CN110286046B - DED process hardness prediction method and device based on temperature field - Google Patents

DED process hardness prediction method and device based on temperature field Download PDF

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CN110286046B
CN110286046B CN201910340374.0A CN201910340374A CN110286046B CN 110286046 B CN110286046 B CN 110286046B CN 201910340374 A CN201910340374 A CN 201910340374A CN 110286046 B CN110286046 B CN 110286046B
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陈哲涵
郭鑫鑫
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University of Science and Technology Beijing USTB
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Abstract

The invention provides a temperature field-based DED process hardness prediction method and device, which can predict the hardness distribution condition of the whole part to be measured. The method comprises the following steps: determining key temperature characteristics of a temperature field aiming at the characteristic of heat cycle change in an exposure period in the DED process printing process; in the DED forming process, acquiring temperature field data of part sampling points of the part to be detected according to the determined key temperature characteristics of the temperature field; establishing a three-dimensional coordinate system according to the shape of the part to be measured; and predicting the hardness of the part from three dimensions according to the temperature change rule of the key temperature characteristics of the temperature field data in the three-dimensional directions of the part to be measured, so as to obtain the hardness distribution condition of the whole part to be measured. The present invention relates to the field of non-destructive monitoring.

Description

DED process hardness prediction method and device based on temperature field
Technical Field
The invention relates to the field of nondestructive monitoring, in particular to a DED process hardness prediction method and device based on a temperature field.
Background
Direct Energy Deposition (DED) is a interdisciplinary technique that utilizes laser technology, computer aided design and fabrication (CAD/CAM), robotics, sensors and controls, and powder metallurgy. The DED technique is a metal Additive Manufacturing (AM) method that deposits molten raw material (wire or powder) to a desired spatial location through a deposition head, thereby rapidly solidifying the material. Unlike powder bed selective laser melting metal (SLM) technology, DED technology does not rely on pressure chambers, which can protect the metal printing process from the surrounding environment. For the SLM printing process, the working area must first be filled with inert gas, which is a time consuming process. Whereas for laser metal powder deposition techniques in the DED classification of techniques, the printing process can start immediately because the inert gas flows directly from the laser head and surrounds the powder flow and the melt pool. DED has received great attention in recent years because it can be used for a variety of material processing activities, such as metal coating, high-value component repair, prototyping, and even small volume production. Parts made using DED technology, however, may be too inconsistent and unreliable to meet the hardness requirements for many industrial applications. In the DED forming process, hardness is an important index for verifying structural integrity and assembly quality of a printed part to determine whether the printed part has characteristics required by a specified application, so that the method has important significance for predicting the hardness of the part in the machining process in real time.
At present, Chinese patent literature (application number: 201811202776.6, application date: 2018.10.16, application publication number: CN 109409271A) discloses a ferromagnetic material hardness prediction algorithm based on a Back Propagation neural network (BP neural network) improved algorithm. The algorithm firstly collects the Barkhausen signals of the ferromagnetic materials, divides a signal set, and obtains a Barkhausen noise training set and a Barkhausen noise testing set; then, performing Autoregressive (AR) spectrum analysis on the collected signals, selecting 5-order expansions, namely 4, 8, 16, 32 and 64 orders, solving a second derivative of the expanded signals, taking positions of valley widths, valley depths and valley point positions of the second derivative signals as features, performing distance on the valleys by using a K-means algorithm, and encoding the signals, thereby completing the unification of feature dimensions; and then optimizing and training the BP neural network model. Simulation shows that the prediction result of the method is good, the mean square error is only 80, namely the error of each hardness prediction can be guaranteed to be 9 Vickers hardnesses, and the mean square error of a time domain algorithm is 229, namely more than 15 Vickers hardnesses, so that the algorithm effectiveness is proved. However, the algorithm does not perform nondestructive prediction on the distribution of the hardness of the part according to the characteristic of heat period change in the exposure period of the DED process printing process.
Disclosure of Invention
The invention aims to provide a method and a device for predicting the hardness of a DED process based on a temperature field, and aims to solve the problem that the distribution of the hardness of a part is not predicted in a lossless manner aiming at the characteristic of the heat period change in the exposure period of the DED process printing process in the prior art.
