Product Assembly Research Articles

Anomaly triplet-net: progress recognition model using deep metric learning considering occlusion for manual assembly work

In this paper, we propose a method for recognizing progress in product assembly considering occlusion using deep metric learning. Visualizing the assembly process in factories is crucial for enhancing work efficiency and minimizing disposal costs. However, there is a problem in that products are managed by pasting them on paper with progress status written on them. We solve the problem of having to manage progress manually. First, the target assembly product is detected from images acquired from a fixed-point camera installed in the factory using a deep learning-based object detection method. Next, the detection area is cropped from the image. Finally, by using a classification method based on deep metric learning on the cropped image, the progress of the product assembly work is estimated as a rough progress step. As a specific progress estimation model, we propose an Anomaly Triplet-Net which is an improved model of the existing method Triplet Loss. This model considers occlusion by learning anomaly samples. In experiments, an 82.9 [%] success rate is achieved for the progress estimation method using Anomaly Triplet-Net. We also experimented with the practicality of the sequence of detection, cropping, and progression estimation, and confirmed the effectiveness of the overall system.

Mechanically Driven, Continuous Synthesis of Chiroplasmonic Assemblies.

Chiral plasmonic nanomaterials─with their significant applications in protein detection, drug screening, and enantioselective sensing─necessitate an industrialized fabrication procedure to enhance their commercial viability. However, the prevailing manufacturing of chiral plasmonic nanoparticles and assemblies heavily leans on manual intervention, causing time-consuming and quality-inconsistent concerns. Here, we develop an automated, continuous mechanical synthesis system that consistently sprays metal nanowires to create chiroplasmonic assemblies: a macroscopic twisted layered structure comprising equivalent linear birefringence layers, approximate linear polarizer layers, and a precise angular offset between them. Utilizing the synthesis-with-automation system, we scale up the production of chiral plasmonic films, generating high optical asymmetry (g-factor, with the order of 10-1) across a broadband ranging from ultraviolet to near-infrared wavelengths. We further introduce the portable chiral sensing, expanding plasmonic assemblies into flexible materials and integrating them with wearable real-time display devices. Our mechanically driven, continuous synthesis of chiral plasmonic structures presents an intriguing pathway to facilitate functional chiral structures toward practice.

A Two-Individual-Based Evolutionary Algorithm for Flexible Assembly Job Shop Scheduling Problem with Uncertain Interval Processing Times

The assembly job shop is a prevalent production organization mode in manufacturing enterprises. During the processing and assembly of products, operation processing times are influenced by numerous factors, leading to significant uncertainty. This paper investigates the flexible assembly job shop scheduling problem (FAJSP) with uncertain processing times, where processing times are represented as variable interval numbers. We develop a robust optimization model for the FAJSP, utilizing confidence level estimation to determine the ranges of processing times and reformulating the model based on the chance-constrained method. A two-individual-based master–apprentice evolutionary (MAE) algorithm is proposed. Two effective encoding schemes are designed to prevent the generation of infeasible solutions under assembly sequence constraints. Additionally, a decoding method based on interval scheduling theory is devised to accurately represent interval processing times. Case studies are conducted to validate the effectiveness of the proposed robust optimization model and demonstrate the superiority of the MAE algorithm.

Optimization Processes in Automotive Logistic Flow

This paper presents a logistic flow of assembling automotive rear axles. The product is presented in detail starting from the detailed research and analysis of relevant documentation about its functionality, including the manufacturing logistic flow diagram and the required equipment for the product manufacturing and assembly. This study is focused on optimizing the logistic flow for the manufacturing and assembly of automotive rear axles using WITNESS Horizon for system modeling and simulation in order to conduct system diagnostics, identify problems, and find solutions that will facilitate the optimization process. The study included a comprehensive assessment of the logistic flow, highlighting the performance of the equipment involved and identifying potential bottlenecks. Using the results obtained after the simulations, the Simplex linear mathematical method was applied to maximize production efficiency and profitability, considering the suppliers’ capacity constraints and the components’ delivery requirements. The results demonstrated a significantly optimized rear-axle production process, with increased profitability and improved productivity by eliminating identified bottlenecks. This research contributes to a deeper understanding of the complexities within the automotive industry and provides a solid foundation for continuously improving manufacturing and assembly processes.

