Patent Publication Number: US-2022227062-A1

Title: In situ monitoring of stress for additively manufactured components

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a division of U.S. application Ser. No. 16/166,931 filed Oct. 22, 2018, issued as U.S. Pat. No. 11,292,198, Issued Apr. 5, 2022, the disclosure of which is incorporated herein by reference in its entirety. 
    
    
     BACKGROUND 
     Conventional additive manufacturing processes have limited or no closed loop controls and, therefore, rely on final material property assessments of a finished manufactured part or product. Specifically, conventional additive manufacturing utilizes post deposition analysis to provide these assessments. 
     BRIEF DESCRIPTION 
     In accordance with one or more embodiments, a material deposition process including in situ sensor analysis of a component in a formation state is provided. The material deposition process is implemented in part by an X-ray source and an X-ray detector of an additive manufacturing machine producing the component. The material deposition process includes sensing, by the X-ray source and the X-ray detector, in situ physical properties of an area of interest of the component during a three-dimensional object production. Compliance to specifications or defects are then detected in the in situ physical properties with respect to pre-specified material requirements. The defects are analyzed to determine corrective actions, and an updated three-dimensional object production, which includes the corrective actions, is implemented to complete the component. 
     In accordance with one or more embodiments or the material deposition process embodiment above, the material deposition process can include implementing the three-dimensional object production of the component according to a computer design file. 
     In accordance with one or more embodiments or any of the material deposition process embodiments above, the material deposition process can include feeding forward and back the corrective actions to the three-dimensional object production in real time to generate the updated three-dimensional object production. 
     In accordance with one or more embodiments or any of the material deposition process embodiments above, the at least one sensing device can include an X-ray source and X-ray detector that together acquire a full or partial X-ray diffraction signal or pattern that is analyzed to determine the in situ physical properties. 
     In accordance with one or more embodiments or any of the material deposition process embodiments above, the in situ physical properties can potentially include: hardness, local strain, yield strength, density, crystallite size, porosity, defect density, crystalline orientation, texture, and compositional variation. 
     In accordance with one or more embodiments or any of the material deposition process embodiments above, a compute device can include a processor executing software to provide one or more process modeling, toolpath planning, defect detection, layer defect detection, part defect detection, feedback control, scan path planning, decision making, and process sensing operations for detecting the defects. 
     In accordance with one or more embodiments or any of the material deposition process embodiments above, a compute device can include a database storing and providing the pre-specified material requirements and a computer design file for detecting the defects and implementing the three-dimensional object production. 
     In accordance with one or more embodiments, a system for implementing a three-dimensional object production of a component via an additive manufacturing is provided. The system includes an additive manufacturing machine including an X-ray source and an X-ray detector. The system also includes a compute device including a processor and a memory. The compute device is communicatively coupled to the additive manufacturing machine and the X-ray source and the X-ray detector. The additive manufacturing machine and the compute device provide in situ sensor analysis of the component while in a formation state during a material deposition process of the additive manufacturing by sensing, by the X-ray source and the X-ray detector, in situ physical properties of an area of interest of the component during a three-dimensional object production. Compliance to specifications or defects are then detected in the in situ physical properties with respect to pre-specified material requirements. The defects are analyzed to determine corrective actions, and an updated three-dimensional object production, which includes the corrective actions, is implemented to complete the component. 
     In accordance with one or more embodiments or the system embodiment above, the three-dimensional object production of the component can be implemented according to a computer design file. 
     In accordance with one or more embodiments or any of the system embodiments above, the compute device can feed forward and back the corrective actions to the three-dimensional object production in real time to generate the updated three-dimensional object production. 
     In accordance with one or more embodiments or any of the system embodiments above, the at least one sensing device can include an X-ray source and X-ray detector that together acquire a full or partial X-ray diffraction signal or pattern that is analyzed to determine the in situ physical properties. 
     In accordance with one or more embodiments or any of the system embodiments above, the in situ physical properties can include hardness, local strain, yield strength, density, crystallite size, porosity, defect density and compositional variation. 
     In accordance with one or more embodiments or any of the system embodiments above, a compute device can include a processor executing software to provide one or more process modeling, toolpath planning, defect detection, layer defect detection, part defect detection, feedback control, scan path planning, decision making, and process sensing operations for detecting the defects. 
