologies, such as WiFi, WiMax, etc. Network506may also be a wired network, e.g., an Ethernet network, an ARINC 429 network, a controller area network (CAN), etc., having any wired connectivity including, e.g., an RS232 connection, R5422 connection, etc. Network506can also be a packet-switched network such as a local area network, wide area network, metropolitan area network, Internet network, or other similar type of network environment. The network506may be a fixed wireless network, a wireless local area network (LAN), a wireless wide area network (WAN) a personal area network (PAN), a virtual private network (VPN), intranet or other suitable network system.

Network506may operatively connect computer500to one or more devices including device517, device518, and device520. Network506may also connect computer500to one or more servers such as, for example, server519.

If computer500is a PC, workstation, laptop, tablet computer and/or the like, the instructions in the memory502may further include a basic input output system (BIOS) (omitted for simplicity). The BIOS is a set of routines that initialize and test hardware at startup, start operating system511, and support the transfer of data among the operatively connected hardware devices. The BIOS is typically stored in ROM510so that the BIOS can be executed when computer500is activated. When computer500is in operation, processor501may be configured to execute instructions stored within the memory502, to communicate data to and from the memory502, and to generally control operations of the computer500pursuant to the instructions.