Dimensional Errors Research Articles

Optimization design of assembly sequence for the proton exchange membrane fuel cell stacks with dimensional errors

Proton exchange membrane fuel cell (PEMFC) stacks are assembled by numerous bipolar plates (BPPs) and membrane electrode assemblies (MEAs). Component manufacturing errors are inevitably caused during the production process and brought into the stack, affecting the mechanical consistency of every cell. Aiming to improve the uniformity of the assembled stack effectively, this paper establishes a novel assembly deviation model based on the asymptotic homogenization (AH) method and proposes an assembly sequence optimization approach for the stack with dimensional errors using the genetic algorithm. The results reveal that dimensional errors significantly affect the equivalent mechanical properties of the stack. Reasonable matching of components with different types of dimensional errors is an effective method to avoid the cumulative negative impacts on the mechanical behavior of the stack. Statistically, the optimization of the assembly sequence can improve contact pressure uniformity in a 10-cell stack by more than 9.00%, while reducing the maximum contact pressure by 15.55%. This methodology is also applicable to improving the assembly uniformity of other stacked structures.

Reliability calculation and error analysis of VSV adjustment mechanism motion accuracy

The adjustable stator vane (VSV) adjustment mechanism of an aircraft engine was taken as the research object, and a kinematic model with kinematic pair clearance was established. Considering the randomness of factors such as drive error, dimensional error and assembly clearance, a VSV adjustment mechanism motion accuracy reliability model considering the failure correlation of each stator blade motion accuracy was established. The BP neural network proxy model was introduced, and the Monte Carlo method was used to calculate the reliability of the mechanism, and the variation law of the reliability with the number of stator blades and the failure threshold was given. By comparing with the system model with independent unit failure assumptions and the fully correlated system model, the rationality of the mechanism system motion accuracy reliability model considering failure correlation was verified. Finally, a method for estimating the reliability error of the complex system caused by the proxy model error was proposed, and it was proved that the reliability calculation result of the VSV adjustment mechanism is accurate and reliable.

Compressive behavior of additively manufactured lightweight structures: Infill density optimization based on energy absorption diagrams

The use of 3D-printed components as load-bearing structures is widely studied, while their use as energy absorbers constitutes a gap in the additive manufacturing (AM) field. Most of the reported works address the compressive behavior of AM parts up to low strains (<15%), thus omitting many important properties. Therefore, this paper presents the compression characterization of Polylactic Acid (PLA) samples with different infill densities-IDs (10–100% range, with a step of 10%) fabricated by material extrusion (MEX) AM process. The physical (dimensional accuracy, printing time and sample mass) and mechanical (elastic, strength, strain and energy absorption) properties are determined and discussed in detail, along with the failure mechanisms that occur in the samples. Moreover, an ID optimization is performed based on the energy absorption diagrams (EAD), a unique approach in the AM field. The highest values of compressive strength (81 MPa) and modulus (1306.6 MPa) are found for 90%-ID, over 2.2% higher than those obtained for 100%-ID. On the other hand, based on three different approaches, EAD identified 30%-ID as optimal. The 30%-ID samples absorb the largest amount of energy (12 MJ/m3) at the lowest (ideal) peak stress value (23.57 MPa), which is 69.1% lower than that for 100%-ID. At low IDs (<30%), the samples exhibit a brittle behavior, changing to a ductile one with the increase of ID (≥40%). The MEX-printed PLA samples show a dimensional relative error below 0.15%, and the optimization process reduces the amount of filament material by 54.3% and the printing time by 42.7%.

Study on hybrid 3D printing and milling process for customized polyether-ether-ketone components.

Polyether-ether-ketone (PEEK) has been widely applied in various fields due to its excellent mechanical properties and biocompatibility. The efficient and high-quality customized manufacturing of PEEK components are investigated in this study by the hybrid 3D printing and milling process. At first, the alternating hybrid process is selected and verified by comparing two typical hybrid process categories and conducting experiments, respectively. Second, a set of procedures are designed to automate the engineering application of the hybrid process trying to avoid the disadvantages of manual programing. Then, considering the tool length and possible interferences during the hybrid process, a model segmentation algorithm, namely, the exchange principle of avoiding interference (EPAI) is proposed. Based on the introduced EPAI and the programing language Python, the additive and subtractive hybrid manufacturing (ASHM) data processing procedure is proposed and realized by post-processing of the conventional 3D printing codes. Finally, the feasibility experiments have been conducted. The experimental results verify the hybrid manufacturing process in the fabrication of parts with complex internal features. The surface roughness Ra and dimensional error L of the parts have been reduced by 75.5% and 85.2%, respectively, while the shear strength τ has been increased by 14.1%. Compared with conventional milling process, the material consumption is reduced by 48.7%.

