CN113076570A - Additive repair and remanufacturing inversion design and reverse planning method - Google Patents
Additive repair and remanufacturing inversion design and reverse planning method Download PDFInfo
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- CN113076570A CN113076570A CN202110257078.1A CN202110257078A CN113076570A CN 113076570 A CN113076570 A CN 113076570A CN 202110257078 A CN202110257078 A CN 202110257078A CN 113076570 A CN113076570 A CN 113076570A
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Abstract
The invention discloses an additive material repair and remanufacture inversion design and reverse planning method, which obtains the associated data of 'base material → repair process → performance' through experiment and model calculation; developing an additive repair and remanufacture database under a Windows operating system by adopting a Qt programming architecture; based on a deep learning neural network architecture, establishing a reverse inversion mapping relation of 'work performance → repair process → repair material', taking the work performance and the damaged construction base material as input, and the repair material and process as output, so as to realize quick and accurate determination of the material increase repair and remanufacture repair material and process of the damaged part. Aiming at the actual processes of on-site additive repair and remanufacture, the invention realizes the inverse design and reverse planning of 'making work performance → repair process → repair material'; the problem that the traditional forward design of 'composition → process → performance' is separated from the reverse process of actual additive repair and remanufacture is solved, and the problem of the on-site rapid repair system of the equipment damaged part is solved.
Description
Technical Field
The invention belongs to the technical field of additive repair and remanufacture, and particularly relates to an inversion design and reverse planning method for additive repair and remanufacture.
Background
The field maintenance remanufacturing of large heavy-load critical parts in the fields of energy, machinery, aviation and the like and the field quick repair of parts in special environments such as open sea, tunnels and the like are key bottleneck problems which restrict the operation efficiency/benefit of important projects in China for a long time. Compared with the traditional forward design that the functional mode of the product is determined by the material performance, the on-site additive repair and remanufacturing reverse design deduces the structural organization and components of the material aiming at the service performance requirement of the repaired product, and then selects the reverse deduction process of a proper processing technology.
At present, the research on additive repair and remanufacturing mainly aims at the forward design of 'composition → process → performance', because the internal relation of the process is linear, and the internal response relation can be established through the derivation of a physical model. However, the inversion process of "work performance → repair process → material property" is nonlinear, the difficulty of inversion design and reverse planning is great, and related research is still lacked. Therefore, how to realize the inversion design and reverse planning of additive repair and remanufacture so as to meet the requirement of on-site emergency repair in a specific equipment environment is a great technical problem existing nowadays.
Disclosure of Invention
In view of the above, the invention provides an additive repair and remanufacturing inversion design and reverse planning method, which is based on an additive repair and remanufacturing database (including damaged part base materials, repair materials, work-doing performance, microstructures and repair process data) and is combined with a deep learning neural network architecture to realize the additive repair and remanufacturing inversion design and reverse planning.
Therefore, the invention provides the following technical scheme:
an additive repair and remanufacture inversion design and reverse planning method comprises the following steps:
acquiring and arranging source data to obtain associated data of 'base material → repair process → performance'; the source data at least comprises a base material, a repair process and performance data after repair;
establishing an additive repair and remanufacture database based on the associated data;
establishing a reverse inversion mapping relation model based on a deep learning neural network architecture based on the additive repair and remanufacturing database, wherein the reverse inversion mapping relation model reflects an inverse inversion mapping relation of 'enabling performance → repair process → repair material', and takes a base material and the enabling performance as input and takes the repair material and the process as output;
determining the base material and the service performance after repair of the damaged part;
inputting the base material and the repair work performance of the damaged part into the inverse inversion mapping relation model to obtain the material and process for additive repair and remanufacture repair of the damaged part.
Further, the source data is acquired and collated to obtain the associated data of 'base material → repair process → performance', which comprises the following steps:
considering the diversity of the damaged parts in the aspects of material types, expression forms and treatment processes, aiming at the difference of heterogeneous material interface matching and molten pool metallurgical behavior in the additive repair and remanufacturing processes, aiming at the repair processes of different heat sources, the additive repair and remanufacturing experiments are carried out, and the associated data of 'base material → repair process → performance' is obtained.
Further, the source data is acquired and collated to obtain the associated data of 'base material → repair process → performance', which comprises the following steps:
through different process heat source models, tissue evolution models and performance prediction models in the additive repair and remanufacturing processes, the associated data of 'base material → repair process → performance' is obtained.
Further, building an additive repair and remanufacturing database based on the associated data, comprising:
and developing an additive repair and remanufacture database under a Windows operating system by adopting a Qt programming architecture.