In order to solve the above technical problem, an embodiment of the present invention provides a temperature field-based method for predicting hardness of a DED process, including:
determining key temperature characteristics of a temperature field aiming at the characteristic of heat cycle change in an exposure period in the DED process printing process;
in the DED forming process, acquiring temperature field data of part sampling points of the part to be detected according to the determined key temperature characteristics of the temperature field;
establishing a three-dimensional coordinate system according to the shape of the part to be measured;
and predicting the hardness of the part from three dimensions according to the temperature change rule of the key temperature characteristics of the temperature field data in the three-dimensional directions of the part to be measured, so as to obtain the hardness distribution condition of the whole part to be measured.
Furthermore, the partial sampling points are points at fixed positions at equal intervals on each layer of the part to be measured and can represent the overall distribution condition of hardness.
Further, the temperature field key temperature characteristics include: an x-direction key temperature characteristic, a y-direction key temperature characteristic and a z-direction key temperature characteristic; wherein the content of the first and second substances,
the key temperature characteristics in the x direction are deposition temperature, cooling rate and average deposition temperature in the exposure period of the sampling point;
the key temperature characteristics in the y direction are the deposition temperature and the cooling temperature in the exposure period of the sampling point;
the critical temperature characteristics in the z direction are deposition temperature, cooling temperature, deposition temperature gradient and average deposition temperature during the exposure period of the sampling point.
Further, the exposure period is the time taken for a sample point to appear until covered during the DED process, but not when covered;
the deposition temperature is the maximum temperature within one exposure period;
the cooling temperature is the lowest temperature during one exposure cycle.
Further, the cooling rate in the X-axis direction is expressed as:
v＝(tX+100)/s
where v represents the cooling rate in the direction of the X axis, tX is the coordinate y, z is the lowest temperature within the same sample exposure period, and s is the time required for the sample to fall 100 degrees to tX.
Further, the average deposition temperature in the X-axis direction is represented as:
Figure BDA0002040513570000031
wherein TX is the average deposition temperature in the X-axis direction, TxyzThe maximum temperature in the exposure period of the sampling point with coordinates (x, y, z) is shown, and m, n and l respectively represent the number of the sampling points selected in the directions x, y and z.
Further, the average deposition temperature in the Z-axis direction is expressed as:
Figure BDA0002040513570000032
wherein TZ represents the average deposition temperature in the Z-axis direction, TxyzThe sample point with coordinates (x, y, z) is the highest temperature within the exposure period.
Further, the deposition temperature gradient in the Z-axis direction is expressed as:
Figure BDA0002040513570000033
wherein grad represents a deposition temperature gradient in the Z-axis direction, TZn+1And TZnAverage deposition temperature, t, of adjacent 2 layers, respectively, with the same layer print startinterThe interval of time during which the printing of the adjacent 2 layers starts when the printing of the layers starts at the same time.
Further, the predicting of the hardness of the part is performed from three dimensions according to the temperature change rule of the key temperature characteristics of the temperature field data in the three dimension directions of the part to be measured, and the hardness distribution condition of the whole part to be measured is obtained by the following steps:
a1, predicting the change trend of hardness along with the increase of the layer number according to the distribution rule of deposition temperature, cooling temperature, deposition temperature gradient and average deposition temperature in the z axis;
a2, predicting the trend of hardness changing along with the position in the length direction according to the distribution rule of deposition temperature, cooling rate, cooling temperature and average deposition temperature in the x axis;
a3, predicting the trend of hardness changing along with the position in the width direction according to the distribution rule of deposition temperature and cooling temperature in the y axis;
and A4, determining the hardness distribution of the whole part to be tested and the area with the maximum hardness according to the prediction results of A1, A2 and A3.
The embodiment of the invention also provides a temperature field-based DED process hardness prediction device, which comprises:
the determining module is used for determining key temperature characteristics of the temperature field according to the characteristic of heat period change in an exposure period in the DED process printing process;
the acquisition module is used for acquiring temperature field data of part sampling points of the part to be detected according to the determined key temperature characteristics of the temperature field in the DED forming process;
the establishing module is used for establishing a three-dimensional coordinate system according to the shape of the part to be detected;
and the prediction module is used for predicting the hardness of the part from three dimensions according to the temperature change rule of the key temperature characteristics of the temperature field data in the three-dimensional directions of the part to be measured, so as to obtain the hardness distribution condition of the whole part to be measured.
The technical scheme of the invention has the following beneficial effects:
in the scheme, the key temperature characteristics of the temperature field are determined according to the characteristic of the heat periodic variation in the exposure period in the DED process printing process; in the DED forming process, acquiring temperature field data of part sampling points of the part to be detected according to the determined key temperature characteristics of the temperature field; establishing a three-dimensional coordinate system according to the shape of the part to be measured; and predicting the hardness of the part from three dimensions according to the temperature change rule of the key temperature characteristics of the temperature field data in the three-dimensional directions of the part to be measured, so as to obtain the hardness distribution condition of the whole part to be measured. Therefore, the overall distribution of the hardness of the part can be efficiently and inexpensively predicted without damage through the temperature change rule.
