Method for identifying and optimizing key procedures of shafting alignment based on error flow model

Abstract

The shafting alignment key process identification and optimization method based on the error flow model of the application comprises the following steps: constructing an error source set in the shafting assembly process, establishing a related space coordinate system based on the assembly topological relationship; establishing a nominal pose transmission equation based on the micro-element rigid body coordinate transformation principle; constructing a joint surface contact error vector based on the flange opening value and the flange gap value measured on site and the flange diameter; performing first-order linearization processing on the nominal pose transmission 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; constructing a sensitivity coefficient based on the state equation and comparing the sensitivity coefficient with a judgment threshold to identify a key control characteristic; and accurately predicting errors by quantifying the transmission mechanism of each error source and accurately identifying key processes in combination with the sensitivity coefficient so as to take reasonable strategies for optimization and ensure product quality.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mechanical assembly, in particular to a shafting alignment key process identification and optimization method based on error flow model. BACKGROUND

[0002] In the process of shafting alignment (straight alignment), due to the large span of the shafting and the multiple support points, the machining or installation error of a single support point is often transmitted to the entire system through rigid coupling of the shaft body, resulting in difficulty in ensuring the final coaxiality. The current straight alignment mainly relies on optical instruments (such as laser theodolite) to measure geometric deviation, and the "measurement-adjustment-remeasurement" trial and error is carried out according to experience, lacking systematic quantitative analysis of error sources and transmission paths, resulting in low alignment efficiency and unstable accuracy.

[0003] In the existing research on shafting installation and alignment process, although there are simulation models based on design data or transfer learning methods based on measured data, they mainly focus on the prediction and correction of the final state, lacking in-depth analysis of the whole process mechanism of error "generation-transmission-accumulation-amplification" in the assembly process. Specifically, the existing technology has the following deficiencies in practical application:

[0004] 1. Lack of quantitative description of error space transmission mechanism: The existing method regards the shafting error as a simple linear superposition of component deviations, ignoring the "geometric lever amplification effect" unique to long shafting. That is, it fails to quantify how a small joint surface angle deviation (such as flange opening) at the front end is significantly amplified into a large radial runout at the end after transmission through the long shaft section. This results in a theoretical prediction result that is often smaller than the actual deviation, making it difficult to guide high-precision assembly.

[0005] 2. Theoretical model is disconnected from field process data: Existing error modeling is often based on abstract mathematical vectors, failing to establish a direct mapping relationship between them and actual measurable process parameters (such as flange opening value Gap and offset value Offset) of workers in the field. This makes it difficult for the theoretical model to directly use field measurement data for real-time calculation, leading to the situation of "algorithm for algorithm, dry for dry".

[0006] 3. Lack of scientific basis for key process identification and precision allocation: Currently, when developing process schemes, a uniform tolerance control standard is often used for all processes, or "trial and error" adjustments are made based on experience. Due to the lack of evaluation system based on sensitivity analysis, construction personnel cannot identify which process is the "key control characteristic (KCC)" that affects the final accuracy, resulting in high-precision processing in non-key links and insufficient control in key links (such as high-sensitivity joint surfaces), causing high rework rate and low alignment efficiency. SUMMARY

[0007] In view of this, the application proposes a shafting alignment key process identification and optimization method based on an error flow model.

[0008] The technical scheme of the application is implemented as follows:

[0009] The shafting alignment key process identification and optimization method based on an error flow model comprises the following steps:

[0010] Step S1, an error source set in the shafting assembly process is constructed, and relevant spatial coordinate systems are established based on assembly topological relations, the spatial coordinate systems comprising a global reference coordinate system 0, a support feature coordinate system B, a shaft segment assembly coordinate system S, and a flange interface coordinate system F;

[0011] Step S2, based on the micro-element rigid body coordinate transformation principle, a nominal pose transmission equation from an input end joint surface to an output end flange is established according to the topological order of the spatial coordinate systems;

[0012] Step S3, based on the measured flange opening value and the flange skin error value, a joint surface contact error vector is constructed in combination with the flange diameter;

[0013] Step S4, the nominal pose transmission equation is linearized to the first order based on the joint surface contact error vector, and a sensitivity matrix of the joint surface error, the support error, the shaft segment deformation, and the flange error is obtained;

[0014] Step S5, according to the sensitivity matrix and the error source set, a state equation describing error accumulation is constructed;

[0015] Step S6, a sensitivity coefficient of the i-th type of error source is constructed based on the state equation, and is compared with a judgment threshold;

[0016] Step S7, if the sensitivity coefficient is greater than the judgment threshold, it is determined that the process is a high-sensitivity process of a key control characteristic, and a source blocking strategy is adopted, otherwise, it is determined that the process is a non-key process, and a reverse compensation strategy is adopted.

