High-precision intelligent processing and assembly preparation method for scaffold form fixer

Abstract

The application discloses a high-precision intelligent processing and assembly preparation method of a scaffold template fixing device, and relates to the technical field of scaffold template fixing devices. The application discloses a high-precision intelligent processing and assembly preparation method of a scaffold template fixing device, and relates to the technical field of scaffold template fixing devices. The application discloses a high-precision intelligent processing and assembly preparation method of a scaffold template fixing device, and relates to the technical field of scaffold template fixing devices. The application discloses a high-precision intelligent processing and assembly preparation method of a scaffold template fixing device, and relates to the technical field of scaffold template fixing devices. The application discloses a high-precision intelligent processing and assembly preparation method of a scaffold template fixing device, and relates to the technical field of scaffold template fixing devices. The application discloses a high-precision intelligent processing and assembly preparation method of a scaffold template fixing device, and relates to the technical field of scaffold template fixing devices. The application discloses a high-precision intelligent processing and assembly preparation method of a scaffold template fixing device, and relates to the technical field of scaffold template fixing devices. The application discloses a high-precision intelligent machining and assembly preparation method of a scaffold template fixing device, comprising: raw material screening and pretreatment; machine vision positioning clamping, comprising: based on the machine vision acquisition method, the global image acquisition of the raw material shape, clamping position and reference surface deviation is carried out, and the component processing reference coordinate system is constructed; intelligent precision cutting processing, comprising: the preparation raw material of the core component is subjected to rough cutting, fine cutting and edge polishing processing through the numerical control cutting equipment; intelligent size error dynamic compensation, comprising: the actual machining size and shape tolerance data of each component semi-finished product are collected in real time through the machine vision, the preset intelligent size compensation algorithm calculation formula is substituted, the machining size compensation amount and shape error correction amount of each component are obtained through measurement, and the numerical control cutting track is dynamically corrected; intelligent accurate assembly positioning, comprising: based on the machine vision, the assembly reference is positioned and automatically assembled; finished product checking and standardization. The corresponding preparation system is also disclosed.

Description

Technical Field

[0001] This invention belongs to the field of intelligent processing and assembly technology of building hardware components, specifically involving a high-precision intelligent processing and assembly method for scaffolding formwork fixing devices. Background Technology

[0002] Scaffolding formwork fixing devices are the core fastening components for concrete formwork support construction in building engineering. They are mainly used to fix building formwork, adjust the spacing of formwork support, and balance the lateral pressure during concrete pouring. Their processing accuracy, assembly consistency, and structural stability directly determine the overall safety and construction accuracy of the formwork support system. They are key components to ensure the quality of building structure forming and to eliminate safety hazards such as formwork running away, bulging, and displacement.

[0003] Currently, the manufacturing process of scaffolding formwork fixing devices in the domestic construction industry generally adopts a traditional machining combined with manual assembly model. The machining process relies on ordinary lathes and milling machines for rough cutting, lacking a precise dimensional error correction mechanism. Affected by multiple factors such as equipment wear, human error, raw material deformation, and environmental temperature deviations, the core components of the finished fixing device, such as the main body, screws, and adjusters, generally suffer from excessive dimensional deviations, substandard geometric tolerances, and poor component matching. The assembly process relies entirely on manual alignment and tightening, lacking standardized and intelligent gap correction and alignment compensation methods. This results in extremely poor assembly consistency for mass-produced formwork fixing devices, with significant differences in fitting gaps, locking force, and coaxiality parameters between different batches and even different products within the same batch. In actual construction applications, substandard formwork fixing devices are prone to problems such as loosening, uneven stress, and localized overload deformation. This not only causes quality issues such as excessive gaps in the formwork splices, structural dimensional deviations, and concrete molding defects, but also poses significant construction safety hazards such as formwork detachment and instability of the support system. Meanwhile, traditional manufacturing processes suffer from high degree of rigidity and poor iterative capabilities, making them unsuitable for the construction needs of scaffolding formwork with different specifications and load levels. This results in significant challenges in improving processing and assembly precision, low yield rates, and high production costs. Based on these industry-wide technical challenges, intelligent manufacturing, intelligent error adaptive compensation, and machine vision-based precise detection and correction have become the core upgrade directions for the manufacturing of architectural hardware components.

[0004] The scaffolding formwork support system is the core temporary support structure in cast-in-place concrete construction. Formwork fasteners, as key fastening components of the support system, play a crucial role in positioning, clamping, limiting, and load transfer, and are widely used in various cast-in-place concrete construction scenarios, including residential buildings, public buildings, municipal engineering, and industrial plants. With the standardization, refinement, and safety development of my country's construction industry, the requirements for formwork forming accuracy, support system safety, and construction standardization in construction projects are continuously increasing. Traditional, extensive component preparation processes are no longer suitable for the technical demands of modern construction. As mentioned above, the current mainstream scaffolding formwork fastener preparation process still follows the traditional model of machining + manual assembly. The overall technical system has many inherent defects, seriously restricting product quality and construction safety. Specific defects are mainly reflected in the following aspects.

[0005] First, traditional machining processes suffer from weak dimensional accuracy control and significant error accumulation. Traditional template fastener machining utilizes ordinary CNC equipment or machine tools for cutting, relying solely on preset machining programs without real-time dimensional detection or dynamic error compensation mechanisms. During batch processing, unavoidable factors such as tool wear, machine vibration, ambient temperature changes, and material heat treatment deformation continuously generate machining errors, which accumulate with each batch. This results in the linear dimensions, roundness, coaxiality, and flatness of core components like screws, fastener bodies, and adjusters generally exceeding industry-permissible error ranges. Component dimensional deviations from conventional processes are typically above ±0.1mm, with some irregularly shaped components even reaching ±0.2mm, far exceeding the ±0.05mm error standard required for high-precision construction, making it impossible to guarantee the basic accuracy of the components.

[0006] Secondly, traditional manual assembly methods suffer from extremely poor consistency and insufficient product stability. Current assembly processes rely entirely on operator experience for manual alignment, tightening, and gap adjustment, lacking standardized alignment benchmarks and precise gap control methods. Different operators exhibit significant differences in assembly habits, operating force, and alignment accuracy. Even the same operator cannot maintain consistent assembly accuracy at different times, resulting in inconsistent assembly gaps, screw tightness, and limit adjustment precision in batch-produced formwork fixtures. Excessive assembly gaps cause formwork fixtures to wobble and loosen during construction, leading to formwork displacement and bulging problems; insufficient gaps cause screw adjustment jamming, difficulty in assembly and disassembly, and breakage due to overload, severely impacting product performance and lifespan. Furthermore, manual assembly cannot achieve precise coaxiality control; eccentric component assembly leads to uneven support load distribution, and localized stress concentration can easily cause component deformation and breakage, posing significant construction safety hazards.

[0007] Secondly, traditional processes lack precise technical matching standards for raw material selection, resulting in poor overall performance compatibility of components. Current formwork fixture production often uses ordinary carbon steel or general-purpose alloy steel, with material selection solely considering cost factors and failing to take into account the high-frequency assembly / disassembly, dynamic loads, and repeated stress characteristics of the formwork fixture. Components made from ordinary raw materials exhibit poor wear resistance, weak deformation resistance, and low fatigue strength. After long-term use, they are prone to problems such as thread wear, screw bending, and limit failure, leading to short product lifespans, frequent replacements, and significantly increased construction costs. Furthermore, general-purpose raw materials lack precise heat treatment and homogenization pretreatment, resulting in uneven internal metallographic structure, high residual stress in processed components, and a tendency for natural deformation during later use, further exacerbating dimensional errors and assembly deviations.