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FIG. 1 is a schematic flow chart of a method for predicting hardness of a DED process based on a temperature field according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a three-dimensional coordinate system provided by an embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating a key temperature characteristic definition of a temperature field according to an embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating a method for predicting hardness of a DED process based on a temperature field according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a temperature field-based DED process hardness prediction apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantages of the present invention more apparent, the following detailed description is given with reference to the accompanying drawings and specific embodiments.
The invention provides a temperature field-based DED process hardness prediction method and device, aiming at the problem that the distribution of part hardness is predicted without damage aiming at the characteristic that the heat period changes in the exposure period of the DED process printing process in the prior art.
Example one
As shown in fig. 1, a method for predicting hardness of a DED process based on a temperature field according to an embodiment of the present invention includes:
s101, determining key temperature characteristics of a temperature field according to the characteristic of heat periodic variation in an exposure period in the DED process printing process;
s102, acquiring temperature field data of part sampling points of the part to be tested according to the determined key temperature characteristics of the temperature field in the DED forming process;
s103, establishing a three-dimensional coordinate system according to the shape of the part to be detected;
and S104, predicting the hardness of the part from three dimensions according to the temperature change rule of the key temperature characteristics of the temperature field data in the three-dimensional directions of the part to be measured, and obtaining the hardness distribution condition of the whole part to be measured.
According to the DED process hardness prediction method based on the temperature field, disclosed by the embodiment of the invention, the key temperature characteristics of the temperature field are determined according to the characteristic of heat cycle change in an exposure cycle of a DED process printing process; in the DED forming process, acquiring temperature field data of part sampling points of the part to be detected according to the determined key temperature characteristics of the temperature field; establishing a three-dimensional coordinate system according to the shape of the part to be measured; and predicting the hardness of the part from three dimensions according to the temperature change rule of the key temperature characteristics of the temperature field data in the three-dimensional directions of the part to be measured, so as to obtain the hardness distribution condition of the whole part to be measured. Therefore, the overall distribution of the hardness of the part can be efficiently and inexpensively predicted without damage through the temperature change rule.
In this embodiment, the partial sampling points are points at fixed positions at equal intervals on each layer of the part to be measured and can represent the overall distribution condition of hardness.
In this embodiment, as shown in fig. 2, three dimensions are x, y, and z directions of a suitable three-dimensional coordinate system established according to a specific shape of the component to be measured.
In the embodiment, the key temperature characteristics of the temperature field are determined according to the characteristic of the heat period change in the exposure period in the DED process printing process; the key temperature characteristics of the temperature field are selected from three dimensions of the x direction, the y direction and the z direction, wherein the key temperature characteristics of the temperature field comprise: an x-direction critical temperature characteristic, a y-direction critical temperature characteristic, and a z-direction critical temperature characteristic.
In the embodiment, the key temperature characteristics in the x direction are deposition temperature, cooling rate and average deposition temperature in the exposure period of the sampling point; the key temperature characteristics in the y direction are the deposition temperature and the cooling temperature in the exposure period of the sampling point; the critical temperature characteristics in the z direction are deposition temperature, cooling temperature, deposition temperature gradient and average deposition temperature during the exposure period of the sampling point.
In the present embodiment, as shown in fig. 3, the exposure period is the time taken for a sample point to appear until being covered in the DED process, but not including being covered; the deposition temperature is the maximum temperature within one exposure period; the cooling temperature is the lowest temperature during one exposure cycle.
In this embodiment, the hardness of the part is predicted from three dimensions of x, y, and z, wherein the following temperature characteristic calculation method needs to be explained:
1) the calculation formula of the average deposition temperature in the Z-axis direction is as follows:
Figure BDA0002040513570000061
wherein TZ represents the average deposition temperature in the Z-axis direction, TxyzThe maximum temperature in the exposure period of the sampling point with coordinates (x, y, z) is shown, and m, n and l respectively represent the number of the sampling points selected in the directions x, y and z.
2) The calculation formula of the deposition temperature gradient in the Z-axis direction is as follows:
Figure BDA0002040513570000071
wherein grad represents a deposition temperature gradient in the Z-axis direction, TZn+1And TZnAverage deposition temperature, t, of adjacent 2 layers, respectively, with the same layer print startinterThe interval of time during which the printing of the adjacent 2 layers starts when the printing of the layers starts at the same time.