     In accordance with one or more embodiments or any of the system embodiments above, a compute device can include a database storing and providing the pre-specified material requirements and a computer design file for detecting the defects and implementing the three-dimensional object production. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The following descriptions should not be considered limiting in any way. With reference to the accompanying drawings, like elements are numbered alike: 
         FIG. 1  depicts a system according to one or more embodiments; 
         FIG. 2  depicts a process flow according to one or more embodiments; and 
         FIG. 3  depicts a schematic flow according to one or more embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     A detailed description of one or more embodiments of the disclosed apparatus and method are presented herein by way of exemplification and not limitation with reference to the Figures. 
     Turning now to an overview of technologies that are more specifically relevant to aspects of the invention, as discussed above, conventional additive manufacturing is rapidly emerging means of flexible manufacturing. However, part-to-part variation, non-uniformity of properties across finished manufactured parts or products, and local or extended defects are significant concerns in utilizing conventional additive manufacturing for high volume production. Most conventional additive manufacturing processes have limited or no closed loop control. Therefore, post deposition analysis is employed to assess only final material properties of the finished manufactured part or product relative to pre-determined materials requirements. Further, post deposition analysis does not allow a manufacturer to change or adapt properties during manufacturing. 
     Turning now to an overview of the aspects of the invention, one or more embodiments of the invention address the above-described shortcomings of the conventional additive manufacturing by providing, via a system, a method, and/or an apparatus (referred to as a system, herein, for brevity), material deposition processes including in situ sensor analysis. The in situ sensor analysis of the material deposition processes extracts physical properties of a component in a formation state during its additive manufacturing. The material deposition processes, then, feed forward and back these physical properties to the additive manufacturing for continuous adaptability. The technical effects and benefits of embodiments of the material deposition processes herein include determining these physical properties during the formation state of the component and, thus, enabling corrective actions, such as altering additive manufacturing depositions, to achieve pre-specified material requirements. 
     Turning now to  FIG. 1 , a system  100  for implementing the teachings herein is shown in according to one or more embodiments. The system  100  implements material deposition processes including in situ sensor analysis. 
     In this embodiment, the system  100  includes a compute device  101 . The compute device  101  can be an electronic, computer framework comprising and/or employing any number and combination of computing device and networks utilizing various communication technologies, as described herein. The compute device  101  can be easily scalable, extensible, and modular, with the ability to change to different services or reconfigure some features independently of others. 
     The compute device  101  has a processor  102 , which can include one or more central processing units (CPUs). The processor  102 , also referred to as a processing circuit, microprocessor, computing unit, is coupled via a system bus  103  to a system memory  104  and various other components. The system memory  104  includes read only memory (ROM) and random access memory (RAM). The ROM is coupled to the system bus  103  and may include a basic input/output system (BIOS), which controls certain basic functions of the system  100 . The RAM is read-write memory coupled to the system bus  103  for use by the processor  102 . 
     The compute device  101  includes a hard disk  107 , which is an example of a tangible storage medium readable executable by the processor  102 . The hard disk  107  stores software  108  and database  109 . The software  108  is stored as instructions for execution on the system  100  by the processor  102  (to perform process, such as the process flows of  FIGS. 2-3 ). The database  109  includes a set of values of qualitative or quantitative variables organized in various data structures to support and be used by operations of the software  108 . Examples of operations provided by the software  108  include process modeling, toolpath planning, defect detection, layer defect detection, part defect detection, feedback control, scan path planning, decision making, and process sensing. Examples of items stored on the database  109  include computer design files, pre-specified material requirements, assessment models, assessment algorithms, and the like. 
     The compute device  101  includes one or more adapters (e.g., hard disk controllers, network adapters, graphics adapters, etc.) that interconnect and support communications between the processor  102 , the system memory  104 , the hard disk  107 , and other components of the translation system  100  (e.g., peripheral and external devices). In one or more embodiments of the present invention, the one or more adapters can be connected to one or more I/O buses that are connected to the system bus  103  via an intermediate bus bridge, and the one or more I/O buses can utilize common protocols, such as the Peripheral Component Interconnect (PCI). 
