Shafting alignment key process identification and optimization method based on error flow model

Abstract

The invention discloses a shaft system alignment key process identification and optimization method based on an error flow model, and the method comprises the steps: constructing an error source set in a shaft system assembly process, and building a related space coordinate system based on an assembly topological relation; establishing a nominal pose transfer equation based on an infinitesimal rigid body coordinate transformation principle; constructing a joint surface contact error vector by combining the diameter of the flange on the basis of a flange opening value and a dislocation value measured on site; performing first-order linearization processing on the nominal pose transfer equation based on the joint surface contact error vector, and obtaining a sensitivity matrix; constructing a state equation for describing error accumulation according to the sensitivity matrix and the error source set; a sensitivity coefficient is constructed based on a state equation and is compared with a judgment threshold, key control characteristics are identified, accurate errors can be predicted by quantifying a transmission mechanism of each error source, and key procedures are accurately identified in combination with the sensitivity coefficient, so that a reasonable strategy is adopted for optimization, and the product quality is ensured to be qualified.

Description

Technical Field

[0001] This invention relates to the field of mechanical assembly technology, and in particular to a method for identifying and optimizing key processes in shaft alignment based on an error flow model. Background Technology

[0002] During the horizontal alignment (straight alignment) of the shaft system, due to the large span and numerous support points of the shaft system, the machining or installation error of a single support point is often transmitted to the entire system through the rigid coupling of the shaft body, making it difficult to guarantee the final coaxiality. The current horizontal alignment mainly relies on optical instruments (such as laser theodolites) to measure geometric deviations and repeatedly trial and error through "measurement-adjustment-remeasurement" based on experience. There is a lack of systematic quantitative analysis of the source and transmission path of error, resulting in low alignment efficiency and unstable accuracy.

[0003] While existing research on shaft system installation and alignment processes includes simulation models based on design data and transfer learning methods based on measured data, these methods primarily focus on predicting and correcting the final state. They lack in-depth analysis of the entire mechanism of error generation, transmission, accumulation, and amplification during assembly. Specifically, existing technologies have the following shortcomings in practical applications: 1. Lack of quantitative description of the error propagation mechanism: Existing methods often treat shaft system errors as a simple linear superposition of deviations from individual components, neglecting the unique "geometric lever amplification effect" of long shaft systems. That is, they fail to quantify how small deviations in the mating surface angles at the front end (such as flange opening) are transmitted through the long shaft section and significantly amplified into huge radial runout at the end. This leads to theoretical predictions often being smaller than actual deviations, making it difficult to guide high-precision assembly.

[0004] 2. Disconnect between theoretical models and field process data: Existing error modeling is often based on abstract mathematical vectors, failing to establish a direct mapping relationship between them and the actual measurable process parameters (such as flange gap and offset) by field workers. This makes it difficult for theoretical models to directly utilize field measurement data for real-time calculations, resulting in a situation where "calculation is one thing, execution is another."

[0005] 3. Lack of scientific basis for identifying and allocating precision in critical processes: Currently, when formulating process plans, a uniform tolerance control standard is often adopted for all processes, or adjustments are made based solely on experience through trial and error. Due to the lack of an evaluation system based on sensitivity analysis, construction personnel cannot identify which processes are the "critical control characteristics (KCC)" that affect the final precision. This leads to wasted costs on high-precision machining in non-critical links, while critical links (such as highly sensitive mating surfaces) are poorly controlled, resulting in high rework rates and low alignment efficiency. Summary of the Invention

[0006] In view of this, the present invention proposes a method for identifying and optimizing key processes in shaft alignment based on an error flow model. By quantifying the transmission mechanism of each error source, and using sensitivity analysis to identify key processes and perform targeted optimization.