[0017] Preferably, the error source set comprises a part manufacturing error, a support positioning error, a static elastic deformation error, a joint surface contact error, and a noise error.

[0018] Preferably, the specific steps of step S2 are as follows: according to the topological order of the spatial coordinates, the nominal pose transmission equation is constructed by the global reference coordinate system 0, the support feature coordinate system B, the shaft segment assembly coordinate system S, and the flange interface coordinate system F.

[0019] ;

[0020] wherein represents the total pose transformation matrix of the flange interface coordinate system F of the end of the kth shaft segment relative to the global reference coordinate system 0;

[0021] represents the infinitesimal rigid body transformation at the joint between the k-1th shaft segment and the kth shaft segment, determined by the joint surface contact error;

[0022] represents the transformation matrix of the support feature coordinate system B relative to the global reference coordinate system 0, the deviation of which is determined by the support positioning error;

[0023] represents the transformation matrix of the shaft segment assembly coordinate system S relative to the support feature coordinate system B, the deviation of which contains the influence of the static elastic deformation error;

[0024] represents the transformation matrix of the flange interface coordinate system F relative to the shaft segment assembly coordinate system S, the deviation of which is determined by the part manufacturing error.

[0025] Preferably, the specific steps of the step S3 are as follows: assuming that the flange diameter is D, the vertical / horizontal direction opening values measured on site are , the vertical / horizontal direction skin error values are , and the joint surface contact error vector is:

[0026] .

[0027] Preferably, the specific steps of the step S4 are as follows:

[0028] According to the assembly process of the shaft system from the stern to the bow, the joint surface contact error vector is regarded as the initial input attitude disturbance of the kth shaft segment, and according to the differential motion principle, the total error screw of the end of the kth shaft segment is derived as:

[0029] ;

[0030] wherein is the joint surface contact error sensitivity matrix, and the expression thereof is:

[0031] ;

[0032] is the support error sensitivity matrix, and the expression thereof is:

[0033] ;

[0034] is the shaft segment deformation sensitivity matrix, and the expression thereof is:

[0035] ​ ;

[0036] is the flange error sensitivity matrix, which is expressed as:

[0037] ;

[0038] is the unit matrix, are the support error vector, the shaft segment deformation error vector and the flange error vector, respectively, is the adjoint transformation matrix.

[0039] Preferably, the adjoint transformation matrix is defined as: defining the transformation matrix , which corresponds to The adjoint transformation matrix is defined as: , wherein is the anti-symmetric matrix of the displacement vector .

[0040] Preferably, the expression of the state equation in step S5 is:

[0041] ;

[0042] , wherein is the cumulative pose error of the end of the kth shaft segment, is the cumulative pose error of the end of the k-1th shaft segment, is the state transition matrix, which takes the value of the adjoint transformation matrix of the inverse matrix of the nominal homogeneous transformation matrix of the kth shaft segment from the input end face to the output end face, is expressed as: , is the input coupling matrix, which is expressed as: , is the input error vector, which is expressed as: , is the system noise error.

[0043] Preferably, the specific steps of step S6 are:

[0044] defining the sensitivity coefficient of the i-th error source to quantify the contribution of a single error source to the total error:

[0045]

[0046] , wherein is the input coupling matrix sub-block of the i-th error source, is the nominal value of the i-th error source,​ is the Euclidean norm, is the theoretical prediction error vector of the end of the kth axial segment calculated according to the state equation.

[0047] Preferably, the specific step of adopting the reverse compensation strategy in the step S7 is:

[0048] If the sensitivity coefficient is less than the determination threshold, it is determined that the process is a non-key process, and a reverse compensation strategy is adopted, wherein the reverse compensation strategy is: under the premise of controlling the source error, the optimal adjustment amount of the bearing seat is calculated by using the reverse property of the state transition matrix

[0049] ;

[0050] wherein is the pseudo-inverse of the sensitivity matrix, is the target value to be achieved.

