Multi-station Assembly Processes Research Articles

Identification of Key Factors and Mining of Association Relations in Complex Product Assembly Process

There are many factors involved in the assembly process of complex products, but only a few of the key influencing factors really affect the performance of the product assembly. In order to ensure the performance of complex products, it is necessary to model the deviation transfer flow of the assembly process of complex products and identify key processes and characteristics and determine its scope of influence. This paper establishes the set of assembly performance influencing factors by analyzing the complex product assembly process and uses complex networks combined with entropy weight method to identify the influencing factors of product performance to achieve the identification of key influencing factors. Based on the key factor identification results using an Apriori algorithm to mine the association rules between key factors and performance to provide guidance for the assembly of complex products and achieve transparent and controllable performance. The assembly process of a certain type of aeroengine low-pressure fan rotor is verified as the research object. The results show that the proposed method can identify the key factors in the multistage assembly process and establish an association rule library to provide decision support for process adjustment.

Sensor Optimization for Variation Diagnosis in Multistation Assembly Processes

Appropriate sensor deployment is the key to the efficient diagnosis of product variation. Yet, optimizing sensor placement in complex manufacturing systems remains challenging. We propose a variation propagation analysis (VPA)-based sensor deployment strategy for variation diagnosis in multistation assembly processes. A state-space model is employed to analyze the influences of fixture faults and workpiece dimensional deviations on assembly variation. Based on matrix transformation, the assembly variation propagation characteristics are quantified and a VPN-based causal graph is constructed to represent the causality between assembly variation and sensor measurement. To ensure the diagnosability of over-tolerance of assembly variation (OAV) and the economics of the sensor system, an optimal sensor deployment scheme is presented. It uses the enhanced shuffled frog-leaping algorithm to minimize the OAV unobservability per unit cost and the sensor cost under the constraint of detectability. Finally, the effectiveness of the proposed approach is illustrated by a case study of sensor deployment for variation diagnosis in a multistation automobile differential assembly process.

Virtual shimming simulation for smart assembly of aircraft skin panels based on a physics-driven digital twin

A leading challenge in the assembly process of aircraft skin panels is the precise control of part-to-part gaps to avoid excessive pre-tensions of the fastening element which, if exceeded, impair the durability and the response under dynamics loads of the whole skin assembly. The current practice is to measure the gap in specific points of the assembly with parts already at their final location, and then be-spoke shims are machined and inserted between the mating components to fill the gap. This process involves several manual measurement-fit-adjust quality loops, such as loading parts on the assembly frame, measuring gaps, off-loading parts, adding be-spoke shims and re-positioning parts ready for the fastening operation—as a matter of fact, the aircraft is re-assembled at least twice and therefore the current practice has been proved highly cost and time ineffective. Additionally, the gap measurement relies on manual gauges which are inaccurate and unable to follow the actual 3D profile of the gap. Taking advantage of emerging tools such as in-line measurement systems and large-scale physics-based simulations, this paper proposes a novel methodology to predict the part-to-part gap and therefore minimise the need for multiple quality loops. The methodology leverages a physics-driven digital twin model of the skin assembly process, which combines a physical domain (in-line measurements) and a digital domain (physics-based simulation). Central to the methodology is the variation model of the multi-stage assembly process via a physics-based simulation which allows to capture the inherent deformation of the panels and the propagation of variations between consecutive assembly stages. The results were demonstrated during the assembly process of a vertical stabiliser for commercial aircraft, and findings showed a significant time saving of 75% by reducing costly and time-consuming measurement-fit-adjust quality loops.

Metal–organic cycle-based multistage assemblies

It is well known that chemical compositions and structural arrangements of materials have a great influence on their resultant properties. Diverse functional materials have been constructed by using either biomolecules (peptides, DNA, and RNA) in nature or artificially synthesized molecules (polymers and pillararenes). The relationships between traditional building blocks (such as peptides) have been widely investigated, for example how hydrogen bonds work in the peptide multistage assembly process. However, in contrast to traditional covalent bond-based building blocks-based assembly, suprastructures formed by noncovalent bonds are more influenced by specific bond features, but to date only a few results have been reported based on noncovalent bond-based building block multistage assembly. Here, three metal–organic cycles (MOCs) were used to show how coordination bonds influence the bimetallacycle conformation then lead to the topology differences of MOC multilevel ordered materials. It was found that the coordination linker (isophthalate-Pt-pyridine) is an important factor to tune the shape and size of the MOC-derived suprastructures.

