CN114589315B - Optimal lapping step matching method for laser additive manufacturing - Google Patents
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B22—CASTING; POWDER METALLURGY
- B22F—WORKING METALLIC POWDER; MANUFACTURE OF ARTICLES FROM METALLIC POWDER; MAKING METALLIC POWDER; APPARATUS OR DEVICES SPECIALLY ADAPTED FOR METALLIC POWDER
- B22F10/00—Additive manufacturing of workpieces or articles from metallic powder
- B22F10/80—Data acquisition or data processing
- B22F10/85—Data acquisition or data processing for controlling or regulating additive manufacturing processes
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B33—ADDITIVE MANUFACTURING TECHNOLOGY
- B33Y—ADDITIVE MANUFACTURING, i.e. MANUFACTURING OF THREE-DIMENSIONAL [3-D] OBJECTS BY ADDITIVE DEPOSITION, ADDITIVE AGGLOMERATION OR ADDITIVE LAYERING, e.g. BY 3-D PRINTING, STEREOLITHOGRAPHY OR SELECTIVE LASER SINTERING
- B33Y50/00—Data acquisition or data processing for additive manufacturing
- B33Y50/02—Data acquisition or data processing for additive manufacturing for controlling or regulating additive manufacturing processes
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- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
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- G06F18/22—Matching criteria, e.g. proximity measures
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P10/00—Technologies related to metal processing
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Abstract
The invention provides an optimal lapping step matching method for laser additive manufacturing, which comprises the following steps of: s1: training to obtain a single-channel laser additive height prediction model, wherein the input of the model is a process parameter, and the output of the model is the height of a single cladding channel; s2: training to obtain a multi-channel lapping average height prediction model, wherein the input of the model is as follows: the process parameter + the initial lapping step amount, and the output of the model is the average height of the cladding layer after the multiple lapping; s3: and establishing an optimal lap joint stepping quantity matching model by using a method based on combination of forward prediction and backward iteration. The invention can quickly and directly match the optimal lapping step amount during horizontal lapping according to basic process parameters (laser power, scanning speed and powder feeding rate), does not need to measure the geometric characteristics of a single cladding channel in advance through experiments and then calculate the proper lapping step amount, or selects the optimal lapping step amount through a large number of experiments, thereby improving the efficiency and reducing the cost while ensuring the forming quality.
Description
Technical Field
The invention relates to the technical field of 3D laser printing, in particular to a matching method for optimal lapping stepping amount in laser additive manufacturing.
Background
The additive manufacturing using laser as a heat source is an important means for precision manufacturing due to the advantages of small laser spot radius, high energy density and the like. Good forming is the most basic requirement of laser additive precision machining, and low geometric dimension error in the forming process is the basis for obtaining a prospective design model. When multiple cladding tracks are lapped, a strong coupling relation exists between basic process parameters (laser power, scanning speed and powder feeding rate) of laser material increase and horizontal lapping stepping quantity (horizontal distance of a robot moving between adjacent cladding tracks when the robot is lapped horizontally). The basic process parameters determine the formation of the single laser additive, and the horizontal lapping step determines the surface quality after lapping. After the basic technological parameters are determined, if the lapping step is too large, the problems of poor lapping, uneven surface, smaller average formed height than the single-pass height and the like can occur, and further the cladding of the next layer is influenced; if the lapping step is too small, most of the metal of the lapping channel falls on the previous channel, the deposition height is gradually accumulated, the distance between the working plane and the nozzle is smaller and smaller, the optimal working distance is deviated, and the inclined forming is not beneficial to cladding of the next layer. Until there is a lack of a reliable optimal step matching model, the optimal step can only be determined by enumerating different lap steps under the process parameters and then by a large number of preliminary experiments. When different basic process parameters are met next time, corresponding experimental operations are repeated. The mode of determining the optimal lapping stepping amount based on a trial-and-error method has the advantages of low efficiency, high cost and low intelligent level.
Application No.: CN201710669368.0 patent of the invention "a laser additive manufacturing lap-joint rate on-line monitoring method", realizes the rapid and reliable monitoring of the laser additive manufacturing lap-joint rate through the calibration, acquisition, pretreatment, correction, calculation and the like of images. However, the method mainly focuses on monitoring the lapping rate in the process, and does not solve the problem of determining the proper lapping step amount according to basic process parameters.
The literature, "calculating strategy of the distance between welding beads in the thick-wall structure manufactured by the electric arc additive manufacturing", is an empirical calculation formula of the step of overlapping obtained through geometric derivation based on the equivalent area method, but the model is premised on the condition that the geometric characteristics of a single cladding channel need to be known, which means that the geometric characteristics of the single channel still need to be measured through pre-experiments before the overlapping is manufactured by the formal additive manufacturing, and then the step of overlapping can be calculated, which is not efficient.