Numerical Model for Investigating Effects of Cracks and Perforation on Polymer Electrolyte Fuel Cell Performance

The presence of cracks in the microporous layer (MPL) of polymer electrolyte fuel cells (PEFCs), often occurring during the membrane electrode assembly manufacturing process, has a significant impact on cell performance. However, the exact influence of crack presence, density, and patterns within MPLs on cell performance and transport behaviors remains unclear. This study introduces a three-dimensional macroscale model of PEFCs aimed at investigating the effects of MPL cracks and gas diffusion layer (GDL) perforations on cell performance and transport behavior. This model offers several advantages, including the ability to potentially integrate the effects of flow channel design in future. Additionally, the model can seamlessly incorporate electrochemical reactions and explore phenomena within the catalyst layers (CLs), expanding simulation capabilities beyond water transport alone. The findings suggest that MPL cracks contribute positively to performance by facilitating water drainage. Furthermore, when combined with perforations in GDLs, MPL cracks can significantly enhance performance by providing pathways for water transport. Overall, this study provides valuable insights into developing models for optimizing PEFC performance and underscores the need for further research and development in this area.

Contactless printing of food micro-particles controlled by ultrasound

Traditional food 3D printing technology, which primarily relies on mechanical control, often faces challenges such as low resolution, stringent material requirements, limited flexibility, and cumbersome equipment. To overcome these limitations, we developed an innovative food 3D printing method utilizing ultrasonic phased arrays and subsequently designed a contactless printing device employing this advanced technology. The device effectively manipulates the phase and amplitude of the ultrasonic phased array to focus the sound field on a specific location. We have successfully demonstrated preliminary control over the suspension of small food particles, including popcorn, chocolate, honey, and marshmallows, using this apparatus. Following this, popcorn particles were selected for detailed practical printing and further analysis. Additionally, we established a software platform, Ultr_printing, based on Ultraino, for intelligent simulation and control of the printing process. Leveraging FPGA and Arduino controllers, this setup facilitates rapid signal transmission from personal computers to sensors, ensuring quick and adaptable movement of suspended particles. Experimental results confirm that the device consistently controls the food particles, irrespective of their motion state. Notably, it achieves a printing precision of 0.16 mm when handling popcorn particles measuring 1.5 mm in diameter. The process, entirely contactless and free from contamination, preserves the original flavor of the food particles. This technology is particularly effective for high-precision printing of micro-scale shapes and offers considerable benefits in assembly and precision manufacturing.

Research on the green development path of prefabricated building industry based on intelligent technology

PurposeIn order to make the construction industry develop in the direction of greening, this paper analyzes whether the application of intelligent technology in prefabricated buildings can achieve carbon emission reduction, starting from the problems of weak technology and insufficient encouragement policies in the prefabricated building industry. It also designs dynamic and adjustable incentives for the smart transformation of prefabricated buildings and makes recommendations to facilitate the transformation of assembly manufacturers into “smart factories”.Design/methodology/approachThis paper takes the intelligent technology for carbon reduction, energy efficiency and policy design in the prefabricated buildings industry as the starting point. Based on in-depth expert interviews and questionnaire survey data, a linear multiple regression model is used to establish an association network of intelligent technology in the production and transportation, construction, operation and maintenance, demolition and scrapping stages. On this basis, an evolutionary game theory is used to construct a smart transformation and carbon reduction utility game model between the government and manufacturers, and relevant suggestions for smart empowerment of green construction development technology combinations and policy settings are proposed.FindingsAn assembly manufacturing plant with smart empowerment is an important way to achieve green and sustainable development in the construction industry. Among them, BIM and IoT have made a greater impact on carbon emission reduction of prefabricated buildings in all stages of the whole life cycle. The government’s proposed energy efficiency incentives and environmental tax amount will effectively increase companies' motivation for smart transformation of prefabricated buildings. However, when the environmental tax amount is low, the government should strengthen the regulation of the industry in order to increase the speed of smart transformation of assembly manufacturers. Therefore, a reasonable setting of the environmental tax rate and energy-saving incentives and flexible adjustment of the regulatory efforts can maximize the functional utility of the government in the process of smart transformation.Research limitations/implicationsThis paper focuses on the impact of intelligent technologies on the overall carbon emissions of the industry and provides an evolutionary analysis of the strategic game between the government and assembly manufacturers, the main players in the smart transformation process of prefabricated buildings. However, smart technologies for different categories of assembly manufacturing plants and strategic options for a wider range of stakeholders have not been examined in depth.Originality/valueDifferent from existing research, this study focuses on exploring the strategic game between the government and assembly manufacturers in the smart transformation of prefabricated buildings. It provides an innovative explanation of the connection between intelligent technology and carbon emissions. The study develops an evolutionary game model for both parties, addressing the research gap on the combined effects of policy incentives and intelligent technology on carbon reduction and efficiency improvement in the prefabricated buildings industry. This research not only offers practical reference for the government in designing incentive mechanisms and establishing regulatory systems but also provides feasible practical guidance for the smart transformation and carbon reduction efforts of assembly manufacturing plants.