3) The calculation formula of the average deposition temperature in the X-axis direction is expressed as:
Figure BDA0002040513570000072
wherein TX is the average deposition temperature in the X-axis direction, TxyzThe sample point with coordinates (x, y, z) is the highest temperature within the exposure period.
4) The calculation formula of the cooling rate in the X-axis direction is expressed as:
v＝(tX+100)/s
where v represents the cooling rate in the direction of the X axis, tX is the coordinate y, z is the lowest temperature within the same sample exposure period, and s is the time required for the sample to fall 100 degrees to tX.
In this embodiment, the formula v ═ tX +100)/s is only used for approximating the exposure period of the sampling point, tX is approximated, and s has a significant difference, where approximation means that the temperature difference is smaller than a preset first threshold value, and difference obviously means that the time difference is greater than a preset second threshold value.
In a specific embodiment of the temperature field-based method for predicting hardness of the DED process, further, the predicting hardness of the part from three dimensions according to a temperature variation rule of a key temperature characteristic of temperature field data in three dimensional directions of the part to be measured, and obtaining a hardness distribution of the whole part to be measured includes:
a1, predicting the change trend of hardness along with the increase of the layer number according to the distribution rule of deposition temperature, cooling temperature, deposition temperature gradient and average deposition temperature in the z axis;
a2, predicting the trend of hardness changing along with the position in the length direction according to the distribution rule of deposition temperature, cooling rate, cooling temperature and average deposition temperature in the x axis;
a3, predicting the trend of hardness changing along with the position in the width direction according to the distribution rule of deposition temperature and cooling temperature in the y axis;
and A4, determining the hardness distribution of the whole part to be tested and the area with the maximum hardness according to the prediction results of A1, A2 and A3.
As shown in fig. 4, the method for predicting the hardness of the DED process based on the temperature field according to the embodiment of the present invention determines the key temperature characteristic of the temperature field according to the characteristic of the thermal period change in the exposure period of the DED process printing process, monitors and obtains the temperature field data of a part of sampling points of the part to be tested in real time, performs gaussian smoothing denoising on the obtained temperature field data, predicts the hardness of the part from three dimensions (x direction, y direction, and z direction) by combining the change rule of the key temperature characteristic in the three dimensions of the part to be tested, obtains the relationship between the temperature characteristic and the hardness in the three dimensions, and further obtains the hardness distribution of the whole part according to the obtained relationship between the temperature characteristic and the hardness in the three dimensions, thereby realizing the nondestructive prediction of the hardness distribution of the DED molded part, and having low cost and simple and convenient operation; the predicted hardness distribution is an important index for verifying the structural integrity of the printed part and the quality of the component, and contributes to future process control, improvement and optimization.
Example two
The invention also provides a concrete implementation mode of the temperature field-based DED process hardness prediction device, and the temperature field-based DED process hardness prediction device provided by the invention corresponds to the concrete implementation mode of the temperature field-based DED process hardness prediction method, and the temperature field-based DED process hardness prediction device can realize the purpose of the invention by executing the flow steps in the concrete implementation mode of the method, so the explanation in the concrete implementation mode of the temperature field-based DED process hardness prediction method is also applicable to the concrete implementation mode of the temperature field-based DED process hardness prediction device provided by the invention, and the explanation in the following concrete implementation modes of the invention will not be repeated.
As shown in fig. 5, an embodiment of the present invention further provides a temperature field-based DED process hardness prediction apparatus, including:
the determining module 11 is configured to determine a key temperature characteristic of a temperature field according to a characteristic of a thermal cycle change in an exposure cycle of the DED process printing process;
the acquisition module 12 is configured to acquire temperature field data of a part of sampling points of the to-be-measured part according to the determined key temperature characteristics of the temperature field in the DED forming process;
the establishing module 13 is used for establishing a three-dimensional coordinate system according to the shape of the part to be detected;
and the prediction module 14 is used for predicting the hardness of the part from three dimensions according to the temperature change rule of the key temperature characteristics of the temperature field data in the three-dimensional directions of the part to be measured, so as to obtain the hardness distribution condition of the whole part to be measured.
