Manufacturing variation models in multi-station machining systems

In product design and quality improvement fields, the development of reliable 3D machining variation models for multi-station machining processes is a key issue to estimate the resulting geometrical and dimensional quality of manufactured parts, generate robust process plans, eliminate downstream manufacturing problems, and reduce ramp-up times. In the literature, two main 3D machining variation models have been studied: the stream of variation model, oriented to product quality improvement (fault diagnosis, process planning evaluation and selection, etc.), and the model of the manufactured part, oriented to product and manufacturing design activities (manufacturing and product tolerance analysis and synthesis). This paper reviews the fundamentals of each model and describes step by step how to derive them using a simple case study. The paper analyzes both models and compares their main characteristics and applications. A discussion about the drawbacks and limitations of each model and some potential research lines in this field are also presented

A methodology for data-driven adjustment of variation propagation models in multistage manufacturing processes

In the current paradigm of Zero Defect Manufacturing, it is essential to obtain mathematical models that express the propagation of manufacturing deviations along Multistage Manufacturing Processes (MMPs). Linear physical-based models such as the Stream of Variation (SoV) model are commonly used, but its accuracy may be limited when applied to MMPs with a large amount of stages, mainly because of the modeling errors at each stage that are accumulated downstream. In this paper we propose a methodology to calibrate the SoV model using data from the inspection stations and prior engineering-based knowledge. The data used for calibration does not contain information about the sources of variation, and they must be estimated as part of the model adjustment