Additive manufacturing system, control method, and program

Abstract

To facilitate adjustment of a combination of steps to be executed when deviation of result data from planned data occurs during molding a lamination molding product.SOLUTION: A lamination modeling system comprises: a plurality of step execution units including a step execution unit capable of executing a bead lamination step for molding a lamination molding product, and a step execution unit capable of executing a preceding or succeeding step of the lamination step; and a control unit configured communicably with the plurality of step execution units so as to determine a combination between step execution units to be executed among the plurality of step execution steps. The control unit is configured to: acquire result data obtained when at least one of the plurality of step execution units executes a step; and determine the combination on the basis of a degree of deviation of the result data from planned data.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an additive manufacturing system, a control method, and a program. [Background technology]

[0002] Patent Document 1 describes a structure manufacturing device that has a waiting room where a platform loaded with base material is brought in from outside, a stacking room where a layered object is formed on the base material using a metal-containing building material, a processing room where the layered object is processed, and an inspection room where the layered object and processed products made from the layered object are inspected, and the platform loaded with base material, etc. can move between each room. Patent Document 2 describes a system and method for supporting welding quality assurance throughout a manufacturing environment, in which a central controller collects actual welding parameter data from the cell controllers of each manufacturing cell via a communication network, forms aggregate welding parameter data for the same type of workpiece being welded in each of the manufacturing cells, analyzes the aggregate welding parameter data to generate updated welding settings, and the updated welding settings are communicated from the central controller to the cell controllers of each of the manufacturing cells via the communication network. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-25120 [Patent Document 2] Japanese Patent Application Publication No. 2019-102086 Summary of the Invention [Problem to be solved by the invention]

[0004] If the actual data deviates from the planned data during the manufacturing of an additive manufacturing object, it is difficult to adjust the combination of processes that should be executed that was initially decided.

[0005] An object of the present invention is to facilitate adjustment of the combination of processes to be executed when deviations between actual data and planned data occur during the manufacturing of a layered object. [Means for solving the problem]

[0006] With this objective in mind, the present invention provides an additive manufacturing system comprising a plurality of process execution units, including a process execution unit capable of executing a bead stacking process for manufacturing an additively manufactured object and a process execution unit capable of executing a process before or after the stacking process, and a control unit configured to be able to communicate with the plurality of process execution units and determining a combination of process execution units to be executed from among the plurality of process execution units, wherein the control unit acquires performance data obtained when at least one of the plurality of process execution units executes a process, and determines the combination based on the degree of deviation of the performance data from planned data. The control unit may be configured to control the combination of the plurality of process execution units to move relative to the layered object. The control unit may determine a combination that minimizes the total execution time of the processes while satisfying that the defect occurrence probability of the additively manufactured object is equal to or less than a predetermined setting value. In this case, the control unit may determine the predetermined setting value based on a defect occurrence probability model that models the defect occurrence probability of the additively manufactured object. The control unit may acquire, as the performance data, performance shape data indicating the shape of the layered object after at least one process execution unit has executed a process, and use, as the plan data, planned shape data indicating the planned shape of the layered object. In this case, the control unit may acquire, as the performance shape data, a performance value of the stack height of the layered object, use, as the planned shape data, a planned value of the stack height of the layered object, and use, as the degree of deviation, a difference between the performance value and the planned value. The additive manufacturing system may further include a movement system that moves each process execution unit included in a combination of multiple process execution units to the additively manufactured object on which each process execution unit executes a process. In this case, the movement system may include a rail device and a switching device that switches the rail device so that each process execution unit moves toward the additively manufactured object on which each process execution unit executes a process.

[0007] The present invention also provides a control method for an additive manufacturing system including a plurality of process execution units, including a process execution unit capable of executing a bead stacking process to form an additive manufacturing object and a process execution unit capable of executing a process before or after the stacking process, and a control unit configured to be able to communicate with the plurality of process execution units and determining a combination of process execution units to be executed from among the plurality of process execution units, the control unit including a step in which the control unit acquires performance data obtained when at least one of the plurality of process execution units executes a process, and a step in which the control unit determines the combination based on the degree of deviation of the performance data from planned data.

