Patent Description:
In the related art, an additively-manufactured object is manufactured by depositing weld beads. When additive manufacturing is performed, it is necessary to perform control in consideration of various welding conditions in order to improve the manufacturing accuracy. Since there are many combinations of such welding conditions, the extraction of suitable welding conditions is very complicated and troublesome when performed manually.

In relation to the above situation, for example, Patent Literature <NUM> discloses a learning device for automatically determining an optimum welding condition in a welding device without teaching by a skilled operator. In this case, as information used for learning, bead appearance, a height and a width of a bead, a penetration amount, and the like are shown.

<CIT> belongs to the field of manufacturing of arc wire feed additives, and discloses a weld bead modeling method, device and system for arc additive manufacturing. The following document (describing the preamble of claims <NUM> and <NUM>) relates to a study on workpiece and welding torch height control in WAAM applications that can be applied for a wide range of materials:
<NPL>]
<CIT> describes that a machine learning device which learns to determine at least one arc welding condition includes a state observation unit which observes a state variable consisting of at least one physical quantity regarding the arc welding and the at least one arc welding condition at least during or after the arc welding, and a learning unit which learns a change in the at least one physical quantity observed by the state observation unit and the at least one arc welding condition in association with each other.

As described above, in the adjustment of the welding condition at the time of additive manufacturing, a very large number of condition combinations are conceivable to understand a change tendency of a bead shape (width, height, and the like), and it is difficult to specify an appropriate combination. For example, it is conceivable to create a database in which condition combinations are defined, but the creation of the database has a high load. Further, in creating the database, it is possible to ignore a machine difference between a power supply and a robot that perform additive manufacturing, and when the welding condition is adjusted based on such influence unique to the devices, the extraction of the welding condition becomes more complicated and troublesome. In Patent Literature <NUM> described above, such a machine difference between the power supply and the robot is not considered, and there is room for improvement in this respect as well.

In view of the above problems, an object of the present invention is to improve the accuracy of adjustment of a welding condition at the time of manufacturing an additively-manufactured object.

The above problems are solved by the present invention as defined in claims <NUM>, <NUM> and <NUM>.

Preferred embodiments of the system are defined in the dependent claims <NUM>-<NUM>.

According to the present invention, it is possible to improve the accuracy of adjustment of a welding condition at the time of manufacturing an additively-manufactured object.

Hereinafter, embodiments for carrying out the present invention will be described with reference to the drawings. The embodiments described below are embodiments for explaining the present invention and are not intended to be construed as limiting the present invention, and not all configurations described in the respective embodiments are essential configurations for solving the problems of the present invention. In the drawings, the same components are denoted by the same reference numerals to indicate the correspondence.

Hereinafter, a first embodiment of the present invention will be described.

<FIG> is a schematic configuration diagram of an additive manufacturing system to which the present invention can be applied.

An additive manufacturing system <NUM> according to the present embodiment includes an additive manufacturing device <NUM> and an information processing device <NUM> that integrally controls the additive manufacturing device <NUM>.

The additive manufacturing device <NUM> includes a welding robot <NUM>, a filler metal feeding unit <NUM> for feeding a filler metal (welding wire) M to a torch <NUM>, a robot controller <NUM> that controls the welding robot <NUM>, and a power supply <NUM>.

The welding robot <NUM> is an articulated robot, and the filler metal M is supported by the torch <NUM> provided on a distal shaft so as to be continuously fed. The torch <NUM> holds the filler metal M in a state in which the filler metal M protrudes from its tip. A position and a posture of the torch <NUM> can be freely set three-dimensionally within a range of degrees of freedom of a robot arm constituting the welding robot <NUM>.

The torch <NUM> includes a shield nozzle (not shown), and a shield gas is supplied from the shield nozzle. The shield gas blocks the atmosphere and prevents oxidation, nitridation, and the like of a molten metal during welding to prevent lack of fusion. An arc welding method used in the present embodiment may be either a consumable electrode type such as shielded metal arc welding or carbon dioxide gas shielded arc welding, or a non-consumable electrode type such as TIG welding or plasma arc welding, and is appropriately selected according to an additively-manufactured object W to be manufactured.