[0008] Finally, traditional technologies are rigid and lack the ability to iterate and upgrade, failing to adapt to diverse construction needs. Current construction scenarios are increasingly diverse, with significant differences in support loads, installation dimensions, and stress requirements for standard formwork, heavy-duty formwork, and irregularly shaped formwork. This necessitates formwork fixtures with multi-specification and multi-parameter adaptability. However, traditional processing and assembly processes have fixed parameters and rigid systems, making it impossible to quickly adjust processing precision parameters and assembly gap standards according to different construction conditions. Process iteration is costly and time-consuming, and it severely lacks intelligent and adaptive upgrade capabilities, failing to meet the development needs of modern industrialized, standardized, and intelligent construction.

[0009] Existing technical solutions attempt to improve product precision by optimizing single processing steps, but these are limited to extensive improvements such as fine-tuning of equipment parameters and manual screening and calibration. They fail to form a complete high-precision manufacturing system encompassing raw material pretreatment, intelligent cutting, dynamic error compensation, precise assembly, and finished product verification. This approach cannot fundamentally solve the core problems of error accumulation, poor assembly consistency, and weak adaptability to various working conditions. Furthermore, without quantitative intelligent compensation algorithms, the precision improvement is limited, and the necessary conditions for technological iteration and upgrading are lacking. Given these shortcomings of existing technologies, developing a fully intelligent, high-precision, rapidly iterative, and multi-condition adaptable method for the processing and assembly of scaffolding formwork fixing devices is an urgent need in this field. Summary of the Invention

[0010] The purpose of this invention is to overcome the technical defects of existing scaffolding formwork fixing devices, such as low processing precision, poor assembly consistency, weak material adaptability, difficulty in technology iteration, and insufficient safety in use. It provides a high-precision intelligent processing and assembly method and system for scaffolding formwork fixing devices. By combining machine vision-based full-domain precision detection technology with a two-dimensional AI dimension compensation algorithm, it achieves dynamic correction of core component processing errors and adaptive adjustment of assembly precision. Combined with corresponding raw material selection and homogenization pretreatment processes, as well as standardized intelligent processing and assembly procedures, it greatly improves product processing precision, assembly consistency, and stability in use. It also possesses rapid process iteration capabilities, adapting to the needs of various construction scenarios. This invention relies on machine vision-based full-domain precision detection technology and intelligent dimension and assembly two-dimensional compensation algorithms to construct a fully intelligent, high-precision, and iterative formwork fixing device processing and assembly system, completely solving the technical defects of traditional processes such as low precision, poor consistency, insufficient safety, and weak adaptability. It represents an innovative technological upgrade in the field of intelligent manufacturing of building hardware.

[0011] To achieve the above technical objectives, the present invention adopts the following technical solution:

[0012] The first aspect of this invention is to provide a high-precision intelligent machining and assembly method for scaffolding template fixers, comprising the following steps:

[0013] S1, Raw material screening and pretreatment, including: screening raw materials for each core component of the scaffold template fixer, performing heat treatment and pretreatment on the selected raw materials to obtain homogenized processed raw materials; the core components include the template fixer body, adjusting screw, limit adjuster and locking gasket;

[0014] S2, machine vision positioning and clamping, includes: clamping the pre-processed raw materials of each component to the CNC cutting processing equipment, and performing full-domain image acquisition of the shape of the raw materials, clamping position and datum plane deviation based on machine vision acquisition method to construct the component processing datum coordinate system;

[0015] S3, intelligent precision cutting and machining, includes: based on the constructed machining reference coordinate system, using CNC cutting equipment to perform rough cutting, fine cutting and trimming and grinding of the raw materials for the preparation of the core components in an integrated process, to initially form semi-finished products of each core component;

[0016] S4, intelligent dynamic compensation for dimensional errors, includes: real-time acquisition of actual processing dimensions and geometric tolerance data of semi-finished components through machine vision, inputting into the preset intelligent dimensional compensation algorithm calculation formula, calculating the processing dimension compensation amount and geometric error correction amount of each component, dynamically correcting the CNC cutting trajectory, completing the secondary fine processing of the components, and obtaining high-precision finished components.

[0017] S5, intelligent and precise assembly positioning, includes: aligning each finished component with the assembly benchmark based on machine vision alignment technology, calculating the assembly fine-tuning amount by combining intelligent assembly gap compensation algorithm, adaptively adjusting the assembly position of each component, and completing the automated assembly of the template fixer.

[0018] S6, Finished product verification and finalization, including: verifying the dimensional accuracy, assembly coaxiality, and locking stability of the assembled template fixture, screening qualified finished products, and completing the overall preparation.

[0019] Preferably, in step S1, the raw material for preparing the template fixing body, the limit adjuster, and the locking gasket is 40Cr alloy structural steel, and the raw material for preparing the adjusting screw is 35CrMo alloy structural steel.

[0020] Preferably, the raw material pretreatment includes: heating the raw material to 830~870℃ and holding it for 2~3 hours, then oil quenching it, followed by tempering at 520~560℃ and holding it for 1.5~2.5 hours. After heat treatment, the surface roughness is polished to Ra≤1.6μm, and the dimensional deviation is controlled within ±0.02mm.

[0021] Preferably, in step S2, the machine vision acquisition method includes: using a binocular high-definition industrial camera in conjunction with a ring-shaped shadowless light source to perform machine vision acquisition; then, extracting the raw material contour reference points through a sub-pixel edge detection algorithm; in this embodiment, the image acquisition resolution is not less than 25 million pixels, the acquisition frame rate is 30~60 frames / s, and the reference coordinate system positioning error is ≤0.01mm.

[0022] Preferably, in step S3, the rough cutting feed rate is 0.2~0.4 mm / r, and the spindle speed is 800~1200 r / min; the finish cutting feed rate is 0.05~0.15 mm / r, and the spindle speed is 1500~2000 r / min, and the initial dimensional deviation of the component after finish cutting is controlled within ±0.05 mm.

[0023] Preferably, in step S4, the intelligent size compensation algorithm includes a linear size compensation calculation formula for the component as shown in equation (1):

[0024] (1);

[0025] in, The total linear dimension compensation of the component, in mm, is used to characterize the axial / radial dimension correction displacement that needs to be performed in the next finishing process of the CNC cutting equipment; The machine tool CNC system reads this value in real time to automatically update the tool path coordinates, which is the target output value of the entire compensation logic. is a linear dimension compensation correction coefficient, a dimensionless coefficient, used to offset the nonlinear deviation correction weighting coefficient caused by tool elastic rebound, cutting thermal expansion and contraction and machine tool servo feed inertia during CNC cutting, with a value of 0.92~0.98; It represents the standard design linear dimension of the component, in mm, and is used to characterize the theoretical standard dimensions marked on the drawings of scaffolding formwork fixing parts, including all linear reference dimensions such as the outer diameter of the screw, the total length of the fixing body, the inner diameter of the adjuster and the thickness of the shim; The linear dimension of the component measured by machine vision, in mm, is used to characterize the real linear dimension of the semi-finished component after it has been formed, which is collected in real time by a binocular industrial camera and a sub-pixel edge detection algorithm. It includes real-time detection data of the actual diameter of the screw after processing, the actual total length of the fixture body, and the actual inner hole size of the adjuster. It is a dynamic variable. This is the inherent system error compensation value for the machine tool equipment, in mm, ranging from 0.005 to 0.015 mm.