Tea Tree Oil Effect on Dimensional Change and Detail Reproduction of Addition Silicon Impression Material

Objective: Impression materials are thought to be the one of the primary sources of cross infection between patients and dentists. However, it was discovered that disinfection of the impression is not conducted routinely in ordinary dental treatment. Disinfection of addition silicon impression, whether by immersion or spray should not produce dimensional or detail errors. The purpose of this study was to examine the immersion of addition silicon impression material in tea tree oil and its influence on dimensional stability and detail reproduction of the addition silicon imprint material. Materials and Methods: This study employed a total of 120 heavy- and light-body addition silicon impression material specimens. The specimens were randomly sorted and immersed into six groups. These test groups where four concentrations of TTO (0.25%, 0.5%, 0.75%, and 1%), and the other two groups were distilled water (negative control) and 2% glutaraldehyde (positive control). All specimens were immersed for 10 minutes in the testing solutions. Each of the six groups had 20 specimens separated into two subgroups. Results: There were no statistically significant differences in linear dimensional changes and detail reproduction among all test groups. Conclusion: The addition silicon impression material may be safely submerged in TTO for 10 minutes to disinfect it without impairing its dimensional correctness or detail replication of the impression.

A method for temperature sensor and model selection for machine tool thermal error modelling using ANFIS and ANN

AbstractThermal errors account for a significant part of the dimensional errors of components produced by precision machine tools. These errors are commonly compensated using predictions from temperature-based empirical models. The accuracy and robustness of these predictions are affected by the locations of temperature sensors used to obtain the model’s input data. Methods for sensor selection found in literature are often difficult to replicate and automate because they require tuning of several hyperparameters. This work presents a sensor and model selection approach that uses proper orthogonal decomposition (POD) and QR pivoting to select a subset of sensors that have been preinstalled in the machine tool as possible model inputs. These sensors are then sorted according to their correlation with the thermal error being modelled. The final set of inputs and thermal error model structure is chosen using Bayesian Information Criterion (BIC) to limit model overfitting. The approach was tested by modelling the Y-axis thermal error measured from air-cutting experiments performed under different spindle speeds and feed rates. This enabled determination of the structure and the choice inputs for Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) thermal error models. The accuracy of these models was compared to that of models trained using inputs selected by conventional approaches: the least absolute shrinkage and selection operator (LASSO), fuzzy c-means clustering (FCM), and principal component regression (PCR). The presented approach had better or comparable results to the conventional approaches while using fewer inputs. The presented approach is also well suited for automation compared to conventional approaches that require expert input.

Dimensional Error Analysis of Cortical Screw during Threading of Magnesium AZ31

Developments in manufacturing to achieve large productivity and high-quality products in the modern era have necessitated the need of using suitable methods. Taguchi or robust design method has been implemented to optimise a process in a single response. This experiment aims to analyse the cutting parameters of geometrical errors of threads. Machining trials were conducted on the diameter of the workpiece (10, 14 and 18 mm), depth of cut (0.23, 0.3067 and 0.46 mm) and spindle speed (212, 318 and 424 rpm). Three responses, namely, pitch, height threads and angle thread error, were investigated. The external thread selected is metric M1,5 with three levels of three factors. Results show that spindle speed was the most significant factor that affected pitch error and height thread error. Depth of cut and diameter of the workpiece significantly affected thread angle error. The optimal condition to produce low thread angle error included machining at level 1 diameter of the tool, level 3 depth of cut of and level 3 spindle speed.