Fault diagnosis for underdetermined multistage assembly processes via an enhanced Bayesian hierarchical model

Previous works have shown that only relying on measurement data is generally insufficient to identify root causes of the dimensional variation for the complex manufacturing system. It is also well known that for underdetermined multistage assembly processes (MAPs), the number of measurements is less than that of process errors so that the traditional methods are not available for the variation source identification. Therefore, there exists a substantial challenge for fault diagnosis of underdetermined MAPs, especially for the case with multiple fault patterns. To tackle this problem, a novel approach that integrates statistical analysis with domain knowledge is proposed in this paper. First, the variation propagation model is employed to reveal the inherent relationship between key control characteristics and key product characteristics, and then the corresponding variance model is constructed to interpret process faults by means of the abnormal variance of process errors. Considering that the probability of less process faults is far higher than that of more process faults in MAPs, the problem of fault diagnosis is further transformed into searching the sparse solution of abnormal variance changes for process faults. Afterwards, based on the non-negative property of the covariance matrix, an enhanced Bayesian hierarchical model is developed to favor sparse estimation of the variance for the underdetermined system in MAPs. Finally, experimental results of both the numeric simulation and practical case study demonstrate the proposed diagnosis methodology can effectively identify the process faults for different patterns with system noise.

Integration of in-plane and out-of-plane dimensional variation in multi-station assembly process for automotive body assembly

Automotive body assembly systems contain multiple operations in multi-station processes. One of the most critical challenges for such manufacturing systems is dimensional quality, which is affected by the accumulation and propagation of variation caused by manufacturing imperfections. However, sheet metal part compliancy behavior makes the variation modeling method extremely intricate when both rigid (in-plane) and compliant (out-of-plane) variations are considered. This paper develops a more accurate variation propagation model to describe dimensional variation of sheet metal assembly in multi-station assembly system through involving both the variation types simultaneously as well as the impacts of assembly operations on each other. In this methodology, three sources of deviations—non-ideal parts, fixture errors, and assembly operations effects—are taken into account. The variation generated in every assembly operation (placing, clamping, fastening, and releasing steps) and the variation propagation through station-to-station interaction (repositioning) are analyzed by the transfer function mechanism. In the in-plane direction, the stream of variation analysis is adopted to obtain the rigid transfer function to describe the position and orientation relationships between part and assembly element errors. For the simulation of part deformation during the assembly process in the out-of-plane direction, the compliant transfer functions are extracted by variation response methodology. A nested state space model is used to integrate the overall assembly variation by updating part geometry after each assembly operation. The capability of proposed method is illustrated through a case study on an automotive body side assembly process.

A knowledge-based online fault detection method of the assembly process considering the relative poses of components

The real-time process fault detection in the multi-station assembly process is always a challenging problem for auto body manufactures. Traditionally, the fault diagnosis approaches for variation source identification are divided into two categories, i.e. the pattern matching methods and model-based estimation ones based on the collected data set. The measurements provide effective process monitoring, but the real-time process fault diagnosis in the assembly process is still difficult with the traditional diagnosis techniques, and always depends on the engineering experience in practice. Based on the assembly process knowledge, including multi-station assembly hierarchy, fixture scheme, measurement characteristics and tolerances etc. in the multi-station, a knowledge-based diagnostic methodology and procedures are proposed with the measurements of each body in white for part/component defections and faulty assembly station identification. For the station involved with defective parts/components, the sub-coordinate system of the part/component is established reflecting its position and pose in the space, and then the relative pose matrix to the “normally build” pose is calculated based on the deviations of sub-coordinates of the parts in this station. Finally, the assembly process malfunctions are determined by a proposed rule-based strategy with the relative pose matrix in real time. A simple 3 stations assembly process with 5 sheet metal parts was analyzed and compared with the traditional diagnostic method to verify the effectiveness and stability of the proposed method.

Assembly variation analysis of the aircraft panel in multi-stage assembly process with N-2-1 locating scheme

The flexible aircraft panel parts are easy to deform during the assembly process, the deformation can directly affect the assembly accuracy of the aircraft panel, and further affect the assembly quality of the aircraft fuselage. In order to evaluate the assembly accuracy of the aircraft panel in multi-stage assembly process with N-2-1 locating scheme, this paper proposes an assembly variation analyzing approach, considering the effect of the contact force between the panel skin and the fixture components on the deformation of the panel in the assembly process. The mathematical models are established to conclude the contact force between the panel skin and the fixture components in multi-stage assembly process of the panel with N-2-1 locating scheme, and the effect of the contact force between the skin and the fixture components on the deformation of the panel in assembly is investigated. An assembly variation model is built for the aircraft panel considering the variation sources including the manufacturing error of the flexible panel parts, fixture error, and the deformation of the panel in multi-stage assembly process considering the effect of the contact force. Finally, the proposed assembly variation analyzing approach for the aircraft panel is tested with a case study.