[0007] The technical solution of this invention is implemented as follows: The method for identifying and optimizing key processes in shaft alignment based on the error flow model includes the following steps: Step S1: Construct a set of error sources in the shaft assembly process, and establish relevant spatial coordinate systems based on the assembly topology. The spatial coordinate systems include the global reference coordinate system O, the support feature coordinate system B, the shaft segment component coordinate system S, and the flange interface coordinate system F. Step S2: Based on the principle of infinitesimal rigid body coordinate transformation, and according to the topological order of each spatial coordinate system, establish the nominal pose transfer equation from the input end mating surface to the output end flange. Step S3: Based on the flange opening value and misalignment value measured on site, construct the contact error vector of the mating surface in combination with the flange diameter; Step S4: Perform first-order linearization on the nominal pose transfer equation based on the contact error vector of the mating surface, and obtain the sensitivity matrices of the mating surface error, support error, shaft segment deformation and flange error. Step S5: Based on the sensitivity matrix and the set of error sources, construct the state equation describing the accumulation of error; Step S6: Construct the sensitivity coefficient of the i-th type of error source based on the state equation and compare it with the judgment threshold; Step S7: If the sensitivity coefficient is greater than the judgment threshold, it is judged as a high-sensitivity process with critical control characteristics, and a source blocking strategy is adopted; otherwise, it is judged as a non-critical process, and a reverse compensation strategy is adopted.

[0008] Preferably, the set of error sources includes part manufacturing error, support positioning error, static elastic deformation error, mating surface contact error, and noise error.

[0009] Preferably, step S2 specifically involves: based on the topological order of each spatial coordinate, the nominal pose transfer equation is jointly constructed by the global reference coordinate system O, the support feature coordinate system B, the shaft segment component coordinate system S, and the flange interface coordinate system F: ; in This represents the total pose transformation matrix of the coordinate system F at the end flange interface of the kth shaft segment relative to the global reference coordinate system 0. This represents the minute rigid body transformation at the connection point between the (k-1)th and kth axis segments, determined by the contact error of the mating surface. The transformation matrix represents the support feature coordinate system B relative to the global reference coordinate system 0, and its deviation is determined by the support positioning error. The transformation matrix represents the coordinate system S of the shaft segment assembly relative to the coordinate system B of the support feature, and its deviation includes the influence of static elastic deformation error. The transformation matrix represents the transformation of the flange interface coordinate system F relative to the shaft segment assembly coordinate system S, and its deviation is determined by the manufacturing error of the part.

[0010] Preferably, the specific steps of step S3 are as follows: The flange diameter is D, and the vertical / horizontal opening values ​​measured on-site are respectively... The vertical / horizontal misalignment values ​​are respectively The contact error vector of the combined surface is: .

[0011] Preferably, the specific steps of step S4 are as follows: Based on the assembly process of the shaft system from stern to bow, the contact error vector of the mating surface is determined. Considering the initial input attitude disturbance of the k-th axis segment, the total error screw at the end of the k-th axis segment is derived based on the principle of differential motion. for: ; in The expression for the sensitivity matrix of the combined surface contact error is as follows: ; To support the error sensitivity matrix, its expression is: ; The deformation sensitivity matrix of the shaft segment is expressed as follows: ; The flange error sensitivity matrix is ​​expressed as follows: ; It is the identity matrix. These are the support error vector, shaft segment deformation error vector, and flange error vector, respectively. is the adjoint transformation matrix.

[0012] Preferably, the adjoint transformation matrix is ​​defined as follows: Define the transformation matrix. Its corresponding The adjoint transformation matrix is ​​defined as: ,in displacement vector An antisymmetric matrix.

[0013] Preferably, the expression for the state equation in step S5 is: ; in Let be the cumulative pose error at the end of the k-th axis segment. The cumulative pose error at the end of the (k-1)th shaft is... Let be the state transition matrix, and let its value be the th state transition matrix. The nominal homogeneous transformation matrix of the root axis segment from the input end face to the output end face The adjoint transformation matrix of the inverse matrix is ​​expressed as: , The input coupling matrix is ​​expressed as follows: , Let the input error vector be expressed as: , This represents system noise error.

[0014] Preferably, the specific steps of step S6 are as follows: Definition of the first Sensitivity coefficients of error sources To quantify the contribution of variations from a single error source to the total error:

[0015] In the formula For the input coupling matrix sub-block of the i-th type of error source, Let be the nominal value of the i-th type of error source. For the Euclidean norm, This is the theoretical prediction error vector at the end of the k-th shaft segment calculated based on the state equation.