[0026] Preferably, the formula for calculating the form and position tolerance deviation compensation is shown in equation (2):

[0027] (2);

[0028] in, The total compensation amount for the comprehensive form and position error of the component is in mm. It is the form and position correction value output by the algorithm, which is used by CNC equipment to correct the composite form and position deviation. This is the form and position compensation weight coefficient, dimensionless, with a value of 0.85~0.95, used to balance the problem of excessive compensation caused by cutting deformation and tooling clamping offset; The measured form and position deviation of the component in the X-axis direction, acquired by machine vision, is in mm and reflects the offset of the component's lateral contour and reference surface. The measured form and position deviation of the component in the Y-axis direction, acquired by machine vision, is in mm, reflecting the longitudinal contour and datum plane offset of the component.

[0029] Preferably, in step S5, the AI ​​assembly gap compensation algorithm sets the assembly reference gap according to the tolerance level of each component, with the standard assembly gap controlled between 0.02 and 0.06 mm. It collects the component assembly misalignment and gap unevenness through visual alignment and adaptively adjusts the displacement of the assembly tooling. Requirement: Assembly alignment accuracy ≤ 0.01 mm.

[0030] Preferably, in step S6, the finished product verification includes dimensional accuracy verification, assembly coaxiality verification, and load stability verification. Requirements: assembly coaxiality deviation ≤ 0.03 mm, relative displacement of components under rated load ≤ 0.02 mm, and change in assembly gap ≤ 0.01 mm after 500 repeated disassembly and assembly cycles.

[0031] A second aspect of the present invention provides a high-precision intelligent machining and assembly preparation system for scaffolding template fixers, used to implement the method of the first aspect, comprising:

[0032] The raw material pretreatment module (101) is used for raw material screening and pretreatment, including: screening raw materials for each core component of the scaffold template fixer, performing heat treatment and raw material pretreatment on the selected raw materials to obtain homogenized processed raw materials; the core components include the template fixer body, adjusting screw, limit adjuster and locking gasket;

[0033] The machine vision clamping and positioning module (102) is used for machine vision positioning and clamping, including: clamping the pre-processed component raw materials to the CNC cutting processing equipment, acquiring full-domain images of the raw material shape, clamping position and datum plane deviation based on the machine vision acquisition method, and constructing a component processing datum coordinate system.

[0034] The intelligent precision cutting module (103) is used for intelligent precision cutting processing, including: based on the constructed processing reference coordinate system, using CNC cutting equipment to perform rough cutting, fine cutting and trimming and grinding of the raw materials for the preparation of the core components in an integrated process, and initially forming semi-finished products of each core component;

[0035] The intelligent dimension error dynamic compensation module (104) is used for intelligent dimension error dynamic compensation, including: real-time acquisition of the actual processing dimensions and form and position tolerance data of each component semi-finished product through machine vision, substituting them into the preset intelligent dimension compensation algorithm calculation formula, calculating the processing dimension compensation amount and form and position error correction amount of each component, dynamically correcting the CNC cutting trajectory, completing the secondary fine processing of the component, and obtaining a high-precision finished component.

[0036] The intelligent precision assembly module (105) is used for intelligent precision assembly positioning, including: aligning each finished component with the assembly reference based on machine vision alignment technology, calculating the assembly fine adjustment amount by combining intelligent assembly gap compensation algorithm, adaptively adjusting the assembly position of each component, and completing the automated assembly of the template fixer.

[0037] The finished product verification and finalization module (106) is used for finished product verification and finalization, including: verifying the dimensional accuracy, assembly coaxiality, and locking stability of the assembled template fixture, screening qualified finished products, and completing the overall preparation.

[0038] The beneficial effects of the method and system of the present invention are as follows:

[0039] All the effects in raw material selection, processing precision, assembly consistency, safety performance, and technological iteration are based on quantified process parameters and measured data, specifically reflected in:

[0040] 1. This invention addresses the problems of poor material compatibility, high residual stress, easy deformation, and easy wear in traditional products by using differentiated and exclusive raw material selection and homogenization pretreatment processes. Based on the stress and motion characteristics of different components, this invention matches 40Cr and 35CrMo alloy structural steels. Compared to traditional ordinary carbon steel, the tensile strength, fatigue resistance, and wear resistance of the components are improved by more than 40%. Long-term use is free from problems such as thread wear, component deformation, and limit failure, more than doubling the product's service life. The quenching and tempering heat treatment and surface pretreatment processes eliminate internal stress and initial deformation errors in the raw materials, ensuring material homogenization and laying the foundation for high-precision machining and assembly, significantly reducing natural deformation errors during later use.

[0041] 2. Based on machine vision-based full-domain precision detection and dual-dimensional intelligent compensation algorithms, the system achieves quantification, dynamic, and full-coverage correction of processing errors, solving the technical problems of error accumulation and uncontrollable precision in traditional processes. Through two sets of quantitative calculation formulas—linear dimension compensation and geometric tolerance compensation—it achieves comprehensive and precise compensation for the linear dimensions and spatial geometric errors of components, reducing processing deviations from ±0.1mm in traditional processes to within ±0.02mm, improving processing accuracy by over 60%. Furthermore, batch processing accuracy is highly uniform, eliminating error accumulation issues, and increasing the product yield rate from the traditional 85% to over 99%, significantly reducing production losses and costs.

[0042] 3. Employing intelligent gap compensation and visual alignment assembly technology, this invention completely replaces manual assembly, achieving standardized control over the consistency of batch product assembly. Traditional manual assembly of batch products suffers from significant variations in assembly gaps, coaxiality, and locking force, resulting in inconsistent product quality. This invention, through intelligent adaptive assembly technology, controls assembly alignment accuracy to within 0.01mm, coaxiality deviation ≤0.03mm, and batch product assembly parameter consistency deviation ≤0.01mm, completely resolving the random error problem of manual assembly and ensuring stable and controllable product assembly quality.

[0043] 4. Significantly improves product safety and construction stability, eliminating potential safety hazards. The formwork fixing device prepared by this invention has uniform assembly gaps, uniform stress distribution, and no assembly eccentricity issues. It ensures stable load transfer, minimal deformation displacement under rated load, and minimal accuracy decay after repeated assembly and disassembly. During construction, there are no quality and safety issues such as loosening, shaking, formwork displacement, or bulging. This avoids major safety accidents such as support system instability or formwork detachment caused by the easy failure of traditional formwork fixing devices, significantly improving the safety and standardization level of building formwork support construction.

[0044] 5. Possesses strong technological iteration and upgrade capabilities. Algorithm parameters and process parameters can be quickly and adaptively adjusted according to different component specifications, construction loads, and industry standards. Without large-scale modifications to production equipment and processes, it can quickly adapt to the preparation needs of various formwork fixing devices, including standard, heavy-duty, and irregular shapes. The process iteration speed is fast, the adaptability is wide, and the upgrade cost is low. At the same time, it adapts to the development trend of building industrialization and intelligence. The entire process is intelligent, automated, and standardized, which can realize the mass, efficient, and high-precision industrial production of formwork fixing devices, replacing the traditional manual-dominated extensive production mode. It significantly reduces labor costs, improves production efficiency, and ensures product quality stability, and can be widely adapted to various construction engineering scenarios. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the specific embodiments or related technologies of the present invention, the drawings used in the description of the specific embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0046] Figure 1 This is a flowchart illustrating the high-precision intelligent machining and assembly method for scaffolding template fixers provided in an embodiment of the present invention.

[0047] Figure 2 This is a schematic diagram of the high-precision intelligent machining and assembly preparation system for scaffolding template fixers provided in an embodiment of the present invention. Detailed Implementation

[0048] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0050] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0051] like Figure 1 As shown, the first aspect of the present invention is to provide a high-precision intelligent machining and assembly method for scaffolding template fixing devices, thereby forming a closed-loop high-precision intelligent manufacturing system, comprising:

[0052] S1, raw material pretreatment, is used to achieve homogenization and low-stress treatment of raw materials, eliminating processing errors caused by raw material deformation from the source;

[0053] S2, machine vision clamping and positioning, is used to build a unified processing reference coordinate system to solve the problems of chaotic clamping reference and large positioning deviation in traditional clamping.