[0051] Compared with the prior art, the beneficial effects of the present application are:

[0052] The shafting key process identification and optimization method based on the error flow model of the present application introduces the error flow theory, and after the modeling range of the error flow is determined, the nominal pose transmission equation is constructed based on the assembly topological relationship of each object in the shafting assembly process, which is used to represent the spatial error transmission motion flow direction. Then, the contact error vector of the joint surface is constructed based on the data measured on site, the differential motion principle and Lie group Lie algebra theory are introduced, the nominal pose transmission equation is linearized to the first order, and the sensitivity matrix corresponding to each error source can be obtained, so that the amplification multiple of each error source to the end through the geometric lever effect can be quantified. Then, based on the sensitivity matrix, the multi-stage shafting error flow discrete state space equation can be constructed, the theoretical prediction error vector generated from the state equation can be used to calculate the sensitivity coefficient, which is compared with the preset determination threshold, and the key process is analyzed and identified. Then, targeted strategy optimization can be performed to cut off or eliminate the error, and the quality of the final product is ensured to be qualified. BRIEF DESCRIPTION OF DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only preferred embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0054] Figure 1 is the flow chart of the shafting key process identification and optimization method based on the error flow model of the present application. DETAILED DESCRIPTION

[0055] In order to better understand the technical content of the present application, a specific embodiment is provided below, and the present application is further described in conjunction with the drawings.

[0056] Referring to Figure 1 , the application provides a key process identification and optimization method for shafting alignment based on an error flow model, which comprises the following steps:

[0057] Step S1, constructing an error source set in the shafting assembly process, the error source set comprising part manufacturing errors, support positioning errors, static elastic deformation errors, joint surface contact errors and noise errors, and simultaneously establishing relevant spatial coordinate systems based on assembly topological relationships, the spatial coordinate systems comprising a global reference coordinate system 0, a support feature coordinate system B, a shaft segment assembly coordinate system S and a flange interface coordinate system F;

[0058] The multi-stage shafting assembly process divides the assembly of a single node into two stages of positioning and docking and fastening connection, wherein the part manufacturing errors (such as eccentricity) and the support positioning errors (such as bearing seat misalignment) cause the rotation center of the shafting to deviate from the theoretical reference, which is defined as the geometric coaxiality error; the static elastic deformation error is caused by the deflection of the shaft segment under its own weight and the interference amount of the mating surface; the joint surface contact error is a nonlinear transmission error caused by the uneven contact stiffness of the docking surface and the interaction of the micro topography; the noise error is defined as a random disturbance term.

[0059] Step S2, based on the principle of micro-element rigid body coordinate transformation, the nominal pose transmission equation from the input end joint surface to the output end flange is established according to the topological order of each spatial coordinate system, and the specific steps are as follows: according to the assembly topological order of each object in the spatial coordinate, the global reference coordinate system 0, the support feature coordinate system B, the shaft segment assembly coordinate system S and the flange interface coordinate system F jointly constitute the nominal pose transmission equation:

[0060] ;

[0061] Wherein represents the total pose transformation matrix of the flange interface coordinate system F at the end of the kth shaft segment relative to the global reference coordinate system 0;

[0062] represents the micro rigid body transformation at the connection between the k-1th shaft segment and the kth shaft segment, which is determined by the joint surface contact error;

[0063] represents the transformation matrix of the support feature coordinate system B (i.e. the bearing seat) relative to the global reference coordinate system 0, and the deviation is determined by the support positioning error;

[0064] represents the transformation matrix of the shaft segment assembly coordinate system S relative to the support feature coordinate system B, and the deviation contains the influence of the static elastic deformation error.

[0065] is the transformation matrix of flange interface coordinate system F relative to shaft segment assembly coordinate system S, whose deviation is determined by part manufacturing error (such as shaft segment bending, eccentricity).

[0066] For a single k-th shaft, the error transmission chain is constructed as input end joint surface → support feature → shaft segment assembly → output end flange.

[0067] According to the principle of differential motion, the small position error and attitude error of any rigid body can be represented by a space vector.

[0068] Let be the error vector:

[0069]

[0070] Wherein the first three are position deviations, and the last three are angle deviations.