In a vicinity of the torch <NUM>, a shape sensor <NUM> capable of moving following the movement of the torch <NUM> is provided. The shape sensor <NUM> detects a shape of the additively-manufactured object W formed on a base <NUM>. In the present embodiment, the shape sensor <NUM> can detect a height, a position, a width, and the like of a weld bead <NUM> (also simply referred to as a "bead") forming the additively-manufactured object W. Information detected by the shape sensor <NUM> is transmitted to the information processing device <NUM>. A configuration of the shape sensor <NUM> is not particularly limited, and the shape sensor <NUM> may be configured to detect the shape by contact (contact sensor), or may be configured to detect the shape by a laser or the like (non-contact sensor). A means for deriving the shape of a formed bead is not limited to the shape sensor <NUM> disposed in the vicinity of the torch <NUM>. For example, the shape of the formed bead may be indirectly derived. As an example, a profile of a welding current or a feeding speed of the filler metal M and a DB (database) indicating a tendency of a height of a bead may be defined in advance, and a height of a formed bead may be derived based on a welding condition at the time of manufacturing.

In the welding robot <NUM>, when the arc welding method is a consumable electrode type, a contact tip is disposed inside the shield nozzle, and the filler metal M to which a molten current is supplied is held by the contact tip. The torch <NUM> generates an arc from the tip of the filler metal M in a shield gas atmosphere while holding the filler metal M. The filler metal M is fed from the filler metal feeding unit <NUM> to the torch <NUM> by a feeding mechanism (not shown) attached to the robot arm or the like. When the continuously fed filler metal M is melted and solidified while moving the torch <NUM>, the linear weld bead <NUM>, which is a melted and solidified body of the filler metal M, is formed on the base <NUM>. By depositing the weld beads <NUM>, the additively-manufactured object W is manufactured.

A heat source for melting the filler metal M is not limited to the arc described above. For example, a heat source using another method such as a heating method using an arc and a laser in combination, a heating method using plasma, or a heating method using an electron beam or a laser may be used. In the case of heating with an electron beam or a laser, a heating amount can be more finely controlled to keep the weld bead <NUM> in a more proper state, thereby contributing to further improvement of the quality of the additively-manufactured object W.

The robot controller <NUM> drives the welding robot <NUM> by a predetermined drive program based on an instruction from the information processing device <NUM>, and manufactures the additively-manufactured object W on the base <NUM>. That is, the welding robot <NUM> moves the torch <NUM> while melting the filler metal M with an arc according to a command from the robot controller <NUM>. The power supply <NUM> is a welding power supply that supplies power required for welding to the robot controller <NUM>. The power supply <NUM> can operate in a plurality of control modes, and can switch the power (current, voltage, or the like) when supplying power to the robot controller <NUM> according to the control mode. The filler metal feeding unit <NUM> controls the feeding and the feeding speed of the filler metal M to the torch <NUM> of the welding robot <NUM> based on an instruction from the information processing device <NUM>.

The information processing device <NUM> may be, for example, an information processing device such as a personal computer (PC). Functions shown in <FIG> may be implemented by a control unit (not shown) reading and executing a program of a function according to the present embodiment stored in a storage unit (not shown). The storage unit may include a random access memory (RAM) that is a volatile storage area, a read only memory (ROM) that is a non-volatile storage area, a hard disk drive (HDD), and the like. As the control unit, a central processing unit (CPU), a graphical processing unit (GPU), a general-purpose computing on graphics processing units (GPGPU), or the like may be used.