[0008] Furthermore, the present invention also provides a program for enabling an additive manufacturing system including a plurality of process execution units, including a process execution unit capable of executing a bead stacking process for manufacturing an additively manufactured object and a process execution unit capable of executing a process before or after the stacking process, and a control unit configured to be able to communicate with the plurality of process execution units and determining a combination of process execution units to be executed among the plurality of process execution units, to enable the control unit to acquire performance data obtained when at least one of the plurality of process execution units executes a process, and to determine the combination based on the degree of deviation of the performance data from the planned data. [Effects of the Invention]

[0009] According to the present invention, when deviation of actual data from planned data occurs during the manufacturing of a layered object, it becomes easy to adjust the combination of processes to be executed. [Brief explanation of the drawings]

[0010] [Figure 1] 1 is a diagram illustrating an example of the overall configuration of an additive manufacturing system according to an embodiment of the present invention. [Figure 2] FIG. 1 is a diagram illustrating a schematic configuration example of a modeling robot according to an embodiment of the present invention. [Figure 3] FIG. 2 is a diagram illustrating an example of a hardware configuration of a general controller according to the present embodiment. [Figure 4] 10(a) to 10(d) are flowcharts showing an example of the operation of each module. [Figure 5] FIG. 2 is a diagram illustrating an example of a functional configuration of a general controller according to the present embodiment. [Figure 6] 10 is a flowchart showing an example of the operation of the general controller in the present embodiment. [Figure 7] 10 is a flowchart showing the contents of a combination determination process according to the present embodiment. [Figure 8] FIG. 10 is a diagram showing an example of the overall configuration of another additive manufacturing system according to the present embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

[0012] [Additive manufacturing system configuration] FIG. 1 is a diagram showing an example of the overall configuration of an additive manufacturing system 1 according to this embodiment. As shown in the figure, the additive manufacturing system 1 is composed of multiple modules 10 divided according to function and an overall controller 30 that controls the multiple modules 10, which are connected via a communication line 81.

[0013] In the figure, modules 10a to 10d are shown as the multiple modules 10. For example, module 10a is a modeling module capable of executing a process of laminating weld beads (hereinafter simply referred to as "beads") to form the additively manufactured object 100. Modules 10b to 10d may be any modules capable of executing pre- or post-bead lamination processes. Here, module 10b is a cutting module capable of executing a cutting process of the additively manufactured object 100 to produce a smooth surface required for UT flaw detection (ultrasonic flaw detection) of the additively manufactured object 100. Module 10c is a UT flaw detection module capable of executing a UT flaw detection process of the additively manufactured object 100. Module 10d is a defect repair module capable of executing a defect repair (gouging) process to repair defects discovered by UT flaw detection of the additively manufactured object 100. Hereinafter, module 10a may be referred to as a molding module 10a, module 10b as a cutting module 10b, module 10c as a UT flaw detection module 10c, and module 10d as a defect repair module 10d. Module 10 is an example of a process execution unit.

[0014] Each module 10 may be robotized. That is, the modeling module 10a may be realized by a modeling robot, the cutting module 10b by a cutting robot, the UT flaw detection module 10c by a UT flaw detection robot, and the defect repair module 10d by a defect repair robot. Alternatively, each module 10 may be realized by replacing the tool at the tip of a manipulator having multiple joint axes with a tool corresponding to the function of each module 10. Furthermore, in the additive manufacturing system 1, the additive manufacturing object 100 is transported along a line. In the figure, the line is shown as a straight line, but the line does not necessarily have to be a straight line. Recognition (positioning) of the additive manufacturing object 100 between each module 10 may be performed, for example, by attaching a cross mark to a base plate on which the additive manufacturing object 100 is placed and having each module 10 recognize this.

[0015] Furthermore, although not shown in the figure, multiple additively-modeled objects 100 may be manufactured simultaneously on one line. For example, while one additively-modeled object 100 is being manufactured by the modeling module 10a, another additively-modeled object 100 may be cut by the cutting module 10b. When multiple additively-modeled objects 100 are manufactured simultaneously, the composition ratio of the modules 10 may be changed based on the proportion of operating time each module 10 takes in the entire process. For example, if cutting takes twice as long as modeling, two cutting modules 10b may be provided for one modeling module 10a.