[0071] For each transformation matrix in the nominal pose transmission equation , its actual value can be represented as the product of the nominal value and the small error matrix :

[0072]

[0073] Wherein is the differential transformation matrix constructed by the vector , and the nominal value is used in subsequent calculation of the theoretical model.

[0074] To realize the docking of the theoretical model and the field process, the discrete data measured by the joint surface need to be mapped to the specific components of the error vector , therefore, in step S3, based on the measured flange opening value and the skin error value, the joint surface contact error vector is constructed based on the flange diameter, and the specific steps are as follows: let the flange diameter be D, the measured vertical / horizontal direction opening values be , and the vertical / horizontal direction skin error values be , and the joint surface contact error vector is:

[0075] .

[0076] Since the coordinate systems defined by each error source (bearing, shaft segment, flange, joint surface) are different, they cannot be directly added together, therefore, the accompanying transformation matrix is used to project all local errors to the same coordinate system (usually the end flange coordinate system​ or global coordinate system Accumulate.

[0077] Transformation matrix corresponding The adjoint transformation matrix is ​​defined as:

[0078] in It is a displacement vector The antisymmetric matrix (used to calculate the cross product).

[0079] 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:

[0080]

[0081] 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:

[0082]

[0083] 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.

[0084] 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:

[0085] 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:

[0086] ;

[0087] In the formula, each Jacobian matrix represents a specific element. The spatial transfer characteristics of the corresponding error source from the location to the end of the shaft segment are characterized and defined according to the physical topology sequence as follows:

[0088] The initial coupling state of the joint surface contact error sensitivity matrix corresponds to the first end of the transfer chain. Since the error occurs at the beginning of the shaft segment, it must pass through the support structure and the entire geometric length of the shaft segment to reach the end, so it is most affected by the geometric lever effect, and its expression is:

[0089] ;

[0090] The error contains the geometric information of the entire shaft length, and its norm is the largest, indicating that this factor has the strongest destructive power on the system accuracy.

[0091] The support error sensitivity matrix corresponds to the support feature coordinate system , which needs to pass through the shaft segment assembly and the end flange, and its expression is:

[0092] ;

[0093] The shaft segment deformation sensitivity matrix corresponds to the shaft segment assembly coordinate system , which needs to pass through the end flange interface, and its expression is:

[0094] ;

[0095] The flange error sensitivity matrix corresponds to the flange interface coordinate system of the end of the transfer chain Since the error is directly located at the output end, there is no geometric amplification path, and its expression is:

[0096] ;

[0097] is a unit matrix, and the end manufacturing error is linearly superimposed into the system total error with a 1:1 ratio, are the support error vector, the shaft segment deformation error vector, and the flange error vector, respectively, is the adjoint transformation matrix.

[0098] wherein represents the support positioning error, such as the misalignment or position deviation of the bearing seat, and the measurement object is the bearing seat or the support structure. The specific measurement method is to use instruments such as laser trackers to measure the position deviation (such as up and down, left and right deviation) of the actual center of the bearing seat relative to the global reference, and directly input the error.

[0099] Representing static elastic deformation error, mainly caused by the shaft segment self-weight deflection (gravity bending) and the interference amount of the matching surface, the calculation / measurement object is the natural state of the shaft segment itself under gravity. The specific operation is: usually through mechanical calculation (calculate the sag amount of the shaft due to self-weight) or static measurement before installation, the deflection value at the shaft neck is obtained.

[0100] Representing static elastic deformation error, part manufacturing error, such as eccentricity, bending, etc. during shaft segment processing, the measurement object is a single shaft segment (when leaving the factory), and the specific operation is: through workshop detection, the geometric runout or eccentricity of the flange at the end of the shaft segment is measured, which is converted into a spatial error vector.

[0101] Step S5, according to the sensitivity matrix and the error source set, a state equation describing error accumulation is constructed, and the expression of the state equation is:

[0102]