Disclosure of Invention
In order to solve the above problems, the present invention aims to provide an optimal lapping step matching method for laser additive manufacturing, which directly matches the optimal lapping step according to basic process parameters, thereby avoiding determining an appropriate lapping step through a large amount of time-consuming preliminary experiments, and improving the efficiency of laser additive manufacturing.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
a laser additive manufacturing optimal lapping step matching method comprises the following steps:
s1: training to obtain a single-channel laser additive height prediction model, wherein the input of the model is a process parameter, and the output of the model is the height of a single cladding channel;
s2: training to obtain a multi-channel lapping average height prediction model, wherein the input of the model is as follows: the process parameter plus the initial lapping stepping quantity, and the output of the model is the average height of the cladding layer after multiple lapping;
s3: and establishing an optimal lap joint stepping quantity matching model by using a method based on combination of forward prediction and backward iteration.
Further, the process parameters comprise laser power p, scanning speed v and powder feeding speed f.
Further, the step S1 specifically includes the following steps:
s11: carrying out single-pass cladding experiments under the condition of a plurality of groups of different process parameter combinations, and then obtaining cladding pass heights corresponding to the process parameters through line structured light measurement;
s12: establishing a data set of the obtained data, and dividing the data set into a training set and a test set according to a ratio of 9: 1;
s13: the XGboost model is trained by using the training set, the accuracy of the model is evaluated by using the test set, and the optimal hyper-parameter combination of the model is obtained by adopting a gridding search mode, so that the single-channel laser additive height prediction model is obtained.
Further, the step S2 specifically includes the following steps:
s21: carrying out a plurality of overlapping laser material increase experiments under the combination of a plurality of groups of different basic process parameters and overlapping steps, and then scanning the cladding layer by using line structured light to obtain the cladding layer profile under each process parameter and overlapping step;
s22: sampling the cladding layer according to a certain interval, and then averaging to obtain the average height of the cladding layer under a certain process parameter plus the lapping step;
s23: establishing a data set of the obtained data, and dividing the data set into a training set and a test set according to a ratio of 9: 1;
s24: the XGboost model is trained by using the training set, the accuracy of the model is evaluated by using the test set, the optimal hyper-parameter combination of the model is obtained by adopting a gridding search mode, and finally the multi-channel lapping average height prediction model is obtained.
Further, the S3 specifically includes the following steps:
s31: inputting technological parameters and an initial lapping step d;
s32: calling the single-channel laser additive height prediction model established in S1, inputting process parameters, and outputting to obtain the predicted height H of the first channel f ；
S33: then, calling the single-layer multi-channel lapping average height prediction model established in the S2, and obtaining the lapping average prediction height H by taking the process parameters and the initial lapping step amount d as input m ；
S34: then calculating the deviation e between the average lapping height and the first deposition height;
s35: judging whether the iteration number N of the current iteration exceeds the maximum iteration number N or not max (ii) a If N > = N max And (5) exiting the loop, outputting the matched lapping step amount d, and otherwise, entering a new iteration.
Further, the step S35 specifically includes the following steps:
s351: the iteration number n = n +1;
s352: judging the current H m And H f Whether the absolute value of the difference is smaller than a threshold epsilon or not, if so, exiting the circulation and outputting the matched optimal lapping stepping quantity; otherwise, updating the lapping stepping quantity;
s353: if H is present m ＜H f If the step number of the lap joint is too large, the step number of the lap joint is required to be reduced during updating, namely d = d- Δ d, otherwise, the step number of the lap joint is required to be increased during updating, namely d = d + Δ d;
s354: inputting the basic process parameters and the updated lapping step quantity into a multi-channel lapping average height prediction model established in S2 to obtain a new average height H m And then the next iteration is started.