A survey on data-driven process monitoring and diagnostic methods for variation reduction in multi-station assembly systems

PurposeDriven by the development in sensing techniques and information and communications technology, and their applications in the manufacturing system, data-driven quality control methods play an essential role in the quality improvement of assembly products. This paper aims to review the development of data-driven modeling methods for process monitoring and fault diagnosis in multi-station assembly systems. Furthermore, the authors discuss the applications of the methods proposed and present suggestions for future studies in data mining for quality control in product assembly.Design/methodology/approachThis paper provides an outline of data-driven process monitoring and fault diagnosis methods for reduction in variation. The development of statistical process monitoring techniques and diagnosis methods, such as pattern matching, estimation-based analysis and artificial intelligence-based diagnostics, is introduced.FindingsA classification structure for data-driven process control techniques and the limitations of their applications in multi-station assembly processes are discussed. From the perspective of the engineering requirements of real, dynamic, nonlinear and uncertain assembly systems, future trends in sensing system location, data mining and data fusion techniques for variation reduction are suggested.Originality/valueThis paper reveals the development of process monitoring and fault diagnosis techniques, and their applications in variation reduction in multi-station assembly.

Sensor layout optimization by integrating Bayesian approach to diagnose multi-station assembly processes

This paper presents a sensor deployment strategy based on a Bayesian network (BN) and information entropy to diagnose multi-station assembly processes. A station-indexed state space model is employed to analyze the influence of fixture faults and part reorientation faults on the process fault. Based on the matrix transformation of the state space model, the inherent rules of process fault propagation in various stations are revealed and the system detectability is quantified by the process fault-detectability index. Subsequently, a BN-based quantified causal graph is developed to model the causal relationship between process faults and sensor measurements, and information entropy is introduced to quantify the uncertainty of process fault diagnosis. Finally, sensor–fault matching algorithms are proposed to minimize information entropy of unit cost and process fault unobservability, under the constraints of detectability, thus achieving optimum sensor placement. An example involving assembly of automobile differential illustrates the methodology.

Comprehensive identification of aircraft coordination feature based on complete importance modeling and its engineering application

PurposeCoordination feature (CF) is the information carrier in dimension and shape transfer process in aircraft manufacturing. The change of its geometric size, shape, position or other attributes would affect the consistency of accumulated errors between two or more assemblies. To identify these “key characteristics” that have a close relationship with the assembly precision, a comprehensive method was developed under digital manufacturing environment, which was based on importance calculation. The multi-hierarchy and multi-station assembly process of aircraft products were also taken into consideration.Design/methodology/approachFirst, the interaction and evaluation relationship between components at different manufacturing stages was decomposed with a hierarchical net. Second, to meet coordination accuracy requirements, with the integrated application of Taguchi quality loss function, accuracy principal and error correction coefficient H, the quality loss between target features and candidate features at adjacent assembly hierarchies were calculated, which was based on their precision variation. Third, the influence degree and affected degree of the features were calculated with DEMATEL (decision-making trial and evaluation laboratory) method, and the concepts of centrality degree index and cause degree index were proposed for calculating the complete importance degree to eventually identify the CFs.FindingsBased on the proposed methodology, CFs, affecting the skin profile and the flush coordination accuracy, were successfully identified at different assembly hierarchies to a certain type of wing flap component.Originality/valueBenefit results for the engineering application showed that the deviation of skin profile was more accurate than before, and the tolerance was also closer to the centerline of required assembly precision range. Moreover, the stability in the assembly process was increased by 26.9 per cent, which could bring a higher assembly quality and an enhancement on aircraft’s flight performance.

Variation simulation model for pre-stress effect on welding distortion in multi-stage assemblies

In the shipbuilding industry, steel plates are assembled and joined to construct a block, and dozens of blocks are erected to build a ship. The major joining process used to assemble the steel plates is welding. Because the welding process causes strong non-uniform heating of the parts being joined, the local heating and subsequent cooling of welded pieces induce volumetric changes, producing transient and residual stresses as well as plastic deformation. This welding distortion is the main factor causing geometrical imperfections in the steel plate assembly process. These non-nominally shaped parts need to be clamped before the following joining process, which in turn causes pre-stress in the parts being joined. The pre-stresses due to welding and clamping significantly affect the subsequent welding processes. This paper proposes a variation simulation model of compliant assemblies to analyze the propagation of geometrical variations through multiple assembly stages, including the welding distortion under the effect of the pre-stress state. This research extends the mechanical variation simulation model to include the effects of clamping stresses by expressing the pre-stress terms in the form of an additional stiffness matrix. The result helps in confirming the effect of the pre-stress state on the subsequent welding distortion in the multi-stage assembly process. The proposed model can be used to predict the propagation of assembly variation in steel plate assemblies considering the pre-stress effect and welding distortion.