[0016] Furthermore, each module 10 may be independent in terms of hardware and software, and modules 10 may be reduced, new modules 10 may be added, or the order of the modules 10 may be changed according to the user's wishes. For example, a user who does not require cutting may omit the cutting module 10b. A user who requires PT flaw detection (penetrant flaw detection) instead of UT flaw detection may add a PT flaw detection module. A user who requires cutting after rather than before UT flaw detection may reverse the order of the cutting module 10b and the UT flaw detection module 10c. Furthermore, each module 10 may have multiple functions instead of a single function. For example, a defect detection and repair module that integrates UT defect detection and defect repair may be provided.

[0017] The overall controller 30 makes full use of each module 10 to manufacture the layered object 100. Specifically, the overall controller 30 has a function to manage stacking plan data and a function to determine whether or not to use each module 10. The overall controller 30 is configured to be able to communicate with a plurality of process execution units, and is an example of a control unit that determines a combination of process execution units to be executed from among the plurality of process execution units.

[0018] [Configuration of modeling robot] As an example of a robot that realizes the module 10 in this embodiment, a configuration of a modeling robot 20 that realizes the modeling module 10a will be described. FIG. 2 is a diagram showing a schematic configuration example of the modeling robot 20. As shown in FIG.

[0019] The modeling robot 20 includes an arm 21 with multiple joints and performs modeling operations by moving according to instructions from a robot controller (not shown). The modeling robot 20 also has a welding torch 23 attached to the tip of the arm 21 via a wrist 22 for forming an additively manufactured object 100. The modeling robot 20 then moves the welding torch 23 while melting a mild steel filler material (wire) 24 to form the additively manufactured object 100. Specifically, the welding torch 23 supplies the filler material 24 while flowing a shielding gas to generate an arc to melt and solidify the filler material 24, and then stacks multiple layers of beads 101 on the base material 90 to form the additively manufactured object 100. Here, an arc is used as a heat source for melting the filler material 24, but a laser or plasma may also be used. The modeling robot 20 also includes a feeder for feeding the filler material 24, but a description of this will be omitted.

[0020] [Hardware configuration of the general controller] FIG. 3 is a diagram illustrating an example of the hardware configuration of the general controller 30. As shown in FIG. As shown in the figure, the overall controller 30 is realized by, for example, a general-purpose PC (Personal Computer) or the like, and includes a CPU 41 as a computing means, a main memory 42 as a storage means, and a magnetic disk device (HDD: Hard Disk Drive) 43. Here, the CPU 41 executes various programs such as an OS (Operating System) and application software, and realizes each function of the overall controller 30. The main memory 42 is a storage area that stores various programs and data used for executing the programs, and the HDD 43 is a storage area that stores input data for the various programs, output data from the various programs, and the like. The general controller 30 also includes a communication I / F 44 for communicating with the outside, a display mechanism 45 including a video memory, a display, etc., an input device 46 including a keyboard, a mouse, etc., and a driver 47 for reading and writing data from and to a recording medium. Note that Fig. 3 merely illustrates an example of the hardware configuration when the general controller 30 is realized by a computer system, and the general controller 30 is not limited to the configuration shown in the figure.

[0021] [Background and Overview of the Present Embodiment] When manufacturing an additive manufacturing object 100 using an additive manufacturing system 1 having such a configuration, if a defect occurs or is suspected to occur during the layering process, it is necessary to deviate from the predetermined process and flexibly rearrange each step to continue manufacturing. In particular, when manufacturing a large additive manufacturing object 100, manufacturing cannot be completed in one day and takes a long time. Therefore, no matter how precisely the manufacturing process is planned, it is difficult to rearrange the process when an abnormality or deviation from the plan occurs. Furthermore, incorporating inspection and repair processes to prevent abnormalities and deviations from the plan would significantly reduce productivity. Therefore, a method is needed to automatically optimize the combination of processes according to the molding situation while minimizing productivity as much as possible.

[0022] Therefore, in this embodiment, the overall controller 30 acquires performance data obtained when at least one of the multiple modules 10 executes a process, and determines the combination of modules 10 to be executed based on the degree of deviation of the performance data from the planned data.

[0023] [Operation of each module] 4(a) to 4(d) are flowcharts showing examples of the operation of each module 10. FIG.

[0024] FIG. 4(a) shows an example of the operation of the modeling module 10a. As shown in the figure, the modeling module 10a receives lamination plan data from the overall controller 30 (step 111) and reads it (step 112). Then, the modeling module 10a performs modeling (step 113) and records (appends) a modeling result log to the lamination plan data (step 114). Here, the modeling result log is, for example, shape data after modeling. Thereafter, the modeling module 10a transmits the lamination plan data in which the modeling result log is recorded to the overall controller 30 (step 115).