Numerical simulation of the first layer printing in electric field-assisted fused filament fabrication for robust unconventionally oriented additive manufacturing

Fused filament fabrication (FFF) stands as a prominent thermoplastic additive manufacturing technique that has a wide range of potential applications. However, the conventional FFF process encounters limitations in unconventional environments, particularly in environments devoid of gravity, such as in extraterrestrial environments or in limited manufacturing conditions where maintaining consistent nozzle standoff distance becomes a challenging task. Fluctuations in nozzle standoff distance lead to inadequate material adhesion and dimensional errors, which results in a high failure rate and prolonged completion time. Thus, the conventional FFF process requires continuous monitoring or human intervention to ensure successful operation. To overcome the shortcomings of the traditional FFF process, an electric field-assisted FFF (E-FFF) process can be utilized to reduce the need for constant supervision and enable reliable production of parts. The E-FFF process integrates a high-voltage power supply unit with a standard FFF printer, generating an electrostatic force between the nozzle and the build plate. The generated electrostatic force can effectively attract the extruded molten polymer toward the build plate, even at a larger z-height of up to 1200 μm in unconventional build platform orientations such as vertical or reverse printing orientation. In this paper, finite element analysis is used to simulate the FFF and E-FFF process. The simulation focuses on the adhesion between extruded material and build plate with extended nozzle standoff distances and predicts the minimum voltage required to achieve a good material adhesion in unconventional orientations for the E-FFF process. The simulation model demonstrated its efficacy by estimating the minimum required voltage levels necessary for successful printing within the E-FFF process across a range of nozzle standoff distances in unconventional orientations.

MultiNet 2.0: A lightweight attention-based deep learning network for stenosis measurement in carotid ultrasound scans and cardiovascular risk assessment

BackgroundCardiovascular diseases (CVD) cause 19 million fatalities each year and cost nations billions of dollars. Surrogate biomarkers are established methods for CVD risk stratification; however, manual inspection is costly, cumbersome, and error-prone. The contemporary artificial intelligence (AI) tools for segmentation and risk prediction, including older deep learning (DL) networks employ simple merge connections which may result in semantic loss of information and hence low in accuracy. MethodologyWe hypothesize that DL networks enhanced with attention mechanisms can do better segmentation than older DL models. The attention mechanism can concentrate on relevant features aiding the model in better understanding and interpreting images. This study proposes MultiNet 2.0 (AtheroPoint, Roseville, CA, USA), two attention networks have been used to segment the lumen from common carotid artery (CCA) ultrasound images and predict CVD risks. ResultsThe database consisted of 407 ultrasound CCA images of both the left and right sides taken from 204 patients. Two experts were hired to delineate borders on the 407 images, generating two ground truths (GT1 and GT2). The results were far better than contemporary models. The lumen dimension (LD) error for GT1 and GT2 were 0.13±0.08 and 0.16±0.07 mm, respectively, the best in market. The AUC for low, moderate and high-risk patients’ detection from stenosis data for GT1 were 0.88, 0.98, and 1.00 respectively. Similarly, for GT2, the AUC values for low, moderate, and high-risk patient detection were 0.93, 0.97, and 1.00, respectively.The system can be fully adopted for clinical practice in AtheroEdge™ model by AtheroPoint, Roseville, CA, USA.

An experimental study of ultrasonic-knife cutting for a woven carbon fiber preform by an industrial robot

This study investigates the effect of the cutting process parameters on the cutting forces and the quality parameters of a woven carbon fiber preform during robotic ultrasonic-knife cutting. An ultrasonic cutting device, with a power of 1200 W, a frequency of 24 kHz, and an amplitude of 60 µm, mounted on the end effector of a six-axis degree of freedom industrial robot to make linear cuts. A three-level factorial experimental design was used to examine the effect of the feed rate (1 m/min to 5 m/min) and the knife’s attack angle (45° to 75°) on the cutting forces, the dimensional accuracy of the machined preform coupons, and the damage on the machined preform edges. The cutting force analysis results show that the increasing feed rate resulted in increasing feed force and thrust force. However, the increase of the attack angle increases the feed force but decreases the thrust force. The average width and damage of the ultrasonic knife cut preform coupons are highly related to the process conditions. The combination of the low feed rate, 1 m/min, and the low attack angle, 45°, resulted in dimensional errors ranging from 253 μm to 365 μm oversized from the programmed 15.0 mm width with no damage. When the feed became 3 m/min and 5 m/min at the attack angle of 75°, the preform coupons’ dimensional accuracy and damage formation worsened. In these conditions, the ultrasonic knife attached to the industrial robot arm could not cut the preform plate effectively, so the tows on the preform were unevenly cut or dislodged.