[0016] Preferably, the specific steps of adopting the reverse compensation strategy in step S7 are as follows: If the sensitivity coefficient is less than the judgment threshold, it is determined to be a non-critical process, and a reverse compensation strategy is adopted. The reverse compensation strategy is as follows: under the premise that the source error is under control, the optimal adjustment amount of the bearing housing is calculated by utilizing the inverse property of the state transition matrix.

[0017] ; in It is the pseudo-inverse of the sensitivity matrix. This represents the target value that is expected to be achieved.

[0018] Compared with the prior art, the beneficial effects of the present invention are: The present invention provides a method for identifying and optimizing key processes in shaft alignment based on an error flow model. It introduces error flow theory and clarifies the modeling scope of the error flow. Based on the assembly topology of each object during shaft assembly, a nominal pose transfer equation is constructed to characterize the spatial error transfer motion direction. Then, based on data measured on-site, a combined surface contact error vector is constructed. Theories of differential motion and Lie groups and Lie algebras are introduced to linearize the nominal pose transfer equation to the first order, obtaining the sensitivity matrix corresponding to each error source. This allows quantification of the amplification factor of each error source on the end effector through geometric leverage. Based on the sensitivity matrix, a multi-level discrete state-space equation for shaft error flow can be constructed. The theoretically predicted error vector generated by the state equation can be used to calculate the sensitivity coefficient, which is then compared with a preset judgment threshold to analyze and identify key processes. Targeted strategy optimization can then be performed to cut off or eliminate errors, ensuring the final product quality is qualified. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only preferred embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart of the method for identifying and optimizing key processes in shaft alignment based on the error flow model of the present invention. Detailed Implementation

[0021] To better understand the technical content of this invention, a specific embodiment is provided below, and the invention will be further described in conjunction with the accompanying drawings.

[0022] See Figure 1 The present invention provides a method for identifying and optimizing key processes in shaft alignment based on an error flow model, comprising the following steps: Step S1: Construct a set of error sources in the shaft assembly process. The set of error sources includes part manufacturing error, support positioning error, static elastic deformation error, mating surface contact error, and noise error. At the same time, establish relevant spatial coordinate systems based on the assembly topology. The spatial coordinate systems include the global reference coordinate system O, the support feature coordinate system B, the shaft segment component coordinate system S, and the flange interface coordinate system F. The multi-stage shaft assembly process divides the assembly of a single node into two stages: positioning and docking, and fastening. Among them, the manufacturing errors of parts (such as eccentricity) and the support positioning errors (such as bearing housing misalignment) cause the shaft rotation center to deviate from the theoretical reference, which are defined as geometric coaxiality errors; static elastic deformation errors are caused by the deflection of the shaft section's self-weight and the interference of the mating surfaces; the contact error of the mating surfaces is the nonlinear transmission error caused by the interaction of uneven contact stiffness and micro-morphology of the mating surfaces; and noise errors are defined as random disturbance terms.

[0023] Step S2: Based on the principle of infinitesimal rigid body coordinate transformation, and according to the topological order of each spatial coordinate system, establish the nominal pose transfer equation from the input end mating surface to the output end flange. Specifically, based on the assembly topological order of each object in spatial coordinates, the nominal pose transfer equation is jointly constructed by the global reference coordinate system 0, the support feature coordinate system B, the shaft segment component coordinate system S, and the flange interface coordinate system F. ; in This represents the total pose transformation matrix of the coordinate system F at the end flange interface of the kth shaft segment relative to the global reference coordinate system 0. This represents the minute rigid body transformation at the connection point between the (k-1)th and kth axis segments, determined by the contact error of the mating surface. The transformation matrix represents the support feature coordinate system B (i.e., the bearing housing) relative to the global reference coordinate system 0, and its deviation is determined by the support positioning error. The transformation matrix represents the coordinate system S of the shaft segment assembly relative to the coordinate system B of the support feature, and its deviation includes the influence of static elastic deformation error. The transformation matrix represents the coordinate system F of the flange interface relative to the coordinate system S of the shaft segment assembly. Its deviation is determined by the manufacturing errors of the parts (such as shaft segment bending and eccentricity).