[0025] FIG. 4(b) shows an example of the operation of the cutting module 10b. As shown in the figure, the cutting module 10b receives lamination plan data from the overall controller 30 (step 121) and reads it (step 122). Then, the cutting module 10b performs cutting (step 123) and records (appends) a cutting result log to the lamination plan data (step 124). Here, the cutting result log is, for example, shape data after cutting. Thereafter, the cutting module 10b transmits the lamination plan data in which the cutting result log is recorded to the overall controller 30 (step 125).

[0026] FIG. 4(c) shows an example of the operation of the UT flaw detection module 10c. As shown in the figure, the UT flaw detection module 10c receives lamination plan data from the overall controller 30 (step 131) and reads it (step 132). Then, the UT flaw detection module 10c performs UT flaw detection (step 133) and records (appends) a flaw detection result log to the lamination plan data (step 134). Here, the flaw detection result log is, for example, data indicating the dimensions and positions of defects found by the UT flaw detection. Thereafter, the UT flaw detection module 10c transmits the lamination plan data in which the flaw detection result log is recorded to the overall controller 30 (step 135).

[0027] FIG. 4(d) shows an example of the operation of the defect repair module 10d. As shown in the figure, the defect repair module 10d receives lamination plan data from the overall controller 30 (step 141) and reads it (step 142). Then, the defect repair module 10d repairs the defect (step 143) and records (appends) a repair result log to the lamination plan data (step 144). Here, the repair result log is, for example, shape data after repair. Thereafter, the defect repair module 10d transmits the lamination plan data in which the repair result log is recorded to the overall controller 30 (step 145).

[0028] [General Controller Functional Configuration] 5 is a diagram showing an example of the functional configuration of the general controller 30 in this embodiment. As shown in the figure, the general controller 30 in this embodiment includes a receiving unit 31, a stacking plan data acquiring unit 32, a combination determining unit 33, a storage unit 34, a usability determining unit 35, and a transmitting unit 36.

[0029] The receiving unit 31 receives lamination plan data, in which each module 10 records log data after performing a task, from that module 10 via the communication line 81. If the module 10 is a modeling module 10a, the log data is a modeling result log. If the module 10 is a cutting module 10b, the log data is a cutting result log. If the module 10 is a UT flaw detection module 10c, the log data is a flaw detection result log. If the module 10 is a defect repair module 10d, the log data is a repair result log. The log data is an example of performance data obtained when at least one process execution unit among the multiple process execution units executes a process, and the receiving unit 31 is an example of a function in the control unit that acquires performance data. The modeling result log, cutting result log, and repair result log are examples of performance shape data that indicate the shape of the additively molded object after at least one process execution unit executes a process, and the receiving unit 31 is an example of a function in the control unit that acquires performance shape data. The modeling result log, the cutting result log, and the repair result log are also examples of the actual value of the stack height of the layered object, and the receiving unit 31 is also an example of a function in the control unit that acquires the actual value.

[0030] The stacking plan data acquisition unit 32 acquires stacking plan data. For example, at the start of modeling, the lamination plan data acquisition unit 32 acquires lamination plan data as follows. That is, the lamination plan data acquisition unit 32 acquires three-dimensional CAD data representing the three-dimensional shape of the layered object 100 from a CAD device (not shown). Then, the lamination plan data acquisition unit 32 divides (slices) this three-dimensional CAD data into multiple layers to generate multiple layer shape data, each representing the shape of each layer. Thereafter, the lamination plan data acquisition unit 32 generates lamination plan data including welding conditions and arc target positions when depositing beads 101 that match the height and width of each layer of the multiple layer shape data. After the start of modeling, the lamination plan data acquisition unit 32 acquires, from the receiving unit 31, the lamination plan data in which each module 10 has recorded log data.