Further, the post-repair service performance is determined by the service performance of the undamaged part, which is not less than 90% of the undamaged part service performance.
Further, the repair material includes a plurality of intensive repair materials;
the repair process comprises electric arc, laser and plasma;
properties include hardness, yield strength, tensile strength, frictional wear.
The invention has the following beneficial effects:
the invention breaks through the forward design traditional thought of 'composition → process → performance' in the field of traditional additive repair and remanufacturing, establishes an additive repair and remanufacturing database and integrates a deep learning neural network architecture based on the actual process of additive repair and remanufacturing, realizes the inverse design and inverse planning of 'enabling performance → repair process → material attribute', and solves the problem of the on-site rapid repair system of equipment damaged parts.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of a method of additive repair and remanufacture inversion design and reverse planning in an embodiment of the invention;
FIG. 2 is a block diagram of a structure of an inverse inversion mapping relationship model according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Referring to fig. 1, a flow chart of an additive repair and remanufacture inversion design and reverse planning method according to an embodiment of the present invention is shown, the method including the following steps:
step 1: acquiring and arranging source data to obtain associated data of 'base material → repair process → performance'; the source data at least comprises base material, repair process and repaired performance data.
The data source of the invention is divided into experimental data and model calculation data, and the purpose is to obtain the associated data of 'base material → repair process → performance' through the experiment and the model calculation. The matrix material relates to common damaged part materials such as iron-based, aluminum-based and titanium-based materials, the repairing material comprises a plurality of intensive repairing materials, the repairing process comprises electric arc, laser, plasma and the like, and the properties comprise hardness, yield strength, tensile strength, frictional wear and the like.
The first data source is: experimental data
Considering the diversity of the damaged part in the aspects of material types, expression forms, treatment processes and the like in the early stage, aiming at the difference of heterogeneous material interface matching and molten pool metallurgical behavior in the repairing and remanufacturing process and aiming at the repairing processes of different heat sources such as laser, electric arc, plasma and the like, 7 kinds of intensified iron-based, aluminum-based and titanium-based alloy powder or wire materials are designed and prepared in the embodiment of the invention, 14 kinds of representative base materials are selected for material additive repairing tests, and basic data of the base materials, the repairing processes and the performance are obtained, as shown in table 1.
TABLE 1
And a second data source: model calculation data
Repairing materials, processes, microstructures and performance data are obtained through different process (laser, electric arc, plasma and the like) heat source models, microstructure models and performance prediction models in the additive repairing and remanufacturing processes.
1. A heat source model:
(1) arc heat source:
front hemisphere:
rear hemisphere:
where Q is η UI, η heat source efficiency. x, y and z represent coordinate positions, a, b and c are half-axis lengths of the ellipsoid shape of the heat source, f1、f2Heat distribution function of front and rear ellipsoids, f1+f2＝2。
(2) Laser heat source:
wherein f is a heat distribution function; a. c, z and mu are hollow shape parameters, and x, y and z represent coordinate positions.
2. A microstructure model:
(1) grain size:
in the formula, QinActivation energy for grain boundary diffusion; d is the grain size; l0Is the distance of the central point between the initial adjacent crystal grains; gamma ray0Is a material parameter; t is the absolute temperature; r is Boltzmann constant.
(2) Phase volume fraction:
wherein T is the absolute temperature; t issusIs the phase transition temperature; v is a material parameter.
(3) Dislocation density:
where ρ is the instantaneous dislocation density, ρiAt an initial dislocation density, pmaxIs the dislocation density at the state of dislocation saturation. Thus, the regularized dislocation density of the initial stage
To 0, the dislocation density is regularized in the saturation state
Is 1; A. n is a material parameter; c is a diffusion coefficient affecting dislocation motion; c0Is dislocation motion diffusion coefficient at absolute zero;
is the strain rate; r is Boltzmann constant; qdisIs the thermal activation energy of dislocations.
3. Performance prediction model:
in the formula, σwIs the total intensity contribution sum; sigmaiA strength contribution to the pure material; sigmadisIntensity contribution due to dislocation density variation; sigmarStrengthening the contribution to strength for the second phase; sigmassContribution to solid solution strengthening; AA. BB, Cdis、Css、ω、ω1、ω2、ω3、ω4、ω5、β1、β2、β3Gamma is a material parameter, and Hv is Vickers hardness.