     The compute device  101  includes an interface adapter  110  interconnecting a keyboard, a mouse, a speaker, a microphone, etc. to the system bus  103 . The compute device  101  includes a display adapter  111  interconnecting the system bus  103  to a display. The display adapter  111  (and/or the processor  102 ) can include a graphics controller to provide graphics performance, such as a display and management of a graphic user interface. A communications adapter  113  interconnects the system bus  103  with a network  120  enabling the translation system  100  to communicate with other systems, devices, data, and software, such as an additive manufacturing machine  130 . 
     The system  100  includes the additive manufacturing machine  130 , which further comprises at least one sensor device  131 , along with a processor, a memory, tool/feeder, and other machining parts that are not shown for brevity. Note that while shown as separate mechanisms communicating across the network  120 , in accordance with one or more embodiment, the compute device  101  and the additive manufacturing machine  130  can be integrated into a single apparatus. 
     The additive manufacturing machine  130  is configured to manufacture a component  140  via the material deposition processes including in situ sensor analysis. In general, additive manufacturing is a three-dimensional object production process utilizing computer design file. In this regard, a variety of materials, ranging from polymer composites, metals, ceramics, food, foams, gels, alloys, and the like, are deposited by a tool or feeder according to the computer design file and heated by an electric beam to set the material in place. The location of the deposited materials as the tool or feeder moves according to the computer design file is referred to as a tool path. 
     The at least one sensor device  131  can be any device including transducer and/or a generator. In general, the transducer of the sensor device  131  can be any detector converts variations in a physical quantity into an electrical signal. Examples of physical quantities can include such as local strain, yield strength, density, crystallite size, porosity, defect density, crystalline orientation, texture, compositional variation, temperature, local porosity, optical density, reflectance (e.g., note that because some of these quantities are difficult to extract, the sensor device  131  provides added benefits for in situ analysis). The generator (also known as a source) of the sensor device  131  can be any mechanism that, in response to electrical signals, generates a wave, which itself is detectable or a reflection thereof is detectable by the transducer. The at least one sensor device  131  can also communicate via any interface, such as a controller area network (CAN), a local interconnect network (LIN), a direct I/O interface, an analog to digital (A/D) interface, a digital to analog (D/A) interface, or any other interface specific to the input, to the compute device  101  via the network  130 , along with a processor, a memory, and machining parts of the additive manufacturing machine  130 . Note that the at least one sensor device  131  is representative of one or more sensors of the same or varying type, each of which is capable of extracting physical properties of the component  140  in a formation state during its additive manufacturing. Example of the at least one sensor device  131  include, but are not limited to, an X-ray, ultra-violet, visible light, near-infrared, short-wave infrared, mid-wavelength infrared, long-wavelength infrared, and terahertz sensors, cameras, and detectors. In accordance with one or more embodiments, the at least one sensor device  131  includes an X-ray source and X-ray detector that together acquire a full or partial X-ray diffraction signal or pattern that is analyzed to determine the in situ physical properties. Further, the X-ray source and the X-ray detector can be directed to detect a small portion of the full X-ray diffraction pattern, such that a single peak with a particular intensity and width representing the detection. 
     Thus, as configured in  FIG. 1 , the operations of the software  108 , the database  109 , and the additive manufacturing machine  130  (e.g., the system  100 ) are necessarily rooted in the computational ability of the processors therein to overcome and address the herein-described shortcomings of the conventional additive manufacturing. In this regard, the software  108  and the data  109  improve manufacturing operations of the additive manufacturing machine  130  by reducing and eliminating errors in manufacturing, part-to-part variation, non-uniformity of properties, and local or extended defects for high volume production. 
       FIG. 2  depicts a process flow  200  of according to one or more embodiments. The process flow  200  is an example operation of implementing material deposition processes including in situ sensor analysis of the component  140  in a formation state during its additive manufacturing by the system  100 . 
     The process flow  200  being at block  210 , where the system  100  implements a material deposition process to form the component  140  according to a computer design file. In this regard, the additive manufacturing machine  130  can receive the computer design file from the database  109  of the compute device  101  and begin three-dimensional object production of the component  140 . 