[0054] S3, intelligent precision cutting, is used to achieve rough and fine graded machining to ensure basic machining accuracy;

[0055] S4, intelligent dynamic compensation for dimensional errors, is used to accurately correct processing errors through quantization algorithms to achieve micron-level precision control;

[0056] S5, intelligent precision assembly, is used to ensure the consistency of batch product assembly based on visual alignment and gap compensation;

[0057] S6, Finished Product Verification and Approval, is used to achieve full-parameter quality control and ensure the finished product qualification rate and safety of use.

[0058] Steps S1-S6 are progressive and linked in a closed loop, forming a complete intelligent, high-precision and iterative manufacturing process system, which fundamentally improves the manufacturing quality and performance of the template fixator.

[0059] In a preferred embodiment, each core component has its own specific raw materials and pretreatment process parameters. The selection of raw materials is based on a precise matching of the stress characteristics, motion characteristics, and wear characteristics of different components of the template holder. Specifically:

[0060] On the one hand, the main body of the template fixer, the limit adjuster, and the locking shim are static load-bearing components. During operation, they mainly bear static compressive loads and support lateral pressures, without high-frequency reciprocating motion. Therefore, they require materials with high strength, high rigidity, resistance to compressive deformation, and good structural stability. Thus, the embodiment of this invention selects 40Cr alloy structural steel. After tempering heat treatment, this material has excellent tensile strength, yield strength, and structural rigidity, which can effectively resist the lateral pressure during concrete pouring and prevent component compression deformation and cracking.

[0061] On the other hand, the adjusting screw is a dynamic moving component that is frequently rotated and adjusted and reciprocated during operation, bearing alternating loads, frictional wear, and torsional stress. Therefore, it requires materials with high wear resistance, high toughness, fatigue resistance, and torsional fracture resistance. Thus, the embodiments of the present invention select 35CrMo alloy structural steel. Compared with 40Cr, this material has better hardenability, toughness, and fatigue resistance, and can adapt to the working conditions of high-frequency adjustment and repeated stress of the screw, effectively avoiding problems such as thread wear, screw breakage, and fatigue failure.

[0062] Meanwhile, the embodiments of the present invention define precise quenching and tempering heat treatment parameters and surface treatment parameters. Through the heat treatment process of oil quenching at 830~870℃ and tempering at 520~560℃, the internal metallographic structure of the raw material can be refined, the internal stress during the rolling and forging process of the raw material can be eliminated, and the overall homogeneity and mechanical property stability of the raw material can be improved. By grinding the surface roughness to Ra≤1.6μm and controlling the pretreatment dimensional deviation within ±0.02mm, burrs, uneven defects and initial deformation errors on the surface of the raw material can be completely eliminated, laying the foundation for subsequent high-precision cutting and gapless precision assembly, and ensuring the product accuracy and service life from the raw material end.

[0063] As a preferred implementation, the hardware parameters and positioning accuracy standards of the machine vision acquisition module are the hardware guarantee for achieving high-precision detection and accurate benchmark construction. The embodiments of this invention employ a binocular high-definition industrial camera combined with a ring-shaped shadowless light source acquisition architecture. Compared to traditional monocular vision acquisition, binocular vision can achieve three-dimensional spatial coordinate positioning, accurately acquiring full-domain parameters such as the planar contour, spatial position, clamping offset, and datum surface deformation of the component, avoiding the shortcomings of monocular vision which can only acquire two-dimensional dimensions and has large spatial positioning errors. A high-resolution configuration of 25 million pixels or more enables sub-pixel-level contour extraction, and an acquisition frame rate of 30~60 frames / s can meet the real-time acquisition requirements of batch continuous processing, ensuring processing efficiency and real-time detection. The sub-pixel edge detection algorithm can accurately identify the contour reference points, cutting reference surfaces, and assembly alignment references of the component raw materials, effectively avoiding the problems of insufficient accuracy and blurred edge recognition in traditional pixel-level detection, ultimately achieving a high-precision positioning effect with a processing reference coordinate system positioning error ≤0.01mm. This parameter setting can completely solve the problems of inconsistent accuracy of batch products caused by the lack of precise reference, large deviation of manual positioning, and inconsistent reference in traditional machining and clamping, and provide a unified and accurate spatial reference for subsequent cutting, error compensation and intelligent assembly.

[0064] As a preferred implementation, the process parameters for graded precision cutting are set. By matching differentiated cutting parameters through roughing and finishing machining modes, a balance between machining efficiency and machining accuracy is achieved. In the roughing stage, parameters are set with a large feed rate and medium-low speed (0.2~0.4 mm / r, spindle speed 800~1200 r / min). This quickly removes excess material, efficiently completes the initial forming of the component, significantly improves machining efficiency, and meets the needs of mass industrial production. In the finishing stage, precision machining parameters are set with a small feed rate and high speed (0.05~0.15 mm / r, spindle speed 1500~2000 r / min). This effectively reduces machining errors caused by cutting vibration and tool deformation, refines the surface finish of the component, corrects residual dimensional deviations and surface defects from the roughing stage, and precisely controls the initial machining dimensional deviation of the component within ±0.05 mm. The precise definition of graded cutting parameters avoids the technical defects of insufficient accuracy in single roughing and low efficiency in single finishing. At the same time, it provides accurate basic machining data for subsequent intelligent dimensional compensation and correction, ensuring the calculation accuracy and correction effect of the error compensation algorithm.

[0065] As a preferred implementation, the present invention defines a dual-dimensional intelligent size compensation algorithm calculation formula, including a linear size compensation calculation formula and a geometric tolerance deviation compensation calculation formula, thereby achieving comprehensive quantitative compensation for component size errors and geometric errors. The core traditional technology can only rely on manual experience for rough error correction, without the support of quantitative algorithms, resulting in low compensation accuracy, poor adaptability, and inability to adaptively adjust. The two sets of calculation formulas constructed in this invention are used to accurately calculate the linear length, width, and diameter size errors, roundness, flatness, coaxiality, and other spatial geometric errors of components, achieving full coverage and quantitative correction of processing errors. Among them, the linear size compensation calculation formula is used to compensate for the basic linear size deviation of the component, accurately correcting the problem of size over- or under-sized due to tool wear, raw material deformation, and equipment displacement during processing. The linear size compensation calculation formula is shown in formula (1):

[0066] (1);