[0031] The combination determination unit 33 determines a combination of modules 10 to be used from among the plurality of modules 10. For example, the combination determination unit 33 may calculate a vector t i The combination determination unit 33 also obtains a vector x representing the degree to which each module 10 performs work on the entire surface of the i-th layer. i Obtain the vector x i The value of each element of is between "0" and "1". The value "0" indicates that no work is performed on the i-th layer, and the value "1" indicates that work is performed on the entire surface of the i-th layer. Furthermore, the combination determination unit 33 calculates a vector w i The weight of the quality effect is the likelihood of defects occurring. i may be given in advance by the user. Note that vectors are shown in bold and italic in equations (1) to (3) described later, but in the text they are shown in normal bold, 3D characters. Here, the vector has a value for each module 10 as a corresponding element. For example, when the modeling is going smoothly, the vector x i is expressed as [Equation 1]. If a height deviation occurs during printing, the vector x i The value of the element is changed, and the work ratio of each module 10 changes. Note that [Equation 1] shows the case where cutting is not required for forming the i-layer, and there may be other examples, such as when cutting is planned from the beginning and the value of the element related to the cutting module is not 0 even if the modeling is going smoothly.

[0032]

number

[0033] The combination determination unit 33 also acquires a setting value b that represents the quality standard required. The setting value b may be, for example, the probability of detecting a defect with a diameter of D mm or more located S mm or less directly below the surface. The combination determination unit 33 may acquire the setting value b based on, for example, a defect occurrence probability model that models the probability of defect occurrence in an additively manufactured object. Because it may be difficult to detect defects across the entire range, managing defects using a defect occurrence probability model makes it easier to reduce losses due to quality failures. The defect occurrence probability model may be a distribution model in which parameters are adjusted based on examples of objects in which defects were observed in a pre-prepared probability distribution model. In addition to the defect occurrence probability model, other parameters such as height variation and the difference between the planned height and the actual height may also be used. The setting value b is an example of a predetermined setting value, and the combination determination unit 33 is an example of a function in the control unit that determines the predetermined setting value based on a defect occurrence probability model that models the probability of defect occurrence in an additively manufactured object.

[0034] Then, the combination determination unit 33 determines a vector x that minimizes the total required time as shown in equation (1) while satisfying the quality constraints shown in equation (2). i Ask for.

[0035]

number

[0036] The above problem can be considered as a general linear programming problem and can be solved using a known calculation method such as the simplex method. The combination determination unit 33 then determines whether or not to implement each module 10 in each layer. The combination determination unit 33 is an example of a function in the control unit that determines a combination that minimizes the total execution time of the processes while satisfying the requirement that the defect occurrence probability of the additively manufactured object be equal to or less than a predetermined value.

[0037] Here, since the constraint on quality is expressed by equation (2), the vector w i is used as the contribution to the increase in defects, but this is not limited to this. If the quality constraint is expressed by another formula, the vector w i may be, for example, the degree of contribution to defect reduction.

[0038] On the other hand, during modeling, each module 10 records log data in the stacking plan data after performing a task. Therefore, the combination determination unit 33 determines the penalty term Σu i Here, we consider the case where the log data is either a printing result log, a cutting result log, or a repair result log. In that case, the penalty term u i may be a quantity (scalar) that depends on the difference between the actual shape data in the log data and the planned shape data in the stacking plan data. i may be a quantity (scalar) that depends on the difference between the actual height in the log data and the planned height in the stacking plan data. The difference between the actual height and the planned height can be calculated relatively easily based on the data from each module 10. The combination determination unit 33 sets the constraint condition of equation (3) by adding this penalty term to equation (2). Then, in the same manner as above, the combination determination unit 33 determines the vector x that minimizes the overall required time as in equation (1) while satisfying the constraint on quality shown in equation (3). i Ask for.

[0039]

number

[0040] In this way, the combination determination unit 33 solves the optimization problem during modeling, thereby successively updating the combination of modules 10 that minimizes the production time while satisfying the quality conditions. The stacking plan data is an example of plan data, and the penalty term u i is an example of the degree of deviation of the actual data from the planned data. The planned shape data in the lamination plan data is an example of planned shape data that indicates the planned shape of the additively manufactured object. The planned height in the lamination plan data is an example of the planned value of the stack height of the additively manufactured object, and the penalty term u i is an example of the difference between the actual value and the planned value.

[0041] Here, the combination determination unit 33 determines the combination of the modules 10 to be used for all the modules 10, but this is not limited to this. i The value of the element corresponding to the modeling module 10a may be set to "1," and the combination of modules 10 to be used may be determined with the other modules 10 as targets.