Political stability in authoritarian regimes: the case of Central Asia

ABSTRACT Existing research indicates that political stability is a prerequisite for good governance and economic development in Central Asia. All five countries in the region are authoritarian regimes but it is unclear which factors impact on political stability. Using data for the period 2002–2022, this paper examines the variables which make a significant contribution to political stability and finds that the rule of law, personalist rule, a free media, freedom of assembly, political polarization and gross domestic product (GDP) growth, respectively have a positive effect on political stability while government accountability, exclusions by social group and civil society participation have a negative effect. Some of the findings are at odds with other studies on autocratic regimes, illustrating the importance of geo-political context.

Modeling of CNC machine tool line rail assembly accuracy and its control method

Aiming at the linear guide assembly process, due to the preload load caused by the assembly deformation mechanism is not clear resulting in the guide assembly straightness is difficult to predict the problem. In this paper, based on the establishment of linear guide assembly deformation model, the ideal and actual conditions of the guide assembly deformation is discussed. Secondly, considering the influence of manufacturing error of the guide rail mounting surface on the guide rail assembly accuracy, it puts forward the integrated assembly deformation and manufacturing error of the guide rail assembly straightness error prediction model, and puts forward the control method of the guide rail assembly accuracy based on the assembly deformation and the inverse repair manufacturing error. Finally, the experimental bench is built and the accuracy of the theoretical model and accuracy control method is verified through experiments , and the results show that the straightness error of the guideway assembly is reduced by .

Multifaceted roles of NADPH oxidases in the growth and pathogenicity of Beauveria bassiana

ABSTRACT Reactive oxygen species (ROS), synthesized by the nicotinamide adenine dinucleotide phosphate (NADPH) oxidase (Nox) complex, are vital molecules in biological cells, influencing various physiological processes such as fungal growth, development, and virulence. Beauveria bassiana, an entomopathogenic fungus, is a promising biopesticide for agricultural, forestry, and urban pest control. This study focuses on the characterization of NADPH oxidases (Noxs) in B. bassiana. Gene expression profiles of Noxs in B. bassiana (BbNoxs) were analysed using RT-qPCR. Knockout strains of single BbNoxA, BbNoxB, BbNoxR, and double BbNoxA and BbNoxB were constructed via homologous recombination, and their phenotypic characteristics were examined. Fungal virulence was evaluated using Galleria mellonella larvae, and infection structures formation and penetration ability were assessed on cicada wings. ROS production and actin assembly during fungal growth and infection were detected using staining and marker methods. Expression analysis revealed significant upregulation of BbNoxs during fungal growth and infection. Compared to the wild-type strain, single knockouts (ΔBbNoxA/B/R) and double knockout (ΔBbNoxAB) of BbNoxs exhibited reduced conidial yields, accelerated conidial germination rates. Deletion of BbNoxB or BbNoxR decreased fungal virulence compared to the WT strain in topical inoculation experiments. Additionally, loss of BbNoxB or BbNoxR impaired infection structures formation, penetration ability, ROS production, and actin aggregation during fungal infection. BbNoxs are crucial for fungal growth, development, and virulence in B. bassiana, playing essential roles in infection structures formation, penetration, ROS production, and actin assembly. Understanding their functions provides insights into B. bassiana’s pathogenic mechanisms.