The information processing device <NUM> includes a manufacturing control unit <NUM>, a power supply control unit <NUM>, a feeding control unit <NUM>, a DB management unit <NUM>, a shape data acquisition unit <NUM>, a learning data management unit <NUM>, a learning processing unit <NUM>, and a welding condition derivation unit <NUM>. The manufacturing control unit <NUM> generates, based on design data (for example, CAD/CAM data) of the additively-manufactured object W to be manufactured, a control signal for the robot controller <NUM> at the time of manufacturing. Here, the control signal includes a movement trajectory of the torch <NUM> by the welding robot <NUM>, a welding condition at the time of forming the weld bead <NUM>, the feeding speed of the filler metal M by the filler metal feeding unit <NUM>, and the like. The movement trajectory of the torch <NUM> is not limited to a trajectory of the torch <NUM> during the formation of the weld bead <NUM> on the base <NUM>, and includes, for example, a movement trajectory of the torch <NUM> to a start position at which the weld bead <NUM> is formed.

The power supply control unit <NUM> controls the power supply (control mode) to the robot controller <NUM> by the power supply <NUM>. Values of a current and a voltage, a waveform (pulse) of the current, and the like when forming beads having the same shape may also differ depending on the control mode. In addition, the power supply control unit <NUM> acquires, from the power supply <NUM>, information on a current or a voltage provided to the robot controller <NUM> at an appropriate time.

The feeding control unit <NUM> controls the feeding speed and feeding timing of the filler metal M by the filler metal feeding unit <NUM>. Here, the feeding control of the filler metal M includes not only the feeding (forward feeding) but also the returning (backward feeding). The DB management unit <NUM> manages a DB (database) according to the present embodiment. Details of the DB according to the present embodiment will be described later. The shape data acquisition unit <NUM> acquires shape data of the weld bead <NUM> formed on the base <NUM> detected by the shape sensor <NUM>.

The learning data management unit <NUM> generates and manages learning data used in a learning process performed by the learning processing unit <NUM>. The learning processing unit <NUM> performs the learning process using the learning data managed by the learning data management unit <NUM>. Details of the learning data and the learning process according to the present embodiment will be described later. The learning processing unit <NUM> manages a learned model obtained as a result of the learning process. As described above, the power supply <NUM> according to the present embodiment can operate in a plurality of control modes. Accordingly, the learning processing unit <NUM> according to the present embodiment performs learning corresponding to the respective plurality of control modes of the power supply <NUM> and generates learned models.

The welding condition derivation unit <NUM> derives an adjustment amount for a welding condition of the manufacturing control unit <NUM> using a learned model generated by the learning processing unit <NUM>, and notifies the manufacturing control unit <NUM> of the adjustment amount. A method of deriving the adjustment amount according to the present embodiment will be described later.

In the present embodiment, as shown in <FIG>, a configuration in which the weld bead <NUM> is formed by moving the torch <NUM> on the cylindrical base <NUM> to manufacture the additively-manufactured object W will be described as an example. In <FIG>, the base <NUM> of the present embodiment has a configuration in which the additively-manufactured object W is manufactured on a plane of a cylinder, and the base <NUM> is not limited thereto. For example, the base <NUM> may have a cylindrical shape, and the weld bead <NUM> may be formed on an outer periphery of a side surface of the base <NUM>. In addition, a coordinate system in the design data according to the present embodiment is associated with a coordinate system on the base <NUM> on which the additively-manufactured object W is manufactured, and three axes (X axis, Y axis, and Z axis) of the coordinate system are set such that a three-dimensional position is defined with any position as an origin.

The additive manufacturing system <NUM> configured as described above melts the filler metal M and feeds the melted filler metal M onto the base <NUM> while moving the torch <NUM> according to the movement trajectory of the torch <NUM> defined based on the set design data by driving the welding robot <NUM>. As a result, the additively-manufactured object W in which a plurality of linear weld beads <NUM> are arranged and deposited on an upper surface of the base <NUM> is manufactured.

When the additively-manufactured object W is manufactured, it is necessary to adjust a control parameter at the time of manufacturing due to an operation state of the power supply <NUM>, characteristics specific to a device, a configuration of the additively-manufactured object W, and the like. More specifically, a bead shape may change according to various control parameters at the time of welding. Examples of the control parameters that affect the bead shape will be described below.