Uncertainty Evaluation in Multistage Assembly Process Based on Enhanced OOPN.

This study investigated the uncertainty of the multistage assembly process from the viewpoint of a stream of defects in the product assembly process. The vulnerable spots were analyzed and the fluctuations were controlled during this process. An uncertainty evaluation model was developed for the assembly process on the basis of an object-oriented Petri net (OOPN) by replacing its transition function with a fitted defect changing function. The definition of entropy in physics was applied to characterize the uncertainty of the model in evaluating the assembly process. The uncertainty was then measured as the entropy of the semi-Markov chain, which could be used to calculate the uncertainty of a specific subset of places, as well as the entire process. The OOPN model could correspond to the Markov process because its reachable token can be directly mapped to the Markov process. Using the steady-state probability combined with the uncertainty evaluation, the vulnerable spots in the assembly process were identified and a scanning test program was proposed to improve the quality of the assembly process. Finally, this work analyzed the assembly process on the basis of the uncertainty of the assembly structure and the variables of the assembly process. Finally, the case of a certain product assembly process was analyzed to test the advantages of this method.

Fixture layout optimization in multi-station sheet metal assembly considering assembly sequence and datum scheme

One of the most influential causes of dimensional inaccuracy in automotive bodies is the inescapable fixture deviation, which is propagated in a Multi-Station Assembly (MSA) process. Therefore, a robust fixture design is necessary to increase product quality. The aim of this paper is to present a methodology for simultaneous optimization of fixture layout and assembly configuration, including assembly sequence and datum shift scheme, in MSA. To model the accumulated variation from one station to the downstream stations, a non-linear state-space representation is adopted. Feasible assembly sequences are automatically generated using a liaison graph and Breadth-First Search algorithm, and also all datum scheme options are provided based on a permutation method that is dependent on the number of parts and stations. Further, the sum of squared standard deviations for key product characteristics to be minimized is an optimization object. Part boundaries, design requirements, and assembly configuration feasibility are the constraints. This constrained problem is converted into an unconstrained one by two strategies: (i) Geometry boundaries and assembly configuration constraints are fulfilled through removing infeasible solutions from the design space and (ii) Design requirements are met by using a self-adaptive penalty function. Finally, the unconstrained problem is optimized using a genetic algorithm. The proposed methodology improved assembly process capability by 68 to 84% for an automotive body side in a case study.

Propagation analysis of variation for fuselage structures in multi-station aircraft assembly

PurposeThis paper aims to present a modeling and analysis approach for multi-station aircraft assembly to predict assembly variation. The variation accumulated in the assembly process will influence the dimensional accuracy and fatigue life of airframes. However, in digital large aircraft assembly, variation propagation analysis and modeling are still unresolved issues.Design/methodology/approachBased on an elastic structure model and variation model of multistage assembly in one station, the propagation of key characteristics, assembly reference and measurement errors are introduced. Moreover, the reposition and posture coordination are considered as major aspects. The reposition of assembly objects in a different assembly station is described using transformation and blocking of coefficient matrix in finite element equation. The posture coordination of the objects is described using homogeneous matrix multiplication. Then, the variation propagation model and analysis of large aircraft assembly are established using a discrete system diagram.FindingsThis modeling and analysis approach for multi-station aircraft assembly reveals the basic rule of variation propagation between adjacent assembly stations and can be used to predict assembly variation or potential dimension problems at a preliminary assembly phase.Practical implicationsThe modeling and analysis approaches have been used in a transport aircraft project, and the calculated results were shown to be a good prediction of variation in the actual assembly.Originality/valueAlthough certain simplifications and assumptions have been imposed, the proposed method provides a better understanding of the multi-station assembly process and creates an analytical foundation for further work on variation control and tolerance optimization.

Fault pattern identification in multi-stage assembly processes with non-ideal sheet-metal parts based on reinforcement learning architecture

A reinforcement learning-based architecture to address the fault detection on body in white assembly processes is introduced in this paper. During the research were addressed: (i) generation of a random defect pattern database using a multi-physics variation modeling for multi-stage assembly systems; (ii) design and implementation of a fault pattern identification reinforcement learning-based architecture, combining neural network, genetic algorithm and Q-learning algorithms; and (iii) validation based on non-ideal sheet-metal parts case study generated by the Variation Response Method toolkit. Finally, a comparative study between the different topologies is done, highlighting the influence of the Q-learning in the default identification process.