[0103] Wherein is the cumulative pose error of the end of the kth shaft segment, is the cumulative pose error of the end of the k-1th shaft, is the state transition matrix, representing the spatial transmission characteristics of the rigid body geometric properties (such as length, rotation angle) of the kth shaft segment assembly to the upstream cumulative error flow, and its value is the inverse matrix of the nominal homogeneous transformation matrix of the kth shaft segment from the input end face to the output end face . is the adjoint transformation matrix of the inverse matrix of the nominal homogeneous transformation matrix of the kth shaft segment from the input end face to the output end face, and the expression is: , which quantifies the geometric lever amplification effect of the end of the last stage after the physical length extension of the current stage shaft segment, is the input coupling matrix, representing the weight distribution relationship of each local error source of the current stage to the shaft system end precision, is a block matrix composed of each sensitivity matrix, and each sub-block is arranged in physical topological order to maintain consistency with the kinematic transmission chain: , through the adjoint transformation, the different error sources distributed on the support seat, shaft body and flange face are accurately mapped into equivalent error components in the output coordinate system of the end, according to their spatial position difference, is the input error vector, defined as the four core error source set introduced in the assembly process of the current shaft system, and the element order strictly corresponds to , and specifically, The expression of 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.

[0104] 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.

[0105] 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:

[0106] Definition of the first Sensitivity coefficients of error sources To quantify the contribution of variations from a single error source to the total error:

[0107]

[0108] 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.

[0109] 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:

[0110] 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.

[0111] 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.

[0112] For out-of-tolerance operating conditions, a sensitivity coefficient is defined. The physical meaning of the index is that when other conditions remain unchanged, if the unit of the i-th error source changes (for example, the joint surface opening increases by 0.01 mm), how much the numerical value of the observation equation will change.

[0113] Step S7: If the sensitivity coefficient is greater than the determination threshold, it is determined that the process is a high-sensitivity process of the key control characteristic, and a source blocking strategy is adopted, otherwise it is determined that the process is a non-key process, and a reverse compensation strategy is adopted.

[0114] If the sensitivity coefficient is less than the determination threshold, it is determined that the process is a non-key process, and a reverse compensation strategy is adopted, which is to calculate the optimal adjustment amount of the bearing seat by using the inverse property of the state transition matrix on the premise that the source error is controlled

[0115] ;

[0116] wherein is the pseudo-inverse of the sensitivity matrix, is the target value to be achieved, usually 0, i.e. no error.

[0117] The recommended value of the determination threshold λ is 0.3-0.4, which is used for significance test of each process:

[0118] Joint dominant type If the sensitivity of the joint coupling error is significantly over-standard, it indicates that a small flange geometric defect is severely amplified through the shaft length lever. At this time, the "flange butt joint process" is determined as a key control process.

[0119] Support dominant type If the support error sensitivity is over-standard, it indicates that the bearing seat position deviation is the main cause of the straightness out-of-tolerance. At this time, the "bearing seat positioning process" is determined as a key control process.

[0120] Based on the above identification results, the following closed-loop control strategy is executed:

[0121] Source blocking strategy (for high sensitivity items): For the flange joint surface identified as , fine control is implemented. For example, when the sensitivity analysis shows that the flange flatness is deteriorated by , which will lead to the end run-out out-of-tolerance, laser tracking compensation process is preferably adopted to cut off the error amplification path from the source.

[0122] Reverse compensation strategy (for residual error): On the premise that the source error is controlled, the optimal adjustment amount of the bearing seat is calculated by using the inverse property of the state transition matrix , which offsets the accumulated rigid transmission error by fine-tuning the vertical and horizontal positions of the bearing seat, and ensures the final product quality.

[0123] The above description is only the preferred embodiment of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for identification and optimization of key alignment process based on error flow model of shafting, characterized in that, The method comprises the following steps: Step S1, constructing an error source set in the shafting assembly process, and establishing relevant spatial coordinate systems based on assembly topological relations, the spatial coordinate systems comprising a global reference coordinate system 0, a support feature coordinate system B, a shaft segment assembly coordinate system S, and a flange interface coordinate system F; Step S2, based on the micro-element rigid body coordinate transformation principle, establishing a nominal pose transmission equation from an input end joint surface to an output end flange according to the topological order of each spatial coordinate system; Step S3, based on the measured flange opening value and the flange gap value, combining the flange diameter to construct a joint surface contact error vector; Step S4, based on the joint surface contact error vector, performing first-order linearization processing on the nominal pose transmission equation, and obtaining the sensitivity matrix of the joint surface error, the support error, the shaft segment deformation, and the flange error; Step S5, according to the sensitivity matrix and the error source set, constructing a state equation describing error accumulation; Step S6, based on the state equation, constructing the sensitivity coefficient of the i-th type of error source, and comparing it with the determination threshold; Step S7, if the sensitivity coefficient is greater than the determination threshold, determining that it is a high-sensitivity process of a key control characteristic, and adopting a source blocking strategy, otherwise, determining that it is a non-key process, and adopting a reverse compensation strategy.