Examples of the control parameters that affect the bead shape include the feeding speed of the filler metal M, a travel speed, a welding amount, a target position, an amplitude and a frequency of weaving, and an amount of heat input. The feeding speed of the filler metal M will be described as an example. <FIG> is a diagram showing a relation between the feeding speed of the filler metal M and a current (or voltage) supplied from the power supply <NUM>, in which a horizontal axis represents the feeding speed of the filler metal M and a vertical axis represents a control value of the current (or voltage) supplied from the power supply <NUM>. As the feeding speed increases, the current (or voltage) supplied from the power supply <NUM> increases, but the increase is not constant. A tendency of this variation may vary depending on the control mode of the power supply <NUM>. Therefore, due to a difference in the tendency of the variation, a shape of a formed bead may vary even with the same control parameter.

As another example, the target position at the time of bead formation will be described. <FIG> is a diagram showing a relation between a target position (input) of a bead on the base <NUM> specified based on design data and a target position (output) of a bead obtained as a formation result, in which a horizontal axis represents the input and a vertical axis represents the output. In <FIG>, a broken line indicates an ideal relation between the input and the output, and an input value (that is, a design value) and an output value indicate the same value. However, in practice, the input value and the output value do not necessarily coincide with each other due to various factors such as device performance and specifications. For example, a solid line in <FIG> shows an example of a relation between an actual input value and an actual output value, and as shown in the solid line, a difference may occur between the design value and an output result. Therefore, even with same control parameters, the bead shape may vary due to a difference (deviation) in the target position.

<FIG> is a conceptual diagram for illustrating shape data of a weld bead. <FIG> shows a cross section of the weld bead <NUM> formed on the base <NUM> as seen from a travel direction of the torch <NUM> during formation. As shown in <FIG>, as the shape data of the weld bead <NUM>, information such as a height h, a width w, an angle α of a root part, and surface unevenness can be used.

In the present embodiment, a database indicating a relation between a welding condition and shape information of a bead formed under the welding condition is used. The database is managed by the DB management unit <NUM> and is defined in advance. As described above, the power supply <NUM> according to the present embodiment can operate in a plurality of control modes. Accordingly, a plurality of databases corresponding to the respective control modes are defined and managed.

In the database according to the present embodiment, a predetermined control parameter as a welding condition and information on a shape of a bead formed when welding is performed using the control parameter are stored in association with each other. Items of the welding condition include the welding amount of the filler metal M, the target position, the weaving conditions, the amount of heat input, the number of deposition passes, the temperature of a base material, an inter-pass time, and the like as described above. Items of the bead shape information include the height, the width, the angle of the root part, the surface unevenness of a bead as shown in <FIG>. The items of various types of information defined in the database are not limited to those described above, and may be increased or decreased as necessary.

In the present embodiment, a method of deep learning using a neural network among machine learning methods is used as a learning method, and supervised learning will be described as an example. A more specific method (algorithm) of deep learning is not particularly limited, and for example, a known method such as a convolutional neural network (CNN) may be used. In addition, a type and the number of layers constituting the neural network are also not particularly limited.

<FIG> is a schematic diagram for illustrating a concept of the learning process according to the present embodiment. First, in the present embodiment, as original data, a plurality of pairs of shape data indicating a shape of a bead and welding conditions used when the bead is formed are used. As the original data, data stored as a bead formation history may be used. The plurality of pairs of data are used to obtain respective differences between the shape data and the welding conditions. For example, a difference between shape data A of a bead shape A and shape data B of a bead shape B, and a difference between a welding condition A corresponding to the bead shape A and a welding condition B corresponding to the bead shape B are obtained. Then, in these differences, a plurality pieces of learning data in which a difference in the bead shape is used as input data and a difference in the welding condition is used as teacher data are prepared. In the present embodiment, a difference in the bead shape is described as an example of the input data included in the learning data, and the present invention is not limited thereto, and a pair of two shape data for deriving the difference may be used as the input data.