Fractal assembly of micrometre-scale DNA origami arrays with arbitrary patterns.

Self-assembled DNA nanostructures enable nanometre-precise patterning that can be used to create programmable molecular machines and arrays of functional materials. DNA origami is particularly versatile in this context because each DNA strand in the origami nanostructure occupies a unique position and can serve as a uniquely addressable pixel. However, the scale of such structures has been limited to about 0.05 square micrometres, hindering applications that demand a larger layout and integration with more conventional patterning methods. Hierarchical multistage assembly of simple sets of tiles can in principle overcome this limitation, but so far has not been sufficiently robust to enable successful implementation of larger structures using DNA origami tiles. Here we show that by using simple local assembly rules that are modified and applied recursively throughout a hierarchical, multistage assembly process, a small and constant set of unique DNA strands can be used to create DNA origami arrays of increasing size and with arbitrary patterns. We illustrate this method, which we term 'fractal assembly', by producing DNA origami arrays with sizes of up to 0.5 square micrometres and with up to 8,704 pixels, allowing us to render images such as the Mona Lisa and a rooster. We find that self-assembly of the tiles into arrays is unaffected by changes in surface patterns on the tiles, and that the yield of the fractal assembly process corresponds to about 0.95m - 1 for arrays containing m tiles. When used in conjunction with a software tool that we developed that converts an arbitrary pattern into DNA sequences and experimental protocols, our assembly method is readily accessible and will facilitate the construction of sophisticated materials and devices with sizes similar to that of a bacterium using DNA nanostructures.

An assembly sensitivity analysis method based on state space model

PurposeThis paper aims to consider a problem of assembly sensitivity in a multi-station assembly process. The authors focus on the assembly process of aircrafts, which includes cabins and inertial navigation system (INSs), and establish the assembly process state space model for their assembly sensitivity research.Design/methodology/approachTo date, the process-related errors that cause large variations in key product characteristics remains one of the most critical research topics in assembly sensitivity analysis. This paper focuses on the unique challenges brought about by the multi-station system: a system-level model for characterizing the variation propagation in the entire process, and the necessity of describing the system response to variation inputs at both station-level and single fixture-level scales. State space representation is used to describe the propagation of variation in such a multi-station process, incorporating assembly process parameters such as fixture-locating layout at individual stations and station-to-station locating layout change.FindingsFollowing the sensitivity analysis in control theory, a group of hierarchical sensitivity indices is defined and expressed in terms of the system matrices in the state space model, which are determined by the given assembly process parameters.Originality/valueA case study of assembly sensitivity for a multi-station assembly process illustrates and validates the proposed methodology.

Rapid Response Diagnosis of Multi-stage Assembly Process with Compliant non-ideal Parts using Self-evolving Measurement System

This paper introduces the novel concept of self-evolving measurement system with the aim of rapidly identifying and localising defect patterns in multi-stage assembly systems with compliant non-ideal parts. This allows to enhance the level of diagnosability which cannot be achieved using fixed and static pre-determined measurement systems. The proposed methodology helps to identify and select new measurement points to increase the likelihood of isolating root causes of defects. This happens by automatically classifying defect patterns and associating them to critical key control characteristics. The methodology integrates supervised machine learning tools with first principle engineering simulations. It is based on the principle of pattern similarity, taking into account data generated by the self-evolving measurement system. The methodology is demonstrated and validated using the results of an automotive door assembly system.

Stream of Variation Modeling and Analysis for Compliant Composite Part Assembly— Part II: Multistation Processes

Part I of this paper (Zhang and Shi, 2015, “Stream of Variation Modeling and Analysis for Compliant Composite Part Assembly—Part I: Single-Station Processes,” ASME J. Manuf. Sci. Eng.,) has studied the variation modeling and analysis of compliant composite part assembly in a single-station process. In practice, multiple assembly stations are involved in assembling the final product. This paper aims to develop a variation propagation model for stream of variation analysis in a multistation assembly process for composite parts. This model takes into account major variation factors, including part manufacturing error (PME), fixture position error (FPE), and relocation-induced error (RIE). With the help of a finite element method (FEM), a state space model (SSM) is established to represent the relationships between the sources of variation and the final assembly variation. The developed methodology is illustrated by using a case study of three composite laminated plates assembled in a two-station assembly system. The validity of the developed SSM is verified by Monte Carlo simulation (MCS), which is implemented on the basis of FEM. The SSM provides a potential application for diagnosis of variation sources and variation reduction.