[0067] in, The total linear dimension compensation amount of the component, in mm, is used to characterize the axial / radial dimension correction displacement that needs to be performed in the next finishing process of the CNC cutting equipment. It is a direct control parameter output by the algorithm. A positive number means that extra material needs to be removed by cutting (the actual measured size of the component is too large, and undercut compensation is needed), while a negative number means that the cutting feed needs to be reduced to avoid overcutting (the actual measured size of the component is too small). The output target value of the entire compensation logic is read in real time by the CNC system of the machine tool to automatically update the tool trajectory coordinates. It is the core quantitative parameter that connects machine vision inspection data and cutting actuator. All dimensional correction processes use this value as the execution benchmark. The linear dimension compensation correction coefficient is a dimensionless coefficient. In this embodiment of the invention, the value range is limited to 0.92 to 0.98. It is used to offset the nonlinear deviation correction weighting coefficient caused by tool elastic rebound, cutting thermal expansion and contraction and machine tool servo feed inertia during CNC cutting. When cutting metal alloys, the tool and steel will produce a small amount of elastic yielding due to the extrusion. The compensation amount of a single cut cannot be directly equal to the theoretical dimension difference. If the difference correction is directly used, overcutting and exceeding the tolerance will occur. Through numerous cutting calibration tests on 40Cr and 35CrMo alloy steels, the deformation springback law applicable to different diameters, wall thicknesses, and cutting speeds of various components of the template fixation device was determined. The standard component was set to 0.95, and the heavy-duty large-size component was set to 0.97, so that the compensation force could be adaptively matched with the component specifications. This indicates the standard design linear dimension of the component, expressed in mm (millimeters). It is used to characterize the theoretical standard dimensions specified in the drawings of scaffolding formwork fixing devices, including all linear reference dimensions such as the outer diameter of the screw, the total length of the fixing body, the inner diameter of the adjuster, and the thickness of the shims. It serves as the benchmark reference value for determining dimensional accuracy. To pre-enter the design benchmark constants into the intelligent compensation algorithm database, the component specifications of the two types of implementations, namely standard type and heavy-duty type, are stored separately. When the algorithm runs, the standard dimensions of the corresponding components are automatically retrieved as the benchmark for error calculation, ensuring that the error calculation benchmark for different specifications of parts is consistent. The linear dimensions of the component measured by machine vision, expressed in mm, represent the actual linear dimensions of the semi-finished component after forming, acquired in real time by a binocular industrial camera combined with a sub-pixel edge detection algorithm. This includes real-time detection data such as the measured diameter of the screw after processing, the measured total length of the fixture body, and the measured inner hole size of the adjuster. These are dynamic variables. To achieve real-time dynamic data acquisition, data is collected and updated synchronously after each rough cutting operation. This allows for real-time capture of dimensional changes caused by tool wear, material deformation, and machine tool displacement. Unlike traditional machining, which relies solely on the static calibration of the first piece, this method enables dynamic error calculation for each piece. This is the inherent system error compensation value for machine tool equipment, expressed in mm (millimeters). It represents the fixed inherent error constant formed by the superposition of mechanical clearance, minor deformation of guide rails, installation and fixing offset of vision camera, and minor thermal deformation under constant temperature environment in CNC lathes or milling machines. This is an inherent static deviation of the equipment and will not change with the processing dimensions of individual components. It needs to be calibrated in advance using standard gauge blocks to integrate the entire vision inspection-cutting equipment. This invention limits the value range to 0.005–0.015 mm, and standard processing equipment uniformly uses this value. Heavy-duty, large-scale processing equipment, due to its larger machine tool stroke and slightly increased mechanical clearance, takes... It is used to compensate for the fixed deviation of the underlying equipment and avoid the continuous accumulation of errors in the basic system.

[0068] The formula for calculating the form and position tolerance deviation is used to perform coupled calculation of the comprehensive form and position deviation of the two-dimensional plane of the component, solving the problem that traditional single-dimensional error detection cannot correct spatial composite deviation. The formula for calculating the form and position tolerance deviation is shown in equation (2):

[0069] (2);

[0070] in, The total compensation amount for the comprehensive form and position error of the component is in mm. It is the form and position correction value output by the algorithm, which is used by CNC equipment to correct composite form and position deviations such as flatness, roundness, and coaxiality. It is a dimensionless coefficient for form and position compensation, with a value of 0.85 to 0.95. It is used to balance the problem of excessive compensation caused by cutting deformation and tooling clamping offset, and to adapt to the form and position deviation distribution law of components of different specifications. The measured form and position deviation of the component in the X-axis direction, acquired by machine vision, is in mm and reflects the offset of the component's lateral contour and reference surface. The measured form and position deviation of the component in the Y-axis direction, acquired by machine vision, is in mm and reflects the longitudinal contour and datum plane offset of the component. This means that the Euclidean distance coupling calculation is performed on the bidirectional form and position deviations of X and Y to obtain the two-dimensional plane comprehensive original form and position deviation, and realize the unified quantification of composite deviations; Formula (2) first couples the measured deviations of the two axes to obtain the total original form and position error of the plane, and then multiplies it by the weight coefficient to correct the nonlinear machining interference, and outputs the form and position compensation amount that can be directly used for machine tool trajectory adjustment.

[0071] In this embodiment, the two sets of formulas work together to achieve comprehensive and accurate compensation for component processing errors, ensuring the stability and controllability of processing accuracy. At the same time, the parameters of the two sets of formulas can be quickly iterated and adjusted according to component specifications, processing conditions, and equipment status, demonstrating strong technical iteration capabilities.

[0072] As a preferred implementation, all parameter values ​​in the dual-dimensional intelligent size compensation algorithm calculation formula are obtained based on extensive processing experiments, equipment operating condition tests, and mechanical accuracy verification, possessing rigorous technical rationality and engineering practicality. In the linear size compensation formula, the size compensation correction coefficient... The value ranges from 0.92 to 0.98. This coefficient is used to adapt to the cutting inertia and tool springback characteristics of CNC cutting equipment, avoiding overcutting due to excessive compensation or residual errors due to insufficient compensation, and adapting to the cutting deformation patterns of components of different specifications; standard design dimensions. The theoretical dimensions for standardized component design serve as the benchmark for accuracy verification; actual machining dimensions... To ensure the real-time and accurate measurement of actual dimensions for machine vision acquisition, and to guarantee the accuracy and timeliness of error calculation; inherent error compensation values ​​of the equipment. With a value ranging from 0.005 to 0.015 mm, it is specifically used to compensate for inherent mechanical errors of machine tools, assembly clearance errors, and minor deformation errors caused by environmental temperature, achieving full-dimensional coverage of errors. In the geometric tolerance deviation compensation formula, the geometric tolerance weighting coefficient... The value is 0.85~0.95, which is used to balance the influence weight of X and Y axis deviations on the overall form and position accuracy, and adapt to the form and position deviation distribution law of different components; , These are the real-time measured deviation values ​​along both axes. The comprehensive form and position error is calculated through square root coupling, accurately reflecting the overall form and position distortion of the component and ensuring the comprehensiveness and accuracy of form and position compensation. The precise quantification of each parameter makes the compensation algorithm quantifiable, replicable, and iterative, completely eliminating the uncertainty of traditional experience-based processing.

[0073] In a preferred embodiment, the core process parameters and assembly control logic of the assembly gap compensation algorithm are used to achieve intelligent adaptive control of component assembly accuracy. The core assembly quality of the template fixer depends on the gap accuracy and alignment accuracy of each mating component. This embodiment of the invention is based on the national standard for tolerances in architectural hardware, setting the standard assembly gap control within 0.02~0.06mm. This gap range ensures flexible screw adjustment and smooth assembly / disassembly, while avoiding excessive gap leading to shaking and loosening, and excessive gap leading to jamming and overload. Through machine vision, the misalignment, gap unevenness, and coaxiality deviation of components during the assembly process are collected in real time. Based on the gap compensation algorithm, the fine-tuning displacement of the assembly fixture is calculated in real time, and the spatial position of the assembly platform and clamping fixture is adaptively adjusted to achieve precise alignment and uniform gap matching of each component, ultimately controlling the assembly alignment accuracy to ≤0.01mm. This intelligent assembly mode completely replaces traditional manual assembly, achieving a high degree of uniformity in assembly gap, alignment accuracy, and coaxiality for batch products, significantly improving product assembly consistency, and ensuring product stability and safety from the assembly end.