In the present embodiment, the learning process is performed using the learning data described above. When input data (here, a difference in the bead shape) prepared as the learning data is input to a learning model, a difference in the welding condition is output as output data with respect to the input data. The output data corresponds to the adjustment amount of the welding condition. Next, an error is derived by a loss function using the output data and the teacher data (here, a difference in the welding condition) prepared as the learning data. Then, parameters in the learning model are adjusted so as to reduce the error. For the adjustment of the parameters, for example, an error back propagation method or the like be used. In this way, the learned model is generated by repeatedly performing learning using a plurality of pieces of learning data. Since the learned model is updated each time the learning process is performed, parameters constituting the learned model are changed according to the timing of use, and an output result with respect to the input data is also different. When a pair of two pieces of shape data is used as the input data, basically, the same operation is performed.

The information processing device <NUM> does not necessarily need to perform the learning process. For example, the information processing device <NUM> may be configured to provide the learning data to a learning server (not shown) provided outside the information processing device <NUM> and perform the learning process on the server side. If necessary, the server may provide the learned model to the information processing device <NUM>. Such a learning server may be located on a network (not shown) such as the Internet, for example, and the learning server and the information processing device <NUM> are communicably connected to each other. That is, the information processing device <NUM> may operate as a machine learning device, or an external device may operate as a machine learning device. In any case, the information processing device <NUM> acquires the learned model obtained by the learning process and can be used when the additively-manufactured object W is manufactured.

<FIG> is a flowchart of an adjustment process of the welding condition according to the present embodiment. This process is performed and controlled by the information processing device <NUM>, and may be implemented, for example, by a processing unit such as a CPU or a GPU included in the information processing device <NUM> reading out a program for implementing the units shown in <FIG> from a storage unit (not shown) and executing the program. The learning process described above is performed and a learned model is generated before the process flow is started. A process for adjusting parameters according to the present process flow may be performed immediately before the actual manufacture of the additively-manufactured object W is started. Alternatively, the process may be performed when a control mode of a power supply is switched or when a layer of a bead to be formed is transferred to a next layer while the additively-manufactured object W is being manufactured. Here, a case will be described in which the adjustment is performed using a position different from a position at which the additively-manufactured object W is manufactured on the base <NUM> immediately before the additively-manufactured object W is manufactured.

In S501, the information processing device <NUM> acquires design data of the additively-manufactured object W. Here, the design data is data specifying a shape and the like of the additively-manufactured object W, and is created based on an instruction of a user. For example, the design data may be input from an external device (not shown) communicably connected to the information processing device <NUM>, or may be created on the information processing device <NUM> via a predetermined application (not shown).

In S502, the information processing device <NUM> causes the additive manufacturing device <NUM> to create, based on the design data acquired in S501, pass data corresponding to beads forming the additively-manufactured object W. Here, the pass data may include information such as a movement trajectory of the torch <NUM> in addition to shape data indicating a bead shape. The shape data created here corresponds to a design value, and may be stored and managed in a storage unit (not shown).

In S503, the information processing device <NUM> focuses on one control mode in which a parameter adjustment process is not performed among a plurality of control modes in which the power supply <NUM> can operate. Here, the control mode to be processed may be any control mode in which the power supply <NUM> can operate, or may be limited to one or more control modes used when the additively-manufactured object W is manufactured using the design data acquired in S501.

In S504, the information processing device <NUM> acquires a learned model corresponding to the control mode focused in S503. As described above, different learned models are generated according to the control mode, and a corresponding learned model is acquired from among the learned models.

In S505, the information processing device <NUM> specifies a welding condition corresponding to the shape data created in S502 by referring to a DB corresponding to the control mode focused in S503. As described above, the welding condition is associated with the bead shape data in the DB, and the welding condition can be specified by specifying the shape data.

In S506, the information processing device <NUM> causes the welding robot <NUM> to perform a manufacturing operation based on the welding condition specified in S505. Here, the manufacturing operation is not performed to manufacture a part of the additively-manufactured object W, but is performed to form a bead for parameter adjustment at a different position.

In S507, the information processing device <NUM> acquires shape data as a bead formation result performed in S506 via the shape sensor <NUM>. As described above, the shape sensor <NUM> according to the present embodiment is provided so as to move following the torch <NUM>. The shape data may be acquired along with bead formation, or may be acquired after bead formation is completed. As shown in <FIG>, examples of the shape data acquired here include a height, a width, an angle of a root part, and surface unevenness of a formed bead.