Safety-risk assessment system for prefabricated building construction in China

PurposePrefabricated building (PB) uses factory production and onsite assembly, which differs from traditional construction methods. This special construction approach may lead to dissimilar safety risks and challenges. Traditional safety assessment methods may not adequately and accurately assess the safety risks of PB construction. This paper aims to develop a new concept and methodology for targeted improvement in assessing PB safety risks.Design/methodology/approachRisk factors and indicators were established based on literature review and expert inputs. A structural equation model (SEM) was developed to investigate the relationships among three main risk categories: objects, workers and management. SEM analyzed the intricate associations between indicators and deepened understanding of safety risks. The model was tailored for China’s PB construction projects to enhance safety-risk management.FindingsThe cloud model evaluation validated the SEM model. A PB case study project tested and verified the model, evaluated its efficacy and quantified its safety performance and grade. We identified significant safety risk impacts across the three risk categories, safety-control level and specific areas that require improvement. The SEM model established a robust safety evaluation indicator system for comprehensive safety assessment of PB construction.Practical implicationsPractical recommendations provide valuable insights for decision-makers to enhance construction efficiency without compromising safety. This study contributed to the conceptual foundation and devised a novel method for evaluating safety performance in PB construction for safer and more efficient practices.Originality/valueThis study departed from the traditional method of calculating weights, opting instead for the SEM method to determine the weights of individual risk indicators. Additionally, we leveraged the cloud model to mitigate the influence of subjective factors in analyzing questionnaire survey responses. The feasibility and reliability of our proposed method were rigorously tested and verified by applying it to the PB case.

A systematic quality-integrated diagnostic method for complex product assembly using multi-task spatial–temporal transfer learning

The assembly process is generally considered one of the primary factors influencing the quality of complex products. Currently, most existing quality-integrated diagnostic methods for products tend to deteriorate over different processes and degrade over time. To address this issue, this paper introduces a systematic quality-integrated diagnostic method for complex product assembly processes. First, the influence factors and error sources in the complex assembly process are analyzed using the 5M1E and FAHP methodologies. Next, similarity-based multi-task clustering and dismantling using RGMM is applied to divide the assembly tasks. Finally, MMD-MSE is employed to develop a quality prediction model for the complex assembly process using a spatial–temporal transfer learning approach. Experiments were conducted on an array antenna assembly task, comparing the proposed method with conventional methods. The results show that the accuracy and PrUP of the proposed model are 97.6% and 95.2%, respectively, for the quality-integrated diagnostic of complex assembly processes, with a fluctuation in accuracy of less than 6%. The diagnostic results effectively meet expert evaluations and provide a stable, reliable, and practical solution for addressing quality fluctuations in complex production assembly processes.

Influence of bile salts on the gastrointestinal digestion of agar-casein hybrid systems and the nanoassembly of their digestion products

This manuscript reports on the effect of different bile salts concentration on the gastrointestinal digestion of casein and casein-agar hybrid systems and evaluates the effect on the nanostructural assembly of the digestion products through the application of advanced small angle X-ray scattering analysis (SAXS).The results showed that bile salts promote the hydrolysis of micellar casein upon in vitro gastrointestinal digestions. It is noteworthy that in the presence of agar, bile salts interact with the polysaccharide, leading to a reduction in their proteolytic activity. While structured agar-casein hydrogels were shown to be able to limit the proteolysis during the gastric phase, increasing concentrations of bile salts promoted the diffusion of casein from the gel network during the intestinal phase, hence leading to higher degree of hydrolysis. The released digestion products were seen to interact with bile salts, forming ordered lamellar/micellar nanostructures. In particular, the presence of solubilized agar in the digesta was seen to promote the formation of these nanostructures. These nanostructures were formed at 10 mM bile salts for the hydrogels, and across all concentrations for agar-casein blends. Thus, this work demonstrates the great relevance of bile salts in the nanostructural assembly of protein digestion products, which is expected to have a great relevance in bioavailability and metabolic responses induced by protein-rich foods.

Mass customization using hybrid manufacturing and smart assembly: An optimal configuration and platform design approach

Hybrid Manufacturing (HM) and smart assembly stand as pivotal pillars in advanced smart manufacturing systems, offering manufacturers highly efficient and adaptable solutions for manufacturing. This paper delves into the configuration of a production line that integrates HM and assembly stages, each comprising multiple cells, with each cell housing one or more parallel stations. The objective is to manufacture a family of final assemblies, leveraging the platform concept to defer mass customization to later stages and thereby minimize processing costs. A mathematical programming model is proposed to identify the optimal configuration for such production lines, considering constraints such as an allowable capital cost and machine availabilities. In addition, the precedence, inclusion, and seclusion restrictions imposed on the part family are considered. The proposed mathematical programming model aims to delineate which HM features are processed in the part platform cell versus those processed in the mass customization (part variants) cells. Simultaneously, the model determines the components (variants from the HM stage) of final assemblies processed in the assembly platform cell, as well as components assembled or disassembled in the final assembly cells. Furthermore, the model seeks to determine the required number of stations in each cell to meet periodic demand. The overall objective of the model is to minimize the capital and the processing cost. A detailed case study illustrates the effectiveness of the proposed configuration approach and mathematical model. The proposed model is solvable in a few seconds by using commercial solvers.