[0074] As a preferred embodiment, this invention establishes a multi-dimensional verification standard for finished products, constructing a quality verification system that integrates dimensions, assembly, and mechanical properties to ensure that the finished product meets quality standards and is safe and reliable in use. Dimensional accuracy verification ensures that the processing dimensions of the component foundation conform to design standards; the standard of assembly coaxiality deviation ≤0.03mm effectively avoids uneven stress and local stress concentration caused by assembly eccentricity, ensuring uniform load transfer; the relative displacement of the component under rated load ≤0.02mm ensures the structural stability of the product under construction stress, preventing load deformation and displacement loosening hazards; the change in assembly gap after 500 repeated disassembly and assembly cycles ≤0.01mm quantitatively assesses the product's wear resistance, fatigue resistance, and long-term stability, ensuring that the product's accuracy does not decrease and its safety performance does not degrade over long-term use. Through multi-dimensional and quantitative finished product verification standards, precise control of product quality is achieved, thereby improving product yield and engineering adaptability.

[0075] like Figure 2 As shown, a second aspect of the present invention provides a high-precision intelligent machining and assembly preparation system for scaffolding template fixers, used to implement the method of the first aspect, comprising:

[0076] The raw material pretreatment module (101) is used to achieve raw material homogenization and low-stress treatment, eliminating processing errors caused by raw material deformation from the source.

[0077] The machine vision clamping and positioning module (102) is used to construct a unified machining reference coordinate system to solve the problems of chaotic clamping reference and large positioning deviation in traditional clamping.

[0078] The intelligent precision cutting module (103) is used to realize rough and fine graded machining to ensure basic machining accuracy;

[0079] The intelligent dimensional error dynamic compensation module (104) is used to accurately correct processing errors through quantization algorithms to achieve micron-level precision control;

[0080] The intelligent precision assembly module (105) is used to ensure the consistency of batch product assembly based on visual alignment and gap compensation.

[0081] The finished product verification and finalization module (106) is used to achieve full-parameter quality control and ensure the finished product qualification rate and safety of use.

[0082] Application Example 1: High-precision intelligent machining and assembly process for standard scaffolding formwork fixing devices

[0083] Example 1 of this application describes the preparation process for standard scaffolding formwork support for conventional civil buildings. It is compatible with 18mm standard thickness building formwork and is suitable for construction scenarios with conventional loads in multi-story residential buildings and ordinary public buildings. The component specifications are: the total length of the formwork fixing body is 120mm, the diameter of the adjusting screw is 12mm, the inner diameter of the limit adjuster is 12mm, and the thickness of the locking washer is 3mm. The specific preparation steps are as follows.

[0084] S1. Raw Material Selection and Pretreatment: 40Cr alloy structural steel was selected as the raw material for the template fixer body, limit adjuster, and locking shims; 35CrMo alloy structural steel was selected as the raw material for the adjusting screw. All raw materials were sent to a heat treatment furnace for quenching and tempering pretreatment at 850℃ for 2.5 hours. After removal from the furnace, the materials were oil-quenched and cooled, followed by tempering at 540℃ for 2 hours and then naturally cooled to room temperature. After heat treatment, high-precision polishing equipment was used to polish the entire surface of the raw materials to remove surface oxide scale, burrs, and uneven defects, controlling the surface roughness to Ra≤1.6μm. The initial dimensional deviation of the pretreated raw materials was controlled within ±0.02mm, resulting in homogeneous, low-stress processed raw materials. This raw material selection and pretreatment process ensures that standard components are structurally stable and deformation-free under conventional support loads, and that the screw adjustment is smooth, wear-resistant, and durable.

[0085] S2, Machine Vision Positioning and Clamping: The pre-processed raw materials are precisely clamped onto a CNC precision cutting machine tool. A binocular high-definition machine vision acquisition module is activated, using a 25-megapixel binocular industrial camera with a ring-shaped shadowless light source. The acquisition frame rate is set to 45 frames / s to acquire full-domain 3D images of the clamping position, datum surface contour, and spatial offset of each raw material. Sub-pixel edge detection algorithms are used to extract the raw material contour datum points, constructing a unified 3D machining datum coordinate system. Clamping deviations are calibrated, and the final datum coordinate system positioning error is controlled within 0.01mm, ensuring uniform machining datum and accurate positioning for all components.

[0086] S3, Intelligent Precision Machining: Based on the established machining reference coordinate system, the CNC cutting equipment is started to perform staged cutting. In the rough cutting stage, the spindle speed is set to 1000 r / min and the cutting feed rate is 0.3 mm / r to quickly remove excess machining allowance from the raw material and complete the initial contour forming of the component. In the finish cutting stage, the process parameters are switched, and the spindle speed is set to 1800 r / min and the cutting feed rate is 0.1 mm / r to perform precision finishing and trimming on the component dimensions, threads, mating surfaces, and limit grooves. After machining, a semi-finished component is obtained, with the initial machining dimensional deviation controlled within ±0.05 mm, and the component surface free of tool marks, burrs, and dents.

[0087] S4, Intelligent Dynamic Compensation for Dimensional Errors: The machine vision acquisition module collects real-time data on the actual linear dimensions and X / Y axis form and position deviations of each semi-finished component, and substitutes this data into the intelligent dimensional compensation algorithm formula of this invention for error calculation. Algorithm parameter settings in this embodiment: Dimensional compensation correction coefficient. =0.95, Geometric Compensation Weighting Coefficient =0.90, equipment inherent error compensation value =0.01mm. (Standard design dimensions) Actual dimensions Biaxial form and position deviations , Substitute into the formula respectively and The linear dimension compensation and form and position error compensation of each component are accurately calculated. The CNC equipment dynamically corrects the cutting trajectory based on the calculated compensation, performs secondary precision finishing on the component, corrects residual machining errors, and finally controls the linear dimension deviation of the component within ±0.02mm and the form and position tolerance deviation ≤0.02mm.

[0088] S5 Intelligent Precision Assembly Positioning: Finished components are fed into the intelligent assembly station, and the machine vision alignment system is activated to collect real-time data on the assembly reference position, fit clearance, and alignment deviation of each component. Based on an intelligent assembly clearance compensation algorithm, a standard assembly clearance of 0.04mm is set. The fine-tuning amount of the assembly tooling is calculated in real time, and the X / Y / Z three-axis displacement of the assembly platform is adaptively adjusted to achieve precise alignment and automated locking assembly of the template holder body, adjusting screw, limit adjuster, and locking shims. The assembly alignment accuracy is 0.01mm, and after assembly, the fit clearance of each component is uniform, without offset or jamming.

[0089] S6. Finished Product Verification and Finalization: The assembled standard template fasteners undergo full-parameter quality verification. The test results show that the coaxiality deviation of the finished product assembly is 0.02mm, the relative displacement of the components under the rated support load of 8kN is 0.015mm, and the change in assembly gap after 500 repeated disassembly and assembly cycles is 0.008mm. All parameters meet the industry's high-precision safety standards. Qualified finished products are selected and finalized for packaging.

[0090] The standard template fixer prepared in this embodiment has a batch product assembly consistency deviation of ≤0.01mm, which is more than 60% higher than that of traditional process products. The product yield rate has increased from 85% to 99.5%. There are no loosening, shaking or deformation problems during construction and use, and the stability and safety of the support are greatly improved.

[0091] This invention provides a first specific application embodiment, designed to adapt to the high-precision processing and assembly technology of standard scaffolding formwork in conventional building construction, matching general-specification component parameters and algorithm compensation parameters. In this embodiment, the formwork fixing device has a total length of 120mm, an adjusting screw diameter of 12mm, and a limit adjuster inner diameter of 12mm, which are standard specifications for formwork fixing devices in the domestic construction industry, suitable for the support construction of standard 15mm and 18mm thick building formwork. The algorithm parameters are set with a processing compensation coefficient k=0.95, a form and position compensation weight coefficient η=0.90, and an inherent equipment error compensation value δ=0.01mm. This set of parameters has been optimized through extensive testing to accurately adapt to the cutting deformation law, equipment error characteristics, and assembly fit requirements of standard-specification small components. The standard formwork fixing device prepared by the process of this embodiment can control the processing dimensional deviation within ±0.02mm, with uniform and stable assembly gaps and a coaxiality deviation ≤0.02mm, fully meeting the precision and safety requirements of conventional building construction and enabling mass standardized production.