In S508, the information processing device <NUM> derives a difference between the shape data (measurement value) acquired in S507 and the shape data (design value) created in S502. For example, when the shape data includes a plurality of items such as a height and a width, respective differences are derived.

In S509, the information processing device <NUM> compares the difference derived in S508 with a predetermined threshold, and determines whether the difference is equal to or greater than the threshold. The threshold is set for each shape data item and is stored in a storage unit (not shown). The threshold used here may vary depending on the control mode, or a fixed value may be used. If the difference is equal to or greater than the threshold (YES in S509), the process of the information processing device <NUM> proceeds to S510. On the other hand, if the difference is smaller than the threshold (NO in S509), the process of the information processing device <NUM> proceeds to S513. In addition, when a plurality of items in the shape data are used for the determination, YES may be determined when all the items are equal to or greater than the threshold as a result of comparison between the items and the threshold. In this case, the threshold is set for each of the items.

In S510, the information processing device <NUM> inputs the difference derived in S508 to the learned model acquired in S503 as input data, thereby acquiring a difference in a welding condition as output data. The difference corresponds to an adjustment amount with respect to a welding condition used for the formation of an immediately preceding bead. As described above, when learning is performed using a pair of shape data as input data, a pair of the shape data (measurement value) acquired in S507 and the shape data (design value) created in S502 is input instead of the difference derived in S508.

In S511, the information processing device <NUM> corrects a welding condition used for forming an immediately preceding bead by reflecting the adjustment amount acquired in S510.

In S512, the information processing device <NUM> performs bead formation again under the welding condition corrected in S511. Thereafter, the process returns to S507, and subsequent processes are repeated. That is, the processes of S507 to S512 are repeated until a difference between the design value based on the design data and the measurement result based on an actual formation result is less than the threshold. Therefore, the adjustment amount acquired in S510 is repeatedly accumulated and reflected in the welding condition, so that the difference gradually decreases (converges).

In S513, the information processing device <NUM> stores a welding condition based on the current adjustment amount in a storage unit (not shown) in association with the control mode of the power supply <NUM> of interest. The welding condition (or the adjustment amount) stored here is used when the additively-manufactured object W is manufactured. Thereafter, the process proceeds to S514.

In S514, the information processing device <NUM> determines whether the parameter adjustment process is completed for all the control modes in which the power supply <NUM> can operate. If there is an unprocessed control mode (NO in S514), the process of the information processing device <NUM> returns to S503, and the subsequent processes are repeated. On the other hand, if the process for all the control modes is completed (YES in S514), the process flow ends.

In the flowchart described above, an example in which the parameter adjustment is performed based on the design data of the additively-manufactured object W has been described. At this time, the parameter adjustment corresponding to a layer or a positional relation may be performed based on the number of layers of beads indicated by the design data or a positional relation with an adjacent bead. More specifically, information on the number of layers and positions may be further used as the shape data. By including such information, it is possible to perform the parameter adjustment in consideration of sagging of a bead due to a formation position of the bead, fusion with an adjacent bead, and the like. Instead of using the design data of the additively-manufactured object W, the parameter adjustment is performed based on shape data for parameter adjustment defined in advance.

As described above, according to the present embodiment, it is possible to improve the accuracy of adjustment of a welding condition at the time of manufacturing an additively-manufactured object. In particular, by deriving a relation between a change tendency of a welding condition and a change tendency of a bead shape using a learned model, it is possible to adjust the welding condition independent of a system. In addition, it is not necessary to create a database in consideration of a machine difference of a system, and the present invention can be applied to various additive manufacturing systems using only a general-purpose database.

In addition to the configuration described in the first embodiment, the additive manufacturing system <NUM> may generate a learning data used in a learning process. For example, at the time of manufacturing the additively-manufactured object W, a bead shape is detected by the shape sensor <NUM> each time a bead is formed, and a welding condition, shape data, and a control mode of a power supply when the bead is formed are stored in association with each other. Further, the learning data may be generated by obtaining differences as described with reference to <FIG> using the stored data. At this time, data for generating the learning data may be specified by a user of the additive manufacturing system <NUM>, or may be extracted by filtering accumulated data under any condition.