[0024] For a single k-th axis, the error propagation chain is constructed as follows: input end mating surface → support feature → shaft segment assembly → output end flange.

[0025] According to the principle of differential motion, the minute position and attitude errors of any rigid body can be represented by a... It is represented by a spatial vector.

[0026] set up For the error vector:

[0027] The first three items are positional deviations, and the last three items are angular deviations.

[0028] For each transformation matrix in the nominal pose transfer equation Its actual value It can be represented as a nominal value. With the small error matrix The product of:

[0029] in It is a vector Constructed The differential transformation matrix is ​​used in subsequent calculations of the theoretical model, and the values ​​are nominal. .

[0030] To achieve the integration of theoretical models and field processes, the discrete data measured at the interface need to be mapped into error vectors. The specific components are determined in step S3. Therefore, based on the flange opening value and misalignment value measured on-site, and combined with the flange diameter, a contact error vector of the mating surface is constructed. The specific steps are as follows: Given a flange diameter of D, the on-site measured vertical / horizontal opening values ​​are respectively... The vertical / horizontal misalignment values ​​are respectively The contact error vector of the combined surface is: .

[0031] Since the coordinate systems defined for each error source (bearing, shaft segment, flange, mating surface) are different, they cannot be directly added together. Therefore, it is necessary to use the adjoint transformation matrix. Project all local errors onto the same coordinate system (usually the end flange coordinate system is chosen). or global coordinate system Accumulate.

[0032] Transformation matrix corresponding The adjoint transformation matrix is ​​defined as:

[0033] in It is a displacement vector The antisymmetric matrix (used to calculate the cross product).

[0034] Based on the nominal pose transfer equation, it can be derived that to calculate the end flange interface... Total error at (Relative to the final coordinate system), according to the chain rule and the principle of differential transformation, the total error is equal to the sum of the errors of each stage projected onto the final stage after inverse transformation of subsequent stages. Let the first stage be... Minor errors exist in each step. Then the total differential change at the end of the system This can be deduced as:

[0035] Multiply both sides of the above equation Error spinor transformed to the end coordinate system Utilizing the properties of adjoint transformation The general analytical expression for error propagation is obtained as follows:

[0036] Based on this mathematical theorem, the cumulative effect of each local error source propagating to the end can be expressed as: total error = vector sum of each level of error source in the global coordinate system.

[0037] Step S4: Perform first-order linearization on the nominal pose transfer equation based on the contact error vector of the mating surface, and obtain the sensitivity matrices for the mating surface error, support error, shaft segment deformation, and flange error. The specific steps are as follows: Based on the assembly process of the shaft system from stern to bow, the contact error vector of the mating surface is determined. Considering the initial input attitude disturbance of the k-th axis segment, the total error screw at the end of the k-th axis segment is derived based on the principle of differential motion. for: ; In the formula, each Jacobian matrix represents a specific element. This characterizes the spatial propagation characteristics from the location of the corresponding error source to the end of the shaft segment, and is defined according to the physical topology as follows: To incorporate the surface contact error sensitivity matrix, we consider the initial coupling state at the beginning of the transmission chain. Since this error occurs at the start of the shaft segment and must traverse the supporting structure and the entire geometric length of the shaft segment to reach the end, it is most significantly affected by the geometric lever effect. Its expression is: ; This error contains geometric information along the entire axis and has the largest norm, indicating that this factor has the strongest destructive effect on the system's accuracy.

[0038] To support the error sensitivity matrix, corresponding to the support feature coordinate system It needs to pass through the shaft segment assembly and the end flange, and its expression is: ; This is the deformation sensitivity matrix of the shaft segment, corresponding to the coordinate system of the shaft segment assembly. It needs to pass through the end flange interface, and its expression is: ; This is the flange error sensitivity matrix, corresponding to the flange interface coordinate system at the end of the transmission chain. Since this error is located directly at the output and has no geometric amplification path, its expression is: ; As an identity matrix, the final manufacturing error is linearly added to the total system error in a 1:1 ratio. These are the support error vector, shaft segment deformation error vector, and flange error vector, respectively. is the adjoint transformation matrix.