CAPP-GPT: A computer-aided process planning-generative pretrained transformer framework for smart manufacturing

Smart manufacturing (SM) constitutes the backbone of Industry 4.0 (I4.0), allowing for heightened autonomy of the various interacting cyber-physical systems, making the various entities on the production floor. Connectivity, a vital enabler, plays a crucial role through state-of-the-art Digital Twinning (DT) technologies driven by underlying innovations like the industrial Internet of Things, Cloud Computing, and advancements in sensory devices. DT, which plays a vital role in the various planning functions under the production and operations management umbrella, is being used in the developed combined CAPP-GPT (Computer-Aided Process Planning-Generative Pretrained Transformer) and production scheduling approach to address disruptions on the shopfloor and in self-healing of the manufacturing processes at a micro-CAPP level by optimally adapting the process parameters and the developed toolpath on the fly based on online process signature measurements. In a leap commensurate with that which has taken place in Natural Language Processing-Large Language Models (Chat-GPT), similar efforts are currently being undertaken to parse CAD data structures and blueprints, fusing operations research and predictive analytics algorithms to carry out setup planning as well as sequencing and grouping manufacturing sub-operations. A hybridized Optimization and Machine Learning (ML) approach is employed where Logical Analysis of Data is used to solve the problem heuristically, exploiting various generative and variant methods at heart. Another extension of this macro-CAPP problem is being tackled by integrating the problem with delayed product differentiation, lot-sizing, and transfer line balance for futuristic batch-production shops employing Hybrid Manufacturing (HM) and Smart Assembly. At the micro-CAPP level, HM process parameters are optimized using a comprehensive approach employing the Taguchi loss function to assess surface roughness, internal failure costs, and other criteria, including greenhouse gas emissions and expended energy. Online measurements of the process signatures are also employed to adapt the initial set of process parameters using different automatic control schemes. ML is used to identify the process parameters carrying simulations on Simulink before the system is deployed.

Развитие теории и принципов планирования многономенклатурных технологических процессов механообработки и сборки

Instructional guidelines for planning a multiproduct manufacturing activity (MA) for machining and assembly aimed at solving currently existing problems of machining industries process design, i.e. subjectivity of making design decisions, insufficient automation, and inability of the observing of current production situation are presented. The objective reasons for the need to improve approaches to the development of MA are the long cycle of the process design period, the low quality of the processes operation, and the impossibility to adjust MA at the implementation stage. The main structural elements of multiproduct manufacturing activity planning are described. Based on a systematic approach, all stages of work on technological support of production systems are combined, making possible to use the information arrays of data on the real state of the production system and operational information on production tasks in a short time frame. The results of theoretical work provide for the process of making MA as a system that combines the design and implementation of technology, taking into account the impact of changes in the production situation. In the process of approbation and use of the presented methodological approaches, significant areas of research were identified, smoothing away difficulties connected with the performance targets, enabling the development path of the system. Among them: the interaction with the design preparation of production; detailing data on the initial workpieces in connection with the choice of the MA structure and the appointment of a rational set of technological equipment; determining the requirements for technological equipment, focused on ensuring the flexibility of their use; determining the correlations between the multiple-path design of parts manufacturing process and the requirements for accuracy indicators in the assembly of products. The developed formalized models for the implementation of the stages of technological preparation of machining industries are a background for full automation of MA design and highly efficient functionality of machine-building complexes.

Hierarchical Semi-Supervised Approach for Classifying Activities of Workers Utilising Indoor Trajectory Data

Activity recognition refers to the process of automatically identifying or interpreting activities of objects based on the data captured from different sensing devices. While previous research on indoor activity recognition predominantly relies on visual data like images or video recordings, we present a novel approach based on spatiotemporal trajectory data recorded by IoT based sensors. The proposed approach is tailored for indoor manufacturing applications leveraging trajectory partitioning, hierarchical clustering, and convolutional neural networks. Moreover, a vast majority of activity recognition models that have been used in different industrial settings are supervised methods requiring large manually labelled datasets to be collected. This manual annotation process is unwieldly and labour-intensive, and hence, often infeasible to deploy for practical applications. In contrast, our proposed activity recognition approach is semi-supervised meaning it can be trained with far less labelled data; significantly reducing the effort and costs associated with the manual annotation process. The proposed approach is evaluated using two indoor trajectory datasets related to different manufacturing assembly processes. Experimental results demonstrate its effectiveness for activity recognition: the classification accuracy (measured using F-score) varies between 0.81 to 0.95 and 0.88 to 0.92 across indoor trajectory datasets. A comparison with a baseline model indicates that it achieves up to a 18% improvement in classification accuracy. Furthermore, the classification results enable insights into factory floor states, aiding in decision-making for operational efficiency and resource allocation.