     At block  220 , the system  100  senses in situ physical properties of the component  140  during the material deposition process. In accordance with one or more embodiments, the at least one sensor device  131  is an X-ray detector that acquires an X-ray diffraction (XRD) pattern while the component  140  is in a formation state (prior to completion). Various parameters of the XRD pattern are analyzed by the software  108  of the compute device  101  to determine the in situ physical properties or material parameters, such as hardness, local strain, yield strength, density, crystallite size, porosity, defect density and compositional variation (among other properties). The XRD pattern can be taken from any area of interest of the component  140 , as directed by the compute device  101 . 
     At block  230 , the system  100  detects compliance to specifications or defects of the in situ physical properties with respect to pre-specified material requirements. In this regard, the compute device  101  can compare the pre-specified material requirements of the database  109  to the in situ physical properties and determine if any defects are present. At block  240 , all defects are analyzed by the system  100  (e.g., by the software  108  of the compute device  101 ) to determine whether corrective actions need to be taken and what those corrective action should be. 
     At block  250 , the system  100  feeds forward and back the corrective actions to the material deposition process in real time for continuous adaptability, thereby updating the material deposition process (e.g., altering additive manufacturing depositions) to account for the defects and achieve pre-specified material requirements. At block  260 , the system  100  implements the material deposition process with the corrective actions to complete the manufacturing of the component  140 . 
     Turning now to  FIG. 3 , a schematic flow  300  is depicted according to one or more embodiments. The schematic flow  300  is an example operation of implementing in situ monitoring of stress for a component (including in situ and post situ process controls) by a system. The schematic flow  300  is executed by an additive manufacturing machine  301  comprising an X-ray source  302  and an X-ray detector  303  (e.g., an example of the sensor device  131  of  FIG. 1 ) and a computing device  304 . To the extent that these items overlap with the above system  100 , further description is not provided for the sake of brevity. 
     In general, the schematic flow  300  depicts a model  305  and a toolpath planning being received by the additive manufacturing machine  301  and utilized in a production operation  315  to produce a component. Due to any number of factors during the production operation  315 , the additive manufacturing machine  301  may produce a trending component  320 . The trending component  320  is note desired as a final component. 
     As shown in  FIG. 3 , the computing device  304  executes a sensing phase  330  through a process sensing  322 . The process sensing  322  includes receiving physical properties of the component while the component is in a formation state. The X-ray source  302  generates X-rays so that an XRD pattern can be taken from any area of interest by the X-ray detector  303 . The physical properties are communicated by the X-ray detector  303  of the additive manufacturing machine  301 , which is performing the in situ monitoring. The process sensing  322  further include comparing pre-specified material requirements to the in situ physical properties to provide comparison information. The sensing phase  330  and the process sensing  322  can be implemented by software of the computing device  304 . 
     Next, the computing device  304  executes a detecting phase  340 , which includes a process defect detection  342 , a layer defect detection  344 , and a part defect detection  346 . The detecting phase  340  identifies defects with respect to errors in the process (e.g., the process defect detection  342 ), defect within one or more layers (e.g., the layer defect detection  344 ), and defects across the component itself (e.g., the part defect detection  346 ). The detecting phase  340  and operations therein can be implemented by software of the computing device  304 . 
     The computing device  304  also executes a reacting phase  350 , which includes a feedback control  352 , a scan path planning  354 , and a decision making  356 . The reacting phase  350  and operations therein can be implemented by software of the computing device  304 . The results of the reacting  350  phase include corrective actions that are provided to the production operation  315 . The corrected actions can include adjusting an area of interest to determine where to perform the in situ monitoring (e.g., by the feedback control  352 ), adjusting a scan path to accommodate or correct defects in the trending component  320  (e.g., by the scan path planning  354 ), and determining material deposit amounts to accommodate or correct defects in the trending component  320  (e.g., by the decision making  356 ). The production operation  315  is improved by the corrective actions from the computing device, such that the additive manufacturing machine  301  may now produce a desired component  350 . 
     The term “about” is intended to include the degree of error associated with measurement of the particular quantity based upon the equipment available at the time of filing the application. 
     The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the present disclosure. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, element components, and/or groups thereof. 
     While the present disclosure has been described with reference to an exemplary embodiment or embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the scope of the present disclosure. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the present disclosure without departing from the essential scope thereof. Therefore, it is intended that the present disclosure not be limited to the particular embodiment disclosed as the best mode contemplated for carrying out this present disclosure, but that the present disclosure will include all embodiments falling within the scope of the claims.