Has the advantages that: the invention can rapidly and directly match the optimal lapping step amount during horizontal lapping according to basic process parameters (laser power, scanning speed and powder feeding rate), does not need to measure the geometric characteristics of a single cladding channel in advance by experiments and then calculate the proper lapping step amount, or selects the optimal lapping step amount by a large number of experiments, thereby improving the efficiency and reducing the cost while ensuring the forming quality. The matching model of the invention comprises two forward reasoning models based on machine learning, and can be suitable for various different process parameter combinations as long as the coverage of training data is wide, so that the adaptability is good, and the intelligent degree is high. The iteration times, the iteration step length, the threshold value and the like of the method can be adjusted according to actual requirements, and the flexibility is good.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate an embodiment of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
fig. 1 is a flowchart of an optimal lap step matching method for laser additive manufacturing according to an embodiment of the present invention;
fig. 2 is a schematic horizontal lapping diagram of an optimal lapping step matching method in laser additive manufacturing according to an embodiment of the present invention;
fig. 3 is a forward and reverse iteration flow chart of the laser additive manufacturing optimal lap step matching method according to the embodiment of the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
The present invention will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Example 1
Referring to FIGS. 1-3: a laser additive manufacturing optimal lapping step matching method comprises the following steps:
s1: training to obtain a single-channel laser additive height prediction model, wherein the input of the model is a process parameter, and the output of the model is the height of a single cladding channel;
s2: training to obtain a multi-channel lapping average height prediction model, wherein the input of the model is as follows: the process parameter plus the initial lapping stepping quantity, and the output of the model is the average height of the cladding layer after multiple lapping;
s3: and establishing an optimal lap joint stepping quantity matching model by using a method based on combination of forward prediction and backward iteration.
According to the embodiment, the optimal lapping step amount during horizontal lapping can be rapidly and directly matched according to basic process parameters (laser power, scanning speed and powder feeding rate), the geometric characteristics of a single cladding channel are not required to be measured in advance through experiments, and then the appropriate lapping step amount is calculated, or the optimal lapping step amount is selected through a large number of experiments, so that the efficiency is improved and the cost is reduced while the forming quality is ensured.
Specifically, the process parameters include laser power p, scanning speed v, and powder feeding rate f.
Note that, referring to fig. 2: after the basic process parameters (laser power p, scanning speed v and powder feeding speed f) of the embodiment are determined, the forming size of the first pass can be determined. The ideal lap joint formation would be the average height H after the lap joint m Height H near first pass forming f . If the lapping step d is too large, the adjacent cladding channels will not contact or contact too little, and the lapping formation will have gullies, then the average height H m Will be less than the first deposition height H f (ii) a Otherwise, H m Will be greater than H f . Therefore, an optimal lap step matching model can be established using this fundamental relationship.
In a specific example, the S1 specifically includes the following steps:
s11: carrying out single-pass cladding experiments under the condition of a plurality of groups of different process parameter combinations, and then obtaining cladding pass heights corresponding to the process parameters through line structured light measurement;
s12: establishing a data set for the obtained data, and dividing the data set into a training set and a test set according to the ratio of 9: 1;
s13: the XGboost model is trained by using the training set, the accuracy of the model is evaluated by using the test set, and the optimal hyper-parameter combination of the model is obtained by adopting a gridding search mode, so that the single-channel laser additive height prediction model is obtained.
In a specific example, the S2 specifically includes the following steps:
s21: carrying out a plurality of overlapping laser material increase experiments under the combination of a plurality of groups of different basic process parameters and overlapping steps, and then scanning the cladding layer by using line structured light to obtain the cladding layer profile under each process parameter and overlapping step;
s22: sampling the cladding layer according to a certain interval, and then averaging to obtain the average height of the cladding layer under a certain process parameter plus the lapping step;
s23: establishing a data set of the obtained data, and dividing the data set into a training set and a test set according to a ratio of 9: 1;
s24: the XGboost model is trained by using the training set, the accuracy of the model is evaluated by using the test set, the optimal hyper-parameter combination of the model is obtained by adopting a gridding search mode, and finally the multi-channel lapping average height prediction model is obtained.
The matching model of the embodiment comprises two forward reasoning models (a single-channel laser additive height prediction model and a multi-channel lapping average height prediction model) based on machine learning, and can be suitable for different process parameter combinations as long as the coverage of training data is wide, so that the adaptability is good, and the intelligent degree is high.
In a specific example, the S3 specifically includes the following steps:
s31: inputting technological parameters and an initial lapping step d;
s32: calling the single-channel laser additive height prediction model established in the S1, inputting process parameters, and outputting to obtain the first-channel predicted height Hf;
s33: then, calling the single-layer multi-channel lapping average height prediction model established in the S2, and obtaining the lapping average prediction height H by taking the process parameters and the initial lapping step amount d as input m ；
S34: then calculating the deviation e between the average lapping height and the first deposition height;
s35: judging the current iterationWhether the generation number N exceeds the maximum iteration number N max (ii) a If N > = N max And (5) exiting the loop, outputting the matched lapping step amount d, and otherwise, entering a new iteration.