[0092] Application Example 2: High-precision intelligent machining and assembly process for heavy-duty scaffolding formwork fixing devices

[0093] This application example 2 describes the manufacturing process of a thickened scaffolding formwork fixing device adapted for high-rise buildings, large-span structures, and heavy-duty formwork support. It is compatible with 25mm thickened heavy-duty building formwork and is suitable for construction scenarios with high support loads and large concrete lateral pressure. The component specifications are: a total length of 150mm for the main body of the formwork fixing device, a diameter of 16mm for the adjusting screw, an inner diameter of 16mm for the limit adjuster, and a thickness of 4mm for the locking shim. In view of the characteristics of large-specification components with large processing errors, difficult control of shape and position deviations, and high assembly accuracy requirements, the algorithm and process parameters are optimized. The specific manufacturing steps are as follows.

[0094] S1. Raw Material Selection and Pretreatment: Adhering to differentiated raw material selection standards, the main body of the template fixator, limit adjuster, and locking gasket are made of 40Cr alloy structural steel to ensure structural rigidity and extrusion resistance under static high loads. The adjusting screw is made of 35CrMo alloy structural steel to improve the torsional resistance, fatigue resistance, and high wear resistance of the large-diameter screw. The raw material heat treatment pretreatment parameters are optimized as follows: heating temperature 860℃, holding time 3h, oil quenching, tempering at 550℃ for 2.5h, to fully refine the internal metallographic structure of large-size raw materials, completely eliminate residual internal stresses that are easily retained during the processing of large-size components, and avoid subsequent deformation. After surface polishing and grinding, the surface roughness is controlled to Ra≤1.6μm, and the initial dimensional deviation of the raw material is strictly controlled within ±0.02mm to ensure the basic accuracy of large-size components.

[0095] S2, Machine Vision Positioning and Clamping: Adopting the same binocular high-definition machine vision acquisition module, and considering the characteristics of large-sized components with large outline dimensions and high risk of clamping offset, the acquisition frame rate is increased to 50 frames / s. The sub-pixel edge detection algorithm is used to extract the reference outline of large-sized components in the whole domain, and a high-precision three-dimensional processing reference coordinate system is constructed to accurately calibrate the clamping tilt and offset of large components. The reference positioning error is ≤0.01mm, ensuring the accuracy and uniformity of the processing reference for large-sized components.

[0096] S3, Intelligent Precision Machining: Adapted to the machining characteristics of large-sized components, optimizing graded cutting parameters. In the roughing stage, the spindle speed is 900 r / min, and the cutting feed is 0.35 mm / r, efficiently removing excess material from large-sized raw materials and completing the basic contour shaping of the component; in the finishing stage, the spindle speed is 1900 r / min, and the cutting feed is 0.12 mm / r, performing precision machining on large-diameter screw threads, large-size retainer mating surfaces, and limiting structures, accurately controlling the initial machining error of large-sized components, with the dimensional deviation of the semi-finished product ≤0.05 mm after machining, and without cutting vibration deformation or surface defects.

[0097] S4, AI Dynamic Compensation for Dimensional Errors: Addressing the characteristics of large-sized components with large cutting deformation, significant equipment error impact, and complex form and position deviations, the AI ​​compensation algorithm parameters are optimized, and dimensional compensation correction coefficients are set. =0.97, Geometric Compensation Weighting Coefficient =0.93, equipment inherent error compensation value =0.012mm. The actual linear dimensions and bidirectional form and position deviations of large-sized components are acquired in real time using machine vision. These are then input into two sets of intelligent compensation calculation formulas to accurately calculate the dimensional and form and position error compensation amounts for the large-sized components. The CNC system adaptively corrects the large-trajectory cutting path based on the compensation data, completing a secondary finishing process. This completely eliminates deformation errors and accumulated equipment errors during the machining of large components, ultimately controlling the linear dimensional deviation of heavy-duty components within ±0.015mm and the form and position tolerance deviation ≤0.018mm. This precision surpasses that of standard components, meeting the requirements for heavy-duty, high-precision applications.

[0098] S5 Intelligent Precision Assembly Positioning: Addressing the higher precision requirements for assembly gaps in heavy-duty, large-sized components, it utilizes an AI assembly gap compensation algorithm to set a standard assembly gap of 0.05mm, adapting to the adjustment needs of large-diameter screws and heavy-duty stress conditions. The machine vision system monitors the alignment deviation and gap distribution uniformity of large components in real time, dynamically adjusting the three-axis fine-tuning displacement of the assembly fixture to achieve deviation-free, precise alignment and uniform gap assembly of large-sized components. The assembly alignment accuracy is ≤0.01mm, resulting in tightly fitted components with uniform stress, smooth adjustment, and no issues of eccentricity or uneven gaps.

[0099] S6. Finished Product Verification and Approval: High-load special verification and testing were conducted on the heavy-duty finished products. The test results showed that the coaxiality deviation of the finished product assembly was 0.025mm, the relative displacement of the components under the rated heavy load of 15kN was 0.018mm, and the change in assembly gap after 500 repeated disassembly and assembly cycles was 0.009mm. The structural stability, fatigue resistance, and locking performance all met the safety standards for heavy-duty construction. The qualified finished products were approved and put into storage.

[0100] The heavy-duty formwork fixture prepared in Example 2 of this application solves the industry problems of severe deformation during processing, eccentric assembly, and easy deformation and loosening under heavy loads of traditional large-size fixtures. The batch products have significantly improved precision consistency, structural stability, and heavy-load safety, and are fully adapted to the heavy-duty formwork support construction needs of high-rise buildings and large-span projects.

[0101] Application Example 2 describes the processing and assembly technology of thickened scaffolding formwork fixing devices adapted to heavy-duty construction scenarios. It optimizes algorithms and process parameters for large-sized, high-load components to suit special construction conditions. In this application example, the formwork fixing device has a total body length of 150mm, an adjusting screw diameter of 16mm, and a limit adjuster inner diameter of 16mm. It is suitable for 20mm and 25mm thickened heavy-duty building formwork and high-rise and large-span building construction scenarios with high support loads; the processing compensation coefficient is optimized. =0.97, Geometric Compensation Weight Coefficient =0.93, equipment inherent error compensation value =0.012mm, addressing the challenges of large-size component cutting deformation, more significant equipment error impact, and high difficulty in controlling form and position deviations, this embodiment improves the accuracy of the compensation coefficient, strengthens error correction, and precisely adapts to the processing characteristics of large-size components. This embodiment specifically solves the problems of large processing errors, high assembly difficulty, and poor stress stability in heavy-duty large-size fixtures. The resulting product exhibits superior load-bearing capacity, structural stability, and assembly accuracy compared to conventional products.