2. The method of identifying and prioritizing critical process sequences for error stream model based shaft alignment according to claim 1, wherein, The error source set comprises part manufacturing errors, support positioning errors, static elastic deformation errors, joint surface contact errors, and noise errors.

3. The method of claim 2, wherein the key process identification and optimization method is based on an error flow model. The specific steps of step S2 are: according to the topological order of each spatial coordinate, the nominal pose transmission equation is composed of the global reference coordinate system 0, the support feature coordinate system B, the shaft segment assembly coordinate system S, and the flange interface coordinate system F: ； wherein Tk represents the total pose transformation matrix of the kth flange interface coordinate system F at the end of the axis segment relative to the global reference coordinate system O; Rk-1k-1k-1k-1k-1k-1k-1k-1k-1k-1k-1k-1k-1k-1k-1k-1k-1k-1k-1k-1k-1k-1k-1k-1 R represents the transformation matrix of the support feature coordinate system B with respect to the global reference coordinate system 0, the deviation of which is determined by the support positioning error; a transformation matrix representing the transformation of the shaft segment assembly coordinate system S with respect to the support feature coordinate system B, whose bias incorporates the effects of static elastic deformation errors; F represents a transformation matrix of the flange interface coordinate system F with respect to the shaft segment assembly coordinate system S, the deviation of which is determined by part manufacturing errors.

4. The method of claim 3, wherein the key process identification and optimization method is based on an error flow model. The specific steps of the step S3 are: the flange diameter is D, the vertical / horizontal direction opening values measured on site are , the vertical and horizontal direction skin error values are , and the contact error vector of the joint surface is 。 5. The method of identifying and prioritizing critical process sequences for error stream model based shaft alignment according to claim 4, wherein, The specific steps of step S4 are: According to the shafting assembly process from the stern to the bow, the joint surface contact error vector The initial input attitude disturbance of the kth shaft segment is regarded as the kth shaft segment end total error screw derived according to the differential motion principle is: ； wherein is the contact error sensitivity matrix for the joining surface, which is expressed as: ； To support the error sensitivity matrix, whose expression is: ； is the sensitivity matrix of the shaft segment, which is expressed as: ； is the flange error sensitivity matrix, which is expressed as: ； is the identity matrix, are the support error vector, the axis segment deformation error vector and the flange error vector, respectively, is the adjoint transformation matrix.

6. The method of identifying and prioritizing critical process sequences for error stream model based shaft alignment according to claim 5, wherein, The definition of the adjoint transformation matrix is corresponding to the definition of the adjoint transformation matrix is where is the anti-symmetric matrix of the displacement vector ​ 7. The method of identifying and prioritizing critical process sequences for error stream model based shaft alignment according to claim 6, wherein, The expression of the state equation in step S5 is: ； wherein is the accumulated pose error at the end of the kth axis segment, is the accumulated pose error at the end of the k-1th axis segment, is the state transition matrix, which takes the value of the is the nominal homogeneous transformation matrix of the kth axis segment from the input face to the output face is the inverse matrix of the kth axis segment, expressed as: , is the input coupling matrix, expressed as: , is the input error vector, expressed as: , is the system noise error.

8. The method for shaft alignment key process identification and optimization based on error flow model according to claim 1, wherein, The specific steps of step S6 are: Definition of the first Sensitivity coefficients of the error sources Quantifying the contribution of individual error sources to the total error: wherein is the input coupling matrix sub-block for the i-th error source, is the nominal value of the i-th error source, is the Euclidean norm, is the theoretical prediction error vector at the end of the k-th axis segment calculated from the state equations.

9. The method of identifying and prioritizing critical process sequences for error flow model based shaft alignment according to claim 7, wherein, The specific steps of adopting the reverse compensation strategy in step S7 are: If the sensitivity coefficient is less than the determination threshold, it is determined that the process is a non-critical process, and a reverse compensation strategy is adopted, wherein the reverse compensation strategy is: under the premise that the source error is controlled, the optimal adjustment amount of the bearing seat is calculated by using the reverse property of the state transition matrix ； wherein is the pseudo-inverse of the sensitivity matrix, is the target value to be achieved.

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