Studying Performance of Wire Electrical Discharge Machining for Multi-pass Cutting Strategy on the Tool Steel SKD 61 Using Recommended Parameters of Machine

This research aims to study the machining efficiency of wire electrical discharge machining for multi-pass cutting. The recommended parameters of the machine are employed for cutting test tool steel grade SKD 61 with the thickness of 40 mm. The brass wire electrode with the diameter of 0.25 mm was used in the cutting test. The recommended parameters for multi-pass tool path cutting consisted of 4 operations. The 1st cut was defined as a full cut that required the removal of a large amount of workpiece material, with the cutting speed set at 2.60 mm/min. The 2nd, 3rd, and 4th cuts involve cutting on the edge of the workpiece to remove only a small amount of work material, with the cutting speed set at 3.20, 3.60, and 6.80 mm/min, respectively. The experimental test showed that the machining performance of the cutting speed for the 1st cut was lower than the expected value by approximately 18.01 percent. The 2nd, 3rd, and 4th cuts found that the cutting speed higher than expected value by approximately 104.34, 128.89 and 4.12 percentage for each cutting pass, respectively. In addition, the surface roughness and dimensional error of the machined part decrease as the number of cutting passes increases. The difference between the expected values and the experimental results is very useful in planning decisions and quality control for cutting workpieces by the process of wire electrical discharge machining.

Improved Quantification of MicroPET/CT Imaging Using CT-derived Scaling Factors.

Combined micro-PET/CT scanners are widely employed to investigate models of brain disorders in rodents using PET-based coregistration. We examined if CT-based coregistration could improve estimates of brain dimensions and consequently estimates of nondisplaceable binding potential (BPND) in rodent PET studies. PET and CT scans were acquired on 5 female and 5 male CD-1 mice with 3-[18F]fluoro-5-(2-pyridinylethynyl)benzonitrile ([18F]FPEB), a radiotracer for the metabotropic glutamate receptor subtype 5 (mGluR5). In the proposed PET/CT (PTCT) approach, the tracer-specific standard volume was dimension-customized to each animal using the scaling factors from CT-to-standard CT coregistration to simplify PET-to-standard PET coregistration (i.e., 3 CT- and 6 PET-derived parameters). For comparison, conventional PET-based coregistration was performed with 9 (PT9) or 12 (PT12) parameters. PET frames were transferred to the standard space by the three approaches (PTCT, PT9, and PT12) to obtain regional time-activity curves (TACs) and BPND in 14 standard volumes of interest (VOIs). Lastly, CT images of the animals were transferred to the standard space by CT-based parameters from PTCT and with the scaling factors replaced with those from PET-based PT9 to evaluate agreement of the skull to the standard CT. The PET-based approaches showed various degrees of underestimations of scaling factors in the posterior-anterior-direction compared to PTCT, which resulted in negatively proportional overestimation of radioactivity in the cerebellum (reference region) up to 20%, and proportional, more prominent underestimation of BPND in target regions down to -50%. The skulls of individual animals agreed with the standard skull for scaling factors from PTCT but not for the scaling factors from PT9, which suggested inaccuracy of the latter. The results indicated that conventional PET-based coregistration approaches could yield biased estimates of BPND in proportion to errors of brain dimensions when applied to tracers for which the cerebellum serves as reference region. The proposed PTCT provides evidence of a quantitative improvement over PET-based approaches for brain studies using micro-PET/CT scanners.

High-accuracy 3D reconstruction of step edge with ray aliasing based on projective parallel single-pixel imaging

Optical 3D measurement technology especially fringe projection profilometry (FPP) is widely utilized in industrial production. However, invalid mixed phases are common when measuring step edges in industrial parts which leads to outliers in reconstructed point cloud and ultimately causes reduction of valid point cloud and dimensional measurement errors. This paper first analyzes reasons for failure of step edge measurement in FPP and illustrates feasibility of parallel single-pixel imaging (PSI) applied to eliminate ray aliasing of step edge. Further for a good balance of accuracy and efficiency, we propose an optimized projective parallel single-pixel imaging (pPSI) method combing with edge detection in wrapped phase generated by pPSI patterns and adaptive bi-direction projection strategy to accurately identify direct correspondence matching points of step edge. Experimental results verified that the proposed method can achieve high accuracy of 3D reconstruction on step edge while taking less measurement time.

Dimension compensation of printed master molds by a desktop LCD 3D printer for high-precision microfluidic applications.