[0039] in This represents the support positioning error, such as the misalignment or positional deviation of the bearing housing. The measurement object is the bearing housing or support structure. The specific measurement method is to use instruments such as laser trackers to measure the positional deviation (such as vertical and horizontal offset) of the actual center of the bearing housing relative to the global reference, and directly use it as the error input.

[0040] Representing static elastic deformation error, it is mainly caused by the self-weight deflection of the shaft section (gravity bending) and the interference of the mating surface. The calculation / measurement object is the natural state of the shaft section itself under gravity. Specific operation: Usually, the deflection value at the journal is obtained by mechanical calculation (calculating the amount of sag caused by the self-weight of the shaft) or by static measurement before installation.

[0041] This represents static elastic deformation error and part manufacturing error, such as eccentricity and bending during shaft segment machining. The measurement object is a single shaft segment (at the factory). The specific operation is as follows: through workshop inspection, the geometric runout or eccentricity of the flange at the end of the shaft segment is measured and converted into a spatial error vector.

[0042] Step S5: Based on the sensitivity matrix and the set of error sources, construct the state equation describing the error accumulation. The expression of the state equation is: ; in Let be the cumulative pose error at the end of the k-th axis segment. The cumulative pose error at the end of the (k-1)th shaft is... Let be the state transition matrix, representing the spatial propagation characteristics of the rigid body geometric properties (such as length and rotation angle) of the k-th shaft segment component to the upstream cumulative error flow. Its value is the _th ... The nominal homogeneous transformation matrix of the root axis segment from the input end face to the output end face The adjoint transformation matrix of the inverse matrix is ​​expressed as: Its physical meaning is that it quantifies the end of the previous stage. After the physical length of this shaft segment is extended, the resulting geometric lever amplification effect... The input coupling matrix represents the weight distribution relationship between each local error source at this level and the accuracy of the shaft end. It is a block matrix, composed of various sensitivity matrices. To maintain consistency with the kinematic transport chain, the sub-blocks are arranged in physical topological order: Through adjoint transformation, the different error sources distributed in the support, shaft, and flange surface are accurately mapped into equivalent error components in the end output coordinate system based on their spatial location differences. The input error vector is defined as the set of four core error sources introduced during the assembly of this level of shaft system, and its element order is the same as... Strict correspondence, specific The expression is: , System noise error represents the undetermined, random, and nonlinear interference factors in the error flow model of shaft assembly. Specifically, these include: measurement noise, such as reading jitter when measuring with a laser tracker or feeler gauge; environmental interference, such as field vibration and thermal deformation caused by small temperature changes; and unmodeled high-order errors, such as high-order minute quantities ignored during model linearization.

[0043] To determine the optimal control scheme, this invention constructs an error sensitivity evaluation system based on variance contribution rate on the basis of establishing a high-reliability error flow model. By quantifying the influence weight of each error source on the terminal accuracy, key control characteristics (KCC) are identified, and a hierarchical optimization strategy is formulated.

[0044] Step S6: Construct the sensitivity coefficient of the i-th type of error source based on the state equation and compare it with the judgment threshold. The specific steps are as follows: Definition of the first Sensitivity coefficients of error sources To quantify the contribution of variations from a single error source to the total error:

[0045] In the formula For the input coupling matrix sub-block of the i-th type of error source, This represents the nominal value of the i-th type of error source (such as flange opening or bearing housing deviation). For the Euclidean norm, This is the theoretical prediction error vector at the end of the k-th shaft segment calculated based on the state equation.

[0046] Using the established discrete state equations, the theoretical prediction error vector at the end of the k-th shaft segment can be calculated. Compare it with the design tolerance range Comparison: Acceptance Criterion: When the prediction error is within the allowable tolerance range If the current process plan and equipment status are deemed to be good, the assembly is carried out according to the established procedures.