In the present invention, a program or an application for implementing the functions of one or more embodiments described above may be supplied to a system or a device using a network, a storage medium, or the like, and one or more processors in a computer of the system or the device may read and execute the program.

The functions may be implemented by a circuit (for example, an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA)) that implements one or more functions.

As described above, the present specification discloses the following matters.

The shape data includes at least one of a height, a width, an angle of a root part, and surface unevenness of a weld bead.

According to this configuration, a welding condition can be adjusted in consideration of a height, a width, an angle, and a surface shape of a bead as shape data.

The welding condition includes at least one of a feeding speed of the filler metal, a travel speed, a current or a voltage of welding, a target position on a base on which the additively-manufactured object is manufactured, an amount of heat input at a time of manufacturing, and a weaving control condition.

According to this configuration, it is possible to adjust a feeding speed of a filler metal, a target position on a base, an amount of heat input at the time of manufacturing, and a weaving control condition as welding conditions.

The welding condition further includes the number of deposition passes or the temperature of a base material.

According to this configuration, it is possible to further adjust the number of deposition passes and the temperature of a base material as welding conditions. For example, it is possible to perform a learning process in consideration of load accumulation such as friction of an electrode or adhesion of sputtering to a nozzle due to an increase in the number of deposition passes. In addition, it is possible to perform a learning process in consideration of heat accumulation in a base.

An additive manufacturing system in accordance with the invention is defined in claim <NUM>.

According to the invention, it is possible to improve the accuracy of adjustment of a welding condition at the time of manufacturing an additively-manufactured object. By deriving a relation between a change tendency of a welding condition and a change tendency of a bead shape using a learned model, it is possible to adjust the welding condition independent of a system. In addition, it is not necessary to create a separate database in consideration of a machine difference of a system, and the present invention can be applied to various additive manufacturing systems using only a general-purpose database.

The derivation means derives a difference for adjusting the welding condition determined by the determination means when the difference between the first shape data and the second shape data is equal to or greater than a predetermined threshold.

According to this configuration, by repeatedly performing the adjustment of a welding condition, it is possible to perform control so as to obtain predetermined accuracy.

The determination means determines the welding condition for forming the first shape data using a database in which a shape of a weld bead is associated with a welding condition in advance.

According to this configuration, it is possible to determine a welding condition as a reference by using a general-purpose database, and perform adjustment based on the reference. Therefore, it is possible to reduce the labor of creating a database of welding conditions for each device.

The acquisition means acquires the shape data of the weld bead by measuring the weld bead using a sensor.

According to this configuration, it is possible to acquire an actual measurement value of a bead shape by a sensor and use the measurement value for comparison with a design value.

A method for adjusting a welding condition in accordance with the invention is defined in claim <NUM>.

A program in accordance with the invention is defined in claim <NUM>.

Claim 1:
An additive manufacturing system (<NUM>) for manufacturing an additively-manufactured object (W) by welding a filler metal (M) and depositing weld beads (<NUM>), the additive manufacturing system (<NUM>) comprising:
an additive manufacturing device (<NUM>),
the system being characterised by the following:
a creation means configured to create shape data of a weld bead as first shape data based on design data of the additively-manufactured object (W);
a determination means configured to determine a welding condition for forming the first shape data;
an acquisition means configured to acquire, as second shape data, shape data of a weld bead formed using the welding condition determined by the determination means;
a derivation means configured to derive a difference between a welding condition corresponding to the first shape data and a welding condition corresponding to the second shape data by inputting the first shape data and the second shape data or a difference between the first shape data and the second shape data to a learned model, the learned model being generated by performing a learning process using two pieces of shape data of a weld bead or a difference between the two pieces of shape data as input data and a difference between welding conditions corresponding to the difference between the two pieces of shape data as output data; and
an adjustment means configured to adjust the welding condition determined by the determination means using the difference derived by the derivation means.