Recent advances in low-cost liquid crystal display (LCD) 3D printing have popularized its use in creating microfluidic master molds and complete devices. However, the quality and precision of these fabrications often fall short of the rigorous standards required for advanced microfluidic applications. This study introduces a novel approach to enhance the dimensional accuracy of microchannels produced using a desktop LCD 3D printer. We propose a method for dimension compensation, optimize the printing parameters, and provide a straightforward post-treatment technique to ensure high-quality curing of polydimethylsiloxane (PDMS) in master molds made from photosensitive resin. Our investigation assesses the precision of 3D printing across three different scales of square cross-section microchannels by measuring their widths and heights, leading to the determination of optimal printing parameters that minimize dimensional errors. The dimensional errors are further reduced by introducing a series of dimension compensation factors, which correct the nominal dimensions of the microchannels by using the compensation factors in 3D printing. The dimensional accuracy is significantly improved after compensation even in fabricating complex microchannels of triangular cross-sections. Finally, a spiral channel of trapezoidal-like cross-section with tilted edges is fabricated for microfluidic application, and highly efficient particle separation is realized in the channel. The proposed method provides new insights for utilizing desktop LCD 3D printers to achieve high-accuracy microstructures necessary for advanced microfluidic applications.

Repairability and effectiveness in direct energy deposition of 316L stainless steel grooves: A comparative study on varying laser strategy

Direct energy deposition (DED) has gained attention in the field of metal repair because of its ability to integrate additively deposited material with preexisting components. Having the beneficial aspects of depositing right material, DED is evaluated the best option for repair deposition. Machining grooves in damaged components is the common first to repair, which is necessary to remove cracked or damaged material and provide suitable geometry for deposition. Preprocessing as grooving is commonly applied to prevent the propagation cracks or tears. However, challenges such as dimensional error and crack induced by residual stress emerge owning to complex interactions between the DED parameters and groove geometry during repair deposition. This study investigates effective deposition strategies for repairing 316L stainless steel grooves with wall-angles of 90° and 135° by exploring the relationship between the repair integrity and parameters such as groove angles, laser energy input, and powder supply, all with reference to the underlying mechanism of metallurgical bonding in angled areas. Based thereon, a modified effective repair strategy is proposed, using enhanced dual laser power to address bonding issues in the angled areas. Samples repaired using this method exhibit a defect-free groove–deposit interface and a continuous columnar-grained microstructure with abrupt changes in grain size between the repaired area and groove. This microstructural heterogeneity results in an exceptional combination of strength and ductility when compared to the base material and samples repaired using a single laser strategy. The findings of this study provide insights into the optimization of DED strategies for achieving robust metallurgical bonding within grooves, thereby advancing technological maturity in the field of metal repair.

Analysis of precision loss of double-nut ball screw considering dimensional error and preload degradation

Abstract A ball screw is a critical drive component capable of converting force into motion, with widespread applications in the high-precision mechanisms of various machine tools. The degradation in precision attributed to ball screw wear significantly impacts machine tool accuracy. Current ball screw wear models lack consideration for the ramifications of radial forces and ball size errors.This study introduces a pioneering approach by formulating a coupled model that integrates screw wear, ball wear, and preload degradation. Through rigorous analysis, we discern the multifaceted influences of axial force, radial force, preload adjustments, and ball size precision on the positional accuracy of ball screws.By elucidating the intricate interplay among these parameters, this study offers crucial theoretical insights into the selection and optimization of ball screws, thereby fostering advancements in machine tool design and performance.

A prediction method for the backlash error of robot precision reducers based on optimal assembly

Abstract The kinematic accuracy and service life of robot precision reducers are directly affected by their assembly accuracy, and the backlash error is one of the most important performance indicators for evaluating the assembly accuracy. A prediction method for the backlash error of robot precision reducers is proposed, which can quickly and accurately calculate the backlash error of the optimal assembly reducers. Based on the optimal assembly process, the sensitive relationship between the errors of the crucial components and the assembly accuracy of the reducer is analysed, and the crucial errors affecting the backlash error are identified. The cumulative backlash of the crucial error along the dimensional chain is clarified, and the corresponding relationship between the backlash and the backlash error is determined. On this basis, backlash prediction models are established for the involute planetary gear and cycloidal-pin gear transmission parts, respectively. The mapping relationship between dimensional errors and backlash is also derived. Furthermore, the backlash error of the reducer is obtained by calculating the backlash error caused by each error term of the high-speed stage and low-speed stage transmission parts. Finally, the effectiveness and correctness of the prediction method are verified by the optimal assembly of RV reducers and the measurement of the backlash error. The theory and method proposed in this paper can quickly and accurately predict the backlash error of the reducer for robots, effectively predict the assembly accuracy of the selective reducers, significantly improve the assembly efficiency of the reducers, and then provide theoretical guidance and technical support for enhancing the assembly quality and kinematic accuracy of robot reducers.