ИНФОРМАЦИОННЫЕ ОСНОВАНИЯ СИСТЕМЫ ТЕХНОЛОГИЧЕСКОГО ОБЕСПЕЧЕНИЯ МЕХАНИЧЕСКОЙ СБОРКИ В УСЛОВИЯХ ЦИФРОВОЙ ТРАНСФОРМАЦИИ ПРЕДПРИЯТИЙ АВТОМОБИЛЕСТРОИТЕЛЬНОГО КЛАСТЕРА

Information grounds for supporting decision-making on the optimal formation of automated systems for technological preparation of assembly production are in demand at the automotive cluster enterprises. The paper proposes to develop two databases in the process of forming the optimal set of control and verification equipment and the set of spare equipment and spare parts. This will allow accumulating information due to the feedback on the state of its subsystems in the process of operating automated systems for the technological preparation of assembly production. For this purpose, the paper proposes the database “A set of the system elements for providing mechanical assembly”, as well as the database “Statistical information for forming the stock of assembly equipment and spare parts in the system for ensuring mechanical assembly”. The work presents the information on the registration output, the structure and number of records of two developed databases, calculates the economic effect in the cost of technological preparation of production using the described databases.

Obstacle avoidance shape control of deformable linear objects with online parameters adaptation based on differentiable simulation

The manipulation of deformable linear objects (DLOs) such as ropes, cables, and hoses by robots has promising applications in various fields such as product assembly and surgical suturing. However, DLOs are more difficult to manipulate than rigid objects because their shape changes during manipulation. Furthermore, preventing a DLO from colliding with the environment is important to prevent it from becoming entangled and causing shape control to fail. In this paper, we proposed an obstacle avoidance and shape control scheme for DLOs based on differentiable simulation that does not require prior data or a specialized controller. First, we established a dynamic model of the DLO that allows for both forward dynamics transfer and error backpropagation to obtain gradients. Then, we employed model predictive control to optimize the embedded neural network for predicting the actions that would manipulate the DLO. Finally, the control scheme was made applicable to DLOs with different material properties by allowing online adaptation of the model parameters essential to deformation during manipulation. Simulations and real-world experiments demonstrate that the proposed control scheme could manipulate the DLO stably and accurately to avoid obstacles and achieve the goal state. In addition, the online adaptation of parameters helped mitigate the sim-to-real gap.

Measurement and optimization method for aero-engine rotors based on binocular multi-line laser sensing and virtual assembly

During the assembly of aero-engine rotors, eccentricity and tilt errors along the radial and axial direction result in uneven loading along the mating surface, which significantly impacts the operational lifespan of the rotor. This paper proposes a three-dimensional contour measurement system based on binocular multi-line laser sensing and conducts an aircraft engine rotor stacking virtual assembly technology system, to response the practical requirements of aircraft engine rotor unit production assembly engineering. We combine multi-line structured light active projection device with the binocular stereo vision model to construct the contour measurement system, which enhances the surface features of the projected object by adhering to the common constraint of structured line and stereo vision. Furthermore, the error propagation model for multi-stage rotor assembly to investigate the mechanism of error impact is established aiming to facilitate quantitative research on virtual assembly mechanisms. By manipulating the rotor phases and evaluating the coaxiality under various conditions, effective prediction of phase variation can be achieved, the multi-objective benchmark virtual assembly method based on the error propagation model of multi-stage rotor coaxiality is presented. The experimental results indicate that the proposed measurement system achieves the maximum disparity between the spatial data points procured by the proposed measurement system and the predetermined value of 2.1 μm, and the maximum disparity between the target plane’s average movement distance and the preset value of 3.7 μm. The precision provided by this system effectively meets the objective of assembly phase optimization for multi-stage rotors. In the empirical experiment conducted on the three-stage rotors, both systems yielded identical optimized phases: 0° for the first-stage rotor, 270° for the second-stage rotor, and 120° for the third-stage rotor, thereby validating the efficacy of the proposed measurement and optimization method for aero-engine multi-stage rotors with virtual assembly.