[0102] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A high-precision intelligent machining and assembly method for scaffolding template fixing devices, characterized in that, include: S1, Raw material screening and pretreatment, including: screening raw materials for each core component of the scaffold template fixer, performing heat treatment and pretreatment on the selected raw materials to obtain homogenized processed raw materials; the core components include the template fixer body, adjusting screw, limit adjuster and locking gasket; S2, machine vision positioning and clamping, includes: clamping the pre-processed raw materials of each component to the CNC cutting processing equipment, and performing full-domain image acquisition of the shape of the raw materials, clamping position and datum plane deviation based on machine vision acquisition method to construct the component processing datum coordinate system; S3, intelligent precision cutting and machining, includes: based on the constructed machining reference coordinate system, using CNC cutting equipment to perform rough cutting, fine cutting and trimming and grinding of the raw materials for the preparation of the core components in an integrated process, to initially form semi-finished products of each core component; S4, intelligent dynamic compensation for dimensional errors, includes: real-time acquisition of actual processing dimensions and geometric tolerance data of semi-finished components through machine vision, inputting into the preset intelligent dimensional compensation algorithm calculation formula, calculating the processing dimension compensation amount and geometric error correction amount of each component, dynamically correcting the CNC cutting trajectory, completing the secondary fine processing of the components, and obtaining high-precision finished components. S5, intelligent and precise assembly positioning, includes: aligning each finished component with the assembly benchmark based on machine vision alignment technology, calculating the assembly fine-tuning amount by combining intelligent assembly gap compensation algorithm, adaptively adjusting the assembly position of each component, and completing the automated assembly of the template fixer. S6, Finished product verification and finalization, including: verifying the dimensional accuracy, assembly coaxiality, and locking stability of the assembled template fixture, screening qualified finished products, and completing the overall preparation.

2. The high-precision intelligent machining and assembly method for a scaffolding template fixer according to claim 1, characterized in that, In step S1, the raw materials for the template fixing body, the limit adjuster and the locking gasket are 40Cr alloy structural steel, and the raw materials for the adjusting screw are 35CrMo alloy structural steel.

3. The high-precision intelligent machining and assembly method for a scaffolding template fixer according to claim 2, characterized in that, The raw material pretreatment includes: heating the raw material to 830~870℃ and holding it for 2~3 hours, then oil quenching it, followed by tempering at 520~560℃ and holding it for 1.5~2.5 hours. After heat treatment, the surface roughness is polished to Ra≤1.6μm, and the dimensional deviation is controlled within ±0.02mm.

4. The high-precision intelligent machining and assembly method for a scaffolding template fixer according to claim 3, characterized in that, In step S2, the machine vision acquisition method includes: using a binocular high-definition industrial camera in conjunction with a ring-shaped shadowless light source to perform machine vision acquisition; then, extracting the raw material contour reference points through a sub-pixel edge detection algorithm; in this embodiment, the image acquisition resolution is not less than 25 million pixels, the acquisition frame rate is 30~60 frames / s, and the reference coordinate system positioning error is ≤0.01mm.

5. The high-precision intelligent machining and assembly method for a scaffolding template fixer according to claim 4, characterized in that, In step S3, the rough cutting feed rate is 0.2~0.4 mm / r and the spindle speed is 800~1200 r / min; the finish cutting feed rate is 0.05~0.15 mm / r and the spindle speed is 1500~2000 r / min. After finish cutting, the initial dimensional deviation of the component is controlled within ±0.05 mm.

6. The high-precision intelligent machining and assembly method for a scaffolding template fixer according to claim 5, characterized in that, In step S4, the intelligent size compensation algorithm includes the component linear size compensation calculation formula as shown in equation (1): （1）； in, The total linear dimension compensation of the component, in mm, is used to characterize the axial / radial dimension correction displacement that needs to be performed in the next finishing process of the CNC cutting equipment; The machine tool CNC system reads this value in real time to automatically update the tool path coordinates, which is the target output value of the entire compensation logic. is a linear dimension compensation correction coefficient, a dimensionless coefficient, used to offset the nonlinear deviation correction weighting coefficient caused by tool elastic rebound, cutting thermal expansion and contraction and machine tool servo feed inertia during CNC cutting, with a value of 0.92~0.98; It represents the standard design linear dimension of the component, in mm, and is used to characterize the theoretical standard dimensions marked on the drawings of scaffolding formwork fixing parts, including all linear reference dimensions such as the outer diameter of the screw, the total length of the fixing body, the inner diameter of the adjuster and the thickness of the shim; The linear dimension of the component measured by machine vision, in mm, is used to characterize the real linear dimension of the semi-finished component after it has been formed, which is collected in real time by a binocular industrial camera and a sub-pixel edge detection algorithm. It includes real-time detection data of the actual diameter of the screw after processing, the actual total length of the fixture body, and the actual inner hole size of the adjuster. It is a dynamic variable. This is the inherent system error compensation value for the machine tool equipment, in mm, ranging from 0.005 to 0.015 mm.

7. The high-precision intelligent machining and assembly method for a scaffolding template fixing device according to claim 6, characterized in that, The formula for calculating the form and position tolerance deviation compensation is shown in equation (2): （2）； in, The total compensation amount for the comprehensive form and position error of the component is in mm. It is the form and position correction value output by the algorithm, which is used by CNC equipment to correct the composite form and position deviation. This is the form and position compensation weight coefficient, dimensionless, with a value of 0.85~0.95, used to balance the problem of excessive compensation caused by cutting deformation and tooling clamping offset; The measured form and position deviation of the component in the X-axis direction, acquired by machine vision, is in mm and reflects the offset of the component's lateral contour and reference surface. The measured form and position deviation of the component in the Y-axis direction, acquired by machine vision, is in mm, reflecting the longitudinal contour and datum plane offset of the component.

8. The high-precision intelligent machining and assembly method for a scaffolding template fixer according to claim 7, characterized in that, In step S5, the AI ​​assembly gap compensation algorithm sets the assembly reference gap according to the tolerance level of each component. The standard assembly gap is controlled at 0.02~0.06mm. The assembly misalignment and gap unevenness of the components are collected by visual alignment, and the displacement of the assembly tooling is adaptively adjusted.

9. The high-precision intelligent machining and assembly method for a scaffolding template fixer according to claim 8, characterized in that, In step S6, the finished product verification includes dimensional accuracy verification, assembly coaxiality verification, and load stability verification.

10. A high-precision intelligent machining and assembly system for scaffolding template fixing devices, used to implement the method described in any one of claims 1-9, characterized in that, include: The raw material pretreatment module (101) is used for raw material screening and pretreatment, including: screening raw materials for each core component of the scaffold template fixer, performing heat treatment and raw material pretreatment on the selected raw materials to obtain homogenized processed raw materials; the core components include the template fixer body, adjusting screw, limit adjuster and locking gasket; The machine vision clamping and positioning module (102) is used for machine vision positioning and clamping, including: clamping the pre-processed component raw materials to the CNC cutting processing equipment, acquiring full-domain images of the raw material shape, clamping position and datum plane deviation based on the machine vision acquisition method, and constructing a component processing datum coordinate system. The intelligent precision cutting module (103) is used for intelligent precision cutting processing, including: based on the constructed processing reference coordinate system, using CNC cutting equipment to perform rough cutting, fine cutting and trimming and grinding of the raw materials for the preparation of the core components in an integrated process, and initially forming semi-finished products of each core component; The intelligent dimension error dynamic compensation module (104) is used for intelligent dimension error dynamic compensation, including: real-time acquisition of the actual processing dimensions and form and position tolerance data of each component semi-finished product through machine vision, substituting them into the preset intelligent dimension compensation algorithm calculation formula, calculating the processing dimension compensation amount and form and position error correction amount of each component, dynamically correcting the CNC cutting trajectory, completing the secondary fine processing of the component, and obtaining a high-precision finished component. The intelligent precision assembly module (105) is used for intelligent precision assembly positioning, including: aligning each finished component with the assembly reference based on machine vision alignment technology, calculating the assembly fine adjustment amount by combining intelligent assembly gap compensation algorithm, adaptively adjusting the assembly position of each component, and completing the automated assembly of the template fixer. The finished product verification and finalization module (106) is used for finished product verification and finalization, including: verifying the dimensional accuracy, assembly coaxiality, and locking stability of the assembled template fixture, screening qualified finished products, and completing the overall preparation.

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