[0047] Out-of-tolerance warning: When the prediction error exceeds the allowable tolerance range. If the system is deemed to have a risk of accuracy failure, error source tracing analysis must be initiated immediately, and targeted corrective measures must be taken for key components or processes.

[0048] For out-of-tolerance operating conditions, a sensitivity coefficient is defined. The physical meaning of this index is: when other conditions remain unchanged, if the i-th type of error source undergoes a unit change (for example, the opening of the joint surface increases by 0.01 mm), by what proportion will the value of the observation equation change?

[0049] Step S7: If the sensitivity coefficient is greater than the judgment threshold, it is judged as a high-sensitivity process with critical control characteristics, and a source blocking strategy is adopted; otherwise, it is judged as a non-critical process, and a reverse compensation strategy is adopted.

[0050] If the sensitivity coefficient is less than the judgment threshold, it is determined to be a non-critical process, and a reverse compensation strategy is adopted. The reverse compensation strategy is as follows: under the premise that the source error is under control, the optimal adjustment amount of the bearing housing is calculated by utilizing the inverse property of the state transition matrix.

[0051] ; in It is the pseudo-inverse of the sensitivity matrix. The target value to be achieved is usually 0, meaning there is no error.

[0052] The recommended threshold value for judgment λ is 0.3~0.4, used for significance testing of each process: Dominant type of interface If the sensitivity of the coupling error at the mating surface significantly exceeds the standard, it indicates that minute flange geometric defects are severely amplified through the axial length lever. In this case, the "flange mating process" is determined to be a critical control process.

[0053] Support-dominant If the support error sensitivity exceeds the standard, it indicates that the bearing housing position deviation is the main cause of the straightness deviation. In this case, the "bearing housing positioning process" is determined to be a critical control process.

[0054] Based on the above identification results, the following closed-loop control strategy will be implemented: Source blocking strategy (for high-sensitivity items): For items identified as... For example, when sensitivity analysis shows that the flange flatness deteriorates by a certain amount, the flange mating surface is subject to refined control. When the end runout exceeds the tolerance, laser tracking compensation technology is preferred to cut off the error amplification path from the source.

[0055] Reverse compensation strategy (for residual errors): Under the premise that the source error is under control, the optimal adjustment amount of the bearing housing is calculated by utilizing the inverse property of the state transition matrix. By finely adjusting the vertical and horizontal positions of the bearing housing, the accumulated rigidity transmission error is offset, ensuring that the final product quality is up to standard.

[0056] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for identifying and optimizing key processes in shaft alignment based on an error flow model, characterized in that, Includes the following steps: Step S1: Construct a set of error sources in the shaft assembly process, and establish relevant spatial coordinate systems based on the assembly topology. The spatial coordinate systems include the global reference coordinate system O, the support feature coordinate system B, the shaft segment component coordinate system S, and the flange interface coordinate system F. Step S2: Based on the principle of infinitesimal rigid body coordinate transformation, and according to the topological order of each spatial coordinate system, establish the nominal pose transfer equation from the input end mating surface to the output end flange. Step S3: Based on the flange opening value and misalignment value measured on site, construct the contact error vector of the mating surface in combination with the flange diameter; Step S4: Perform first-order linearization on the nominal pose transfer equation based on the contact error vector of the mating surface, and obtain the sensitivity matrices of the mating surface error, support error, shaft segment deformation and flange error. Step S5: Based on the sensitivity matrix and the set of error sources, construct the state equation describing the accumulation of error; Step S6: Construct the sensitivity coefficient of the i-th type of error source based on the state equation and compare it with the judgment threshold; Step S7: If the sensitivity coefficient is greater than the judgment threshold, it is judged as a high-sensitivity process with critical control characteristics, and a source blocking strategy is adopted; otherwise, it is judged as a non-critical process, and a reverse compensation strategy is adopted.

2. The method for identifying and optimizing key processes in shaft alignment based on an error flow model according to claim 1, characterized in that, The set of error sources includes part manufacturing errors, support positioning errors, static elastic deformation errors, mating surface contact errors, and noise errors.