Artificial neural networks-based modelling of effects of cryogenic electrode treatment, nano-powder, and surfactant-mixed dielectrics on wear performance and dimensional errors on superalloy machining

In the present era dominated by Industry 4.0, the digital transformation and intelligent management of industrial systems is significantly important to enhance efficiency, quality, and the effective use of resources. This underscores the need for a framework that goes beyond merely boosting productivity and work quality, aiming for a net-zero impact from industrial activities. This research introduces a comprehensive and adaptable analytical framework intended to bridge existing gaps in research and technology within the manufacturing sector. It encompasses the essential stages of using artificial intelligence (AI) for modelling and optimizing manufacturing systems. The effectiveness of the proposed AI framework is evaluated through a case study on electric discharge machining (EDM), concentrating on optimizing the electrode wear rate (EWR) and overcut (OC) for aerospace alloy Inconel 617. Utilizing a comprehensive design of experiments, the process modelling through an artificial neural network (ANN) is carried out, accompanied by careful fine-tuning of hyperparameters throughout the training process. The trained models are further assessed using an external validation (Valext) dataset. The results of the sensitivity analysis indicated that the surfactant concentration (Sc) has the highest level of influence, accounting for 52.41% of the observed influence on the EWR, followed by the powder concentration (Cp) with a contribution of 33.14%, and the treatment variable with a contribution of 14.43%. Regarding OC, Sc holds the highest percentage significance at 72.67%, followed by Cp at 21.25%, and treatment at 6.06%. Additionally, parametric optimization (PO) shows that EWR and OC overcome experimental data by 47.05% and 85.00%, respectively, showcasing successful performance optimization with potential applications across diverse manufacturing systems.

Coordinate system setting for post-machining of impeller shape by wire arc DED and evaluation of processing efficiency

Wire arc additive manufacturing (WAAM) is a direct energy deposition (DED) process that uses arc welding. It is a method of stacking beads made by melting metal wires with an arc heat source generated by a short-circuit current. Compared to other metal additive manufacturing methods, this process can be used to quickly produce large and complex-shaped metal parts. However, due to the multi-bead stacking method, the surface is highly curved and the dimensional errors are large; therefore, post-processing of the surface by cutting is required. Impellers, which are widely used in various industries, have complex shapes and high material consumption during cutting; therefore, the WAAM process can improve the manufacturing efficiency. In this study, a manufacturing process for an impeller with a diameter of 160 mm was developed by using the WAAM process. A 6-bladed fan-type impeller used for high-pressure fluid delivery was similarly modeled, and the product was additively manufactured using an Inconel 625 alloy wire. Manufacturing conditions that ensure productivity and quality or the product were determined through experimentation. Considering the post-processing of the WAAM-fabricated structure, the robot and tool paths of the impeller model were designed, and the error in the process coordinate system caused by attaching and detaching the workpiece between the two processes was reduced. Through the post-processing of the WAAM-fabricated structure, the production efficiency and process reliability were verified when the conventional manufacturing method and WAAM process were applied.

Effect of Roundness Error of the Grooves on the Inner Ring Runout of Angular Contact Ball Bearings

In this paper, a prediction model of the inner ring runout of angular contact ball bearings is established according to the geometric and kinematic relationships of the bearing, considering factors such as the roundness error of the inner and outer grooves, the dimensional error of the balls, and the change of the contact angle between the balls and the grooves. The correctness of the model is verified through experiments. The effects of the order and amplitude of the roundness error of the inner groove and the order and amplitude of the roundness error of the outer groove on the inner ring runout are analyzed. The coupling effect of the roundness error of the inner and outer grooves on the inner ring runout is further analyzed. The results show that the inner ring runout changes periodically with a change to the roundness error order of the grooves, which increases with an increase in the roundness error amplitude. Under the coupling of the roundness error of the inner and outer grooves, the magnification of the inner ring runout increases as a whole. When there are specific relationships between the roundness error orders of the grooves and the number of balls, the magnification of the axial or radial runout changes significantly.