The DED process hardness prediction device based on the temperature field determines the key temperature characteristics of the temperature field according to the characteristic of heat cycle change in the exposure period of the DED process printing process; in the DED forming process, acquiring temperature field data of part sampling points of the part to be detected according to the determined key temperature characteristics of the temperature field; establishing a three-dimensional coordinate system according to the shape of the part to be measured; and predicting the hardness of the part from three dimensions according to the temperature change rule of the key temperature characteristics of the temperature field data in the three-dimensional directions of the part to be measured, so as to obtain the hardness distribution condition of the whole part to be measured. Therefore, the overall distribution of the hardness of the part can be efficiently and inexpensively predicted without damage through the temperature change rule.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (8)

1. A DED process hardness prediction method based on a temperature field is characterized by comprising the following steps:
determining a key temperature characteristic of a temperature field aiming at the characteristic of heat period change in an exposure period in a DED process printing process, wherein DED represents direct energy deposition;
in the DED forming process, acquiring temperature field data of part sampling points of the part to be detected according to the determined key temperature characteristics of the temperature field;
establishing a three-dimensional coordinate system according to the shape of the part to be measured;
according to the temperature change rule of the key temperature characteristics of the temperature field data in the three-dimensional directions of the part to be measured, predicting the hardness of the part from the three dimensions to obtain the hardness distribution condition of the whole part to be measured;
wherein the temperature field key temperature characteristics include: an x-direction key temperature characteristic, a y-direction key temperature characteristic and a z-direction key temperature characteristic; wherein the content of the first and second substances,
the key temperature characteristics in the x direction are deposition temperature, cooling rate and average deposition temperature in the exposure period of the sampling point;
the key temperature characteristics in the y direction are the deposition temperature and the cooling temperature in the exposure period of the sampling point;
the critical temperature characteristics in the z direction are deposition temperature, cooling temperature, deposition temperature gradient and average deposition temperature during the exposure period of the sampling point.
2. The method for predicting the DED process hardness based on the temperature field according to claim 1, wherein the partial sampling points are points which are fixed at regular intervals on each layer of the part to be tested and can represent the overall hardness distribution.
3. The method of claim 1, wherein the exposure period is the time it takes for a sample point of the DED process to occur until covered, but not when covered;
the deposition temperature is the maximum temperature within one exposure period;
the cooling temperature is the lowest temperature during one exposure cycle.
4. The method of claim 1, wherein the cooling rate in the X-axis direction is expressed as:
v＝(tX+100)/s
where v represents the cooling rate in the direction of the X axis, tX is the coordinate y, z is the lowest temperature within the same sample exposure period, and s is the time required for the sample to fall 100 degrees to tX.
5. The method for temperature field based DED process hardness prediction according to claim 1, wherein the average deposition temperature in the X-axis direction is expressed as:
Figure FDA0002420799200000021
wherein TX is the average deposition temperature in the X-axis direction, TxyzThe maximum temperature in the exposure period of the sampling point with coordinates (x, y, z) is shown, and m, n and l respectively represent the number of the sampling points selected in the directions x, y and z.
6. The method of claim 1, wherein the average deposition temperature in the Z-axis direction is expressed as:
Figure FDA0002420799200000022
wherein TZ represents the average deposition temperature in the Z-axis direction, TxyzThe maximum temperature in the exposure period of the sampling point with coordinates (x, y, z) is shown, and m, n and l respectively represent the number of the sampling points selected in the directions x, y and z.
7. The method for temperature field based DED process hardness prediction according to claim 6, wherein the deposition temperature gradient in the Z-axis direction is expressed as:
Figure FDA0002420799200000023
wherein grad represents a deposition temperature gradient in the Z-axis direction, TZn+1And TZnAverage deposition temperature, t, of adjacent 2 layers, respectively, with the same layer print startinterThe interval of time during which the printing of the adjacent 2 layers starts when the printing of the layers starts at the same time.
8. The method for predicting the DED process hardness based on the temperature field according to claim 1, wherein the step of predicting the hardness of the part from three dimensions according to the temperature change rule of the key temperature characteristic of the temperature field data in the three-dimensional directions of the part to be measured to obtain the hardness distribution condition of the whole part to be measured comprises the following steps:
a1, predicting the change trend of hardness along with the increase of the layer number according to the distribution rule of deposition temperature, cooling temperature, deposition temperature gradient and average deposition temperature in the z axis;
a2, predicting the trend of hardness changing along with the position in the length direction according to the distribution rule of deposition temperature, cooling rate, cooling temperature and average deposition temperature in the x axis;
a3, predicting the trend of hardness changing along with the position in the width direction according to the distribution rule of deposition temperature and cooling temperature in the y axis;
and A4, determining the hardness distribution of the whole part to be tested and the area with the maximum hardness according to the prediction results of A1, A2 and A3.
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