[0042] The storage unit 34 stores combination information indicating the combination of the modules 10 determined by the combination determination unit 33.

[0043] The usability determining unit 35 refers to the combination information stored in the storage unit 34 and determines whether or not each module 10 is to be used.

[0044] The transmission unit 36 ​​transmits the stacking plan data to the module 10 that the usability determination unit 35 has determined to be used, via the communication line 81. The transmission unit 36 ​​also transmits, via the communication line 81, a movement instruction to instruct the module 10 that the usability determination unit 35 has determined to be used to move relative to the layered object 100. The transmission unit 36 ​​is an example of a function in the control unit that controls a combination of multiple process execution units to move relative to the layered object.

[0045] [Operation of the general controller] 6 is a flowchart showing an example of the operation of the overall controller 30 in this embodiment. In this example, it is assumed that the combination of modules 10 to be used when stacking the first layer is predetermined, and combination information indicating this combination is stored in advance in the storage unit 34. In addition, in this example, it is assumed that the modeling module 10a is set to be always used.

[0046] In the general controller 30, first, the lamination plan data acquisition unit 32 acquires lamination plan data (step 301). Next, the overall controller 30 operates the modeling module 10a (Step 302). Specifically, when the first layer is to be modeled, the transmitting unit 36 ​​transmits the lamination plan data acquired in Step 301 to the modeling module 10a. When the ith layer (i≧2) is to be modeled, the transmitting unit 36 ​​transmits the lamination plan data received during processing of the (i-1)th layer to the modeling module 10a. Then, the receiving unit 31 receives the lamination plan data to which the modeling result log has been added by the modeling module 10a.

[0047] Next, the usability determination unit 35 determines whether to use cutting module 10b (Step 303). Specifically, when printing the first layer, the usability determination unit 35 determines whether to use cutting module 10b by referring to the combination information stored in advance in the storage unit 34. When printing the ith layer (i≧2), the usability determination unit 35 determines whether to use cutting module 10b by referring to the combination information stored in the storage unit 34 in Step 310 when processing the (i-1)th layer. If it is determined in step 303 that the cutting module 10b is to be used, the overall controller 30 operates the cutting module 10b (step 304). Specifically, the transmitter 36 transmits the lamination plan data received from the modeling module 10a in step 302 to the cutting module 10b. Then, the receiver 31 receives the lamination plan data to which the cutting module 10b has added the cutting result log.

[0048] Next, the usability determination unit 35 determines whether to use the UT flaw detection module 10c (Step 305). Specifically, when the first layer is to be modeled, the usability determination unit 35 determines whether to use the UT flaw detection module 10c by referring to the combination information stored in advance in the storage unit 34. When the ith layer (i≧2) is to be modeled, the usability determination unit 35 determines whether to use the UT flaw detection module 10c by referring to the combination information stored in the storage unit 34 in Step 310 when processing the (i-1)th layer. If it is determined in step 305 that the UT flaw detection module 10c will be used, the overall controller 30 operates the UT flaw detection module 10c (step 306). Specifically, if the cutting module 10b is operating, the transmission unit 36 ​​transmits the lamination plan data received from the cutting module 10b in step 304 to the UT flaw detection module 10c. If the cutting module 10b is not operating, the transmission unit 36 ​​transmits the lamination plan data received from the modeling module 10a in step 302 to the UT flaw detection module 10c. Then, the reception unit 31 receives the lamination plan data to which the UT flaw detection module 10c has added the flaw detection result log.

[0049] Next, the usability determination unit 35 determines whether to use the defect repair module 10d (Step 307). Specifically, when printing the first layer, the usability determination unit 35 determines whether to use the defect repair module 10d by referring to the combination information stored in advance in the storage unit 34. When printing the i-th layer (i≧2), the usability determination unit 35 determines whether to use the defect repair module 10d by referring to the combination information stored in the storage unit 34 in Step 310 when processing the (i-1)-th layer. If it is determined in step 307 that the defect repair module 10d will be used, the overall controller 30 operates the defect repair module 10d (step 308). Specifically, if the UT flaw detection module 10c is operating, the transmission unit 36 ​​transmits the lamination plan data received from the UT flaw detection module 10c in step 306 to the defect repair module 10d. If the UT flaw detection module 10c is not operating but the cutting module 10b is operating, the transmission unit 36 ​​transmits the lamination plan data received from the cutting module 10b in step 304 to the defect repair module 10d. If neither the UT flaw detection module 10c nor the cutting module 10b is operating, the transmission unit 36 ​​transmits the lamination plan data received from the modeling module 10a in step 302 to the UT flaw detection module 10c. Then, the receiving unit 31 receives the lamination plan data to which the defect repair module 10d has added the repair result log.