In this embodiment, the overlap step amount d is continuously updated by the above method of forward inference and reverse iteration, so as to gradually reduce H m And H f Until the absolute value of the deviation e is smaller than the set threshold value, and then the optimal horizontal lapping stepping amount d is output.
In a specific example, the S35 specifically includes the following steps:
s351: the number of iterations n = n +1;
s352: judging the current H m And H f Whether the absolute value of the difference is smaller than a threshold epsilon or not, if so, exiting the circulation and outputting the matched optimal lapping stepping quantity; otherwise, updating the lapping step;
s353: if H is m ＜H f If the step number of the lap joint is too large, the step number of the lap joint is required to be reduced during updating, namely d = d- Δ d, otherwise, the step number of the lap joint is required to be increased during updating, namely d = d + Δ d;
s354: inputting the basic process parameters and the updated lapping step amount into the multi-channel lapping average height prediction model established in the S2 to obtain a new average height Hm, and then starting the next iteration.
It should be noted that, in the specific implementation, the user may customize the threshold epsilon and the number of iterations N as needed max And the step size Δ d of each update of the lap step amount d. When the method is used, only basic process parameters (laser power, scanning speed and powder feeding rate) are required to be input into the established optimal lapping step matching model, and then the optimal lapping step is obtained through automatic calculation of the model.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (2)
1. The optimal lapping step matching method for laser additive manufacturing is characterized by comprising the following steps of:
s1: training to obtain a single-channel laser additive height prediction model, wherein the input of the model is a process parameter, and the output of the model is the height of a single cladding channel; the S1 specifically comprises the following steps:
s11: carrying out single-pass cladding experiments under the condition of a plurality of groups of different process parameter combinations, and then obtaining cladding pass heights corresponding to the process parameters through line structured light measurement;
s12: establishing a data set of the obtained data, and dividing the data set into a training set and a test set according to a ratio of 9: 1;
s13: training the XGboost model by using a training set, evaluating the accuracy of the model by using a test set, and obtaining the optimal hyper-parameter combination of the model by adopting a gridding search mode so as to obtain a single-channel laser additive height prediction model;
s2: training to obtain a multi-channel lapping average height prediction model, wherein the input of the model is as follows: the process parameter + the initial lapping step amount, and the output of the model is the average height of the cladding layer after the multiple lapping; the S2 specifically comprises the following steps:
s21: carrying out a plurality of lapping laser material increase experiments under the combination of a plurality of groups of different process parameters and lapping steps, and then scanning the cladding layer by using line structured light to obtain the contour of the cladding layer under each process parameter and lapping step;
s22: sampling the cladding layer according to a certain interval, and then averaging to obtain the average height of the cladding layer under a certain process parameter plus the lapping step;
s23: establishing a data set for the obtained data, and dividing the data set into a training set and a test set according to the ratio of 9: 1;
s24: training the XGboost model by using a training set, evaluating the accuracy of the model by using a test set, and obtaining the optimal hyper-parameter combination of the model by adopting a gridding search mode to finally obtain a multi-channel lapping average height prediction model;
s3: establishing an optimal lapping step matching model by using a method based on combination of forward prediction and reverse iteration; the S3 specifically comprises the following steps:
s31: inputting technological parameters and initial lapping step d 0 ；
S32: calling the single-channel laser additive height prediction model established in S1, inputting process parameters, and outputting to obtain the predicted height H of the first channel f ；
S33: then, calling the single-layer multi-channel lapping average height prediction model established in the S2, and enabling the process parameters and the initial lapping step amount d 0 Obtaining as input the average predicted height H of the lap joint m ；
S34: then calculating the deviation e between the average lapping prediction height and the first prediction height;
s35: judging whether the iteration number N of the current iteration exceeds the maximum iteration number N or not max (ii) a If N > = N max The loop is exited, the matched lapping stepping amount d is output, and otherwise, a new iteration is entered;
s36: the number of iterations n = n +1;
s37: judging the current H m And H f Whether the absolute value of the difference is smaller than a threshold value epsilon or not, if so, exiting the circulation and outputting the matched optimal lapping stepping quantity; otherwise, updating the lapping step;
s38: if H is present m ＜H f If the overlap step is too large, the overlap step should be reduced during updating, namely d = d- Δ d, otherwise, the overlap step should be increased during updating, namely d = d + Δ d;
s39: inputting the technological parameters and the updated lapping step quantity into a multi-channel lapping average height prediction model established in S2 to obtain a new average height H m And then the next iteration is started.
2. The laser additive manufacturing optimal lap step matching method according to claim 1, wherein said process parameters include laser power p, scanning speed v, powder feeding rate f.
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