3. The method for identifying and optimizing key processes in shaft alignment based on an error flow model according to claim 2, characterized in that, The specific steps of step S2 are as follows: Based on the topological order of each spatial coordinate, the nominal pose transfer equation is jointly constructed by the global reference coordinate system 0, the support feature coordinate system B, the shaft segment component coordinate system S, and the flange interface coordinate system F: ； in This represents the total pose transformation matrix of the coordinate system F at the end flange interface of the kth shaft segment relative to the global reference coordinate system 0. This represents the minute rigid body transformation at the connection point between the (k-1)th and kth axis segments, determined by the contact error of the mating surface. The transformation matrix represents the support feature coordinate system B relative to the global reference coordinate system 0, and its deviation is determined by the support positioning error. The transformation matrix represents the coordinate system S of the shaft segment assembly relative to the coordinate system B of the support feature, and its deviation includes the influence of static elastic deformation error. The transformation matrix represents the transformation of the flange interface coordinate system F relative to the shaft segment assembly coordinate system S, and its deviation is determined by the manufacturing error of the part.

4. The method for identifying and optimizing key processes in shaft alignment based on an error flow model according to claim 1, characterized in that, The specific steps of step S3 are as follows: Assuming the flange diameter is D, the vertical / horizontal opening values ​​measured on-site are... The vertical / horizontal misalignment values ​​are respectively The contact error vector of the combined surface is: 。 5. The method for identifying and optimizing key processes in shaft alignment based on an error flow model according to claim 1, characterized in that, The specific steps of step S4 are as follows: Based on the assembly process of the shaft system from stern to bow, the contact error vector of the mating surface is determined. Considering the initial input attitude disturbance of the k-th axis segment, the total error screw at the end of the k-th axis segment is derived based on the principle of differential motion. for: ； in The expression for the sensitivity matrix of the combined surface contact error is as follows: ； To support the error sensitivity matrix, its expression is: ； The deformation sensitivity matrix of the shaft segment is expressed as follows: ； The flange error sensitivity matrix is ​​expressed as follows: ； It is the identity matrix. These are the support error vector, shaft segment deformation error vector, and flange error vector, respectively. is the adjoint transformation matrix.

6. The method for identifying and optimizing key processes in shaft alignment based on an error flow model according to claim 5, characterized in that, The adjoint transformation matrix is ​​defined as follows: Define the transformation matrix. Its corresponding The adjoint transformation matrix is ​​defined as: ,in displacement vector An antisymmetric matrix.

7. The method for identifying and optimizing key processes in shaft alignment based on an error flow model according to claim 6, characterized in that, The expression for the state equation in step S5 is: ； in Let be the cumulative pose error at the end of the k-th axis segment. The cumulative pose error at the end of the (k-1)th shaft is... Let be the state transition matrix, and let its value be the th state transition matrix. The nominal homogeneous transformation matrix of the root axis segment from the input end face to the output end face The adjoint transformation matrix of the inverse matrix is ​​expressed as: , The input coupling matrix is ​​expressed as follows: , The input error vector is expressed as follows: , This represents system noise error.

8. The method for identifying and optimizing key processes in shaft alignment based on an error flow model according to claim 1, characterized in that, The specific steps of step S6 are as follows: Definition of the first Sensitivity coefficients of error sources To quantify the contribution of variations from a single error source to the total error: In the formula For the input coupling matrix sub-block of the i-th type of error source, Let be the nominal value of the i-th type of error source. For the Euclidean norm, This is the theoretical prediction error vector at the end of the k-th shaft segment calculated based on the state equation.

9. The method for identifying and optimizing key processes in shaft alignment based on an error flow model according to claim 6, characterized in that, The specific steps for adopting the reverse compensation strategy in step S7 are as follows: If the sensitivity coefficient is less than the judgment threshold, it is determined to be a non-critical process, and a reverse compensation strategy is adopted. The reverse compensation strategy is as follows: under the premise that the source error is under control, the optimal adjustment amount of the bearing housing is calculated by utilizing the inverse property of the state transition matrix. ； in It is the pseudo-inverse of the sensitivity matrix. This represents the target value that is expected to be achieved.

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