[0050] In this way, the additive manufacturing system 1 produces the additively manufactured object 100 by transmitting and receiving unified stacking plan data between the modules 10 via the overall controller 30. For example, the defect repair module 10d reads length information indicating the dimensions and positions of defects recorded by the UT flaw detection module 10c, thereby determining the positions requiring repair.

[0051] Thereafter, the overall controller 30 determines whether the layer to be stacked has reached the layer at which modeling will end (step 309). That is, the overall controller 30 determines whether the number i of the layer to be stacked has reached the maximum value of the layer number. If it is determined in step 309 that the layer to be stacked has not reached the layer at which modeling ends, the combination determination unit 33 executes a combination determination process to determine a combination of modules 10 to be used (step 310). Specifically, the combination determination process is a process to determine a combination of modules 10 to be used from the modeling module 10a, cutting module 10b, UT flaw detection module 10c, and defect repair module 10d. Combination information indicating the combination of modules 10 determined in the combination determination process is stored in the memory unit 34. Then, the overall controller 30 returns the process to step 302. If it is determined in step 309 that the layer to be stacked has reached the layer at which modeling will end, the overall controller 30 ends the process.

[0052] FIG. 7 is a flowchart showing the details of the combination determination process in this embodiment.

[0053] First, the combination determination unit 33 obtains a vector ti representing the time required for work on the i-th layer (step 351). The combination determination unit 33 also obtains a vector x i (Step 352). Furthermore, the combination determination unit 33 obtains a vector w i is obtained (step 353). Next, the combination determination unit 33 acquires a set value b that represents a standard required for quality (step 354).

[0054] Next, the combination determination unit 33 acquires the log data added to the lamination plan data received by the receiving unit 31 (step 355). The log data includes at least a modeling result log. If the cutting module 10b is operating, the log data further includes a cutting result log. If the UT flaw detection module 10c is operating, the log data further includes a flaw detection result log. If the defect repair module 10d is operating, the log data further includes a repair result log. As a result, the combination determination unit 33 determines the penalty term u i Specifically, the combination determination unit 33 calculates the penalty term u based on a comparison between the stacking plan data and the log data. i In particular, if the log data is any one of the forming result log, the cutting result log, and the repair result log, the combination determination unit 33 calculates the penalty term u based on a comparison between the plan shape data in the lamination plan data and the actual shape data in the log data. i Calculate.

[0055] Then, the combination determination unit 33 determines a vector x that minimizes the total required time as shown in equation (1) while satisfying the quality constraints as shown in equation (3). i is calculated (step 357).

[0056]

number

[0057] [Variations] In the above description, the combination of modules 10 to be used is determined each time one layer of bead is formed, but this is not limited to this. For example, the combination of modules 10 to be used may be determined each time one pass of bead is formed. Alternatively, the combination of modules 10 to be used may be determined each time one module 10 is operated during the formation of one layer of bead or one pass of bead.

[0058] In the above description, each module 10 arranged at a fixed position performs work on the layered object 100 transported on the line, but this is not limited to this. Each movable module 10 may perform work on the layered object 100 arranged at a fixed position. Hereinafter, the layered object manufacturing system in this case will be described as layered object manufacturing system 2.

[0059] 8 is a diagram showing an example of the overall configuration of an additive manufacturing system 2 according to this embodiment. Note that in the diagram, constituent elements of the same type are distinguished by adding subscripts, but if there is no need to distinguish between them in the explanation, the subscripts will not be added.

[0060] As shown in the figure, the layered manufacturing system 2 in this embodiment is configured by connecting an overall controller 30 and a factory system 50 via a communication line 82. In the factory system 50, layered objects 1001-1009 are arranged on work tables 1501-1509, respectively. Each module 10 included in a combination determined by the overall controller 30 is moved to the side of the work table 150 on which the target layered object 100 is arranged. The factory system 50 is an example of a moving system that moves each process execution unit included in a combination of multiple process execution units to the layered object on which each process execution unit is to execute a process.