Step 2: establishing an additive repair and remanufacture database based on the associated data;
and (3) developing an additive repair and remanufacture database under a Windows operating system by adopting a Qt programming architecture based on the experimental and model calculation data in the step 1, wherein the database comprises damaged part base materials, repair processes and performance associated data.
And step 3: establishing a reverse inversion mapping relation model based on a deep learning neural network architecture based on the additive material repair and remanufacturing database;
and (3) establishing a reverse inversion mapping relation model reflecting an 'enabling performance → repairing process → repairing material' reverse inversion mapping relation by taking the base material and the enabling performance as input and the repairing material and process as output on the basis of the additive repairing and remanufacturing database established in the step (2) and on the basis of a deep learning neural network architecture (figure 2).
And 4, step 4: determining the base material and the service performance after repair of the damaged part;
the service performance of the repaired part is ultimately determined by the service performance of the undamaged part, requiring that the repaired part has a performance not less than 90% of the undamaged part performance.
And 5: inversion design and reverse planning of the additive repair and remanufacturing process;
inputting the base material and the repaired work performance of the damaged part obtained in the step 4 into the inverse inversion mapping relation model to obtain the material and the process for additive repair and remanufacture repair of the damaged part, so that the material and the process for additive repair and remanufacture repair of the damaged part can be quickly and accurately determined.
The invention breaks through the forward design traditional thought of 'composition → process → performance' in the field of traditional additive repair and remanufacturing, establishes an additive repair and remanufacturing database and integrates a deep learning neural network architecture based on the actual process of additive repair and remanufacturing, realizes the inverse design and inverse planning of 'enabling performance → repair process → material attribute', and solves the problem of rapidly repairing the system on the site of equipment damaged parts.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (6)
1. An additive repair and remanufacture inversion design and reverse planning method, the method comprising the steps of:
acquiring and arranging source data to obtain associated data of 'base material → repair process → performance'; the source data at least comprises a base material, a repair process and performance data after repair;
establishing an additive repair and remanufacture database based on the associated data;
establishing a reverse inversion mapping relation model based on a deep learning neural network architecture based on the additive repair and remanufacturing database, wherein the reverse inversion mapping relation model reflects an inverse inversion mapping relation of 'enabling performance → repair process → repair material', and takes a base material and the enabling performance as input and takes the repair material and the process as output;
determining the base material and the service performance after repair of the damaged part;
inputting the base material and the repair work performance of the damaged part into the inverse inversion mapping relation model to obtain the material and process for additive repair and remanufacture repair of the damaged part.
2. The additive repair and remanufacture inverse design and inverse planning method according to claim 1, wherein the obtaining and collating source data to obtain associated data of "base material → repair process → performance" comprises:
considering the diversity of the damaged parts in the aspects of material types, expression forms and treatment processes, aiming at the difference of heterogeneous material interface matching and molten pool metallurgical behavior in the additive repair and remanufacturing processes, aiming at the repair processes of different heat sources, the additive repair and remanufacturing experiments are carried out, and the associated data of 'base material → repair process → performance' is obtained.
3. The additive repair and remanufacture inversion design and reverse planning method according to claim 1 or 2, wherein the obtaining and collating source data to obtain associated data of "base material → repair process → performance" comprises:
through different process heat source models, tissue evolution models and performance prediction models in the additive repair and remanufacturing processes, the associated data of 'base material → repair process → performance' is obtained.
4. The additive repair and remanufacture inverse design and inverse planning method of claim 1, wherein building an additive repair and remanufacture database based on the associated data comprises:
and developing an additive repair and remanufacture database under a Windows operating system by adopting a Qt programming architecture.
5. An additive repair and remanufacturing inversion design and reverse planning method according to claim 1 wherein the post repair work-function performance is governed by a work-function performance of an undamaged part not less than 90% of the work-function performance of the undamaged part.
6. The additive repair and remanufacture inverse design and inverse planning method of claim 1, wherein the repair material comprises a plurality of intensive repair materials;
the repair process comprises electric arc, laser and plasma;
properties include hardness, yield strength, tensile strength, frictional wear.
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| CN114872325A (en) * | 2022-04-27 | 2022-08-09 | 河北水利电力学院 | Additive remanufacturing method, additive remanufacturing device, electronic apparatus, storage medium, and program product |
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| CN1807685A (en) * | 2005-12-09 | 2006-07-26 | 浙江工业大学 | Nano coating process for metal surface |
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| CN114872325A (en) * | 2022-04-27 | 2022-08-09 | 河北水利电力学院 | Additive remanufacturing method, additive remanufacturing device, electronic apparatus, storage medium, and program product |
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