[0061] Specifically, the factory system 50 includes rails 511 to 518 and switches 521 to 528. The rails 51 define a track along which the module 10 moves. For example, a mount on which a unit is attached may be prepared as the module 10, and the mount may be movable on the rails 51. Here, if the module 10 is a modeling module 10a, the unit may be, for example, a device including a heat source and a wire feeder, and if the module 10 is a cutting module 10b, the unit may be, for example, a device that operates a cutting tool. The rails 51 are an example of a rail device. Furthermore, the overall controller 30 selects an optimal route to the module 10 and the layered object 100 based on the position of the workbench 150 on which the layered object 100 is placed and the content of the work to be performed on the layered object 100, and outputs a switching instruction for the rails 51 to the switch 52. The switch 53 then switches the direction of travel along the rails 51 in response to this switching instruction. For example, suppose that the modules 102 and 104 need to perform work on the layered object 1003 placed on the workbench 1503. In this case, the switch 522 switches the direction of travel so that the module 102 heads toward the rails 511, so that the module 102 performs the work from the upper side of the workbench 1503 in the drawing. Furthermore, the switch 524 switches the direction of travel so that the module 104 heads toward the rails 512, so that the module 104 performs the work from the lower side of the workbench 1503 in the drawing. The switch 52 is an example of a switching device that switches the rail device so that each process execution unit moves in the direction of the layered object on which the process execution unit is to execute a process.

[0062] By moving the modules 10 along the rails 51 as shown in FIG. 8, it is possible to increase the number of modules 10 that can be combined and the number of layered objects 100 that can be handled, even if the number of modules 10 is limited.

[0063] [Advantages of this embodiment] In this embodiment, the combination of modules 10 to be executed is determined according to the degree of deviation of the log data from the lamination plan data during the modeling of the layered object 100. This makes it easy to adjust the combination of modules 10 to be executed when deviation of the log data from the lamination plan data occurs during the modeling of the layered object 100. [Explanation of symbols]

[0064] 1, 2... Additive manufacturing system, 10... module, 20... modeling robot, 30... overall controller, 31... receiving unit, 32... stacking plan data acquisition unit, 33... combination determination unit, 34... storage unit, 35... usability determination unit, 36... transmission unit

Claims

1. a plurality of process execution units including a process execution unit capable of executing a bead stacking process for forming a layered object, and a process execution unit capable of executing a process before or after the bead stacking process; a control unit configured to be able to communicate with the plurality of process execution units and determining a combination of process execution units to be executed among the plurality of process execution units; Equipped with the control unit acquires performance data obtained when at least one of the plurality of process execution units executes a process, and determines the combination that minimizes the total execution time of the processes while satisfying that the probability of defect occurrence in the layered object is equal to or less than a predetermined set value, based on the degree of deviation of the performance data from the plan data. Additive manufacturing system.

2. The additive manufacturing system according to claim 1 , wherein the control unit determines the predetermined setting value based on a defect occurrence probability model that models a defect occurrence probability of the additively manufactured object.

3. a plurality of process execution units including a process execution unit capable of executing a bead stacking process for forming a layered object, and a process execution unit capable of executing a process before or after the bead stacking process; a control unit configured to be able to communicate with the plurality of process execution units and determining a combination of process execution units to be executed among the plurality of process execution units; In an additive manufacturing system comprising: a step in which the control unit acquires performance data obtained when at least one of the plurality of process execution units executes a process; determining the combination that minimizes the total execution time of the processes while satisfying that the defect occurrence probability of the additive manufacturing object is equal to or less than a predetermined set value, based on the degree of deviation of the performance data from the plan data; A control method comprising:

4. a plurality of process execution units including a process execution unit capable of executing a bead stacking process for forming a layered object, and a process execution unit capable of executing a process before or after the bead stacking process; a control unit configured to be able to communicate with the plurality of process execution units and determining a combination of process execution units to be executed among the plurality of process execution units; In an additive manufacturing system comprising: The control unit a function of acquiring performance data obtained when at least one of the plurality of process execution units executes a process; a function of determining the combination that minimizes the total execution time of the processes while satisfying that the defect occurrence probability of the additively manufactured object is equal to or less than a predetermined set value, based on the degree of deviation of the performance data from the plan data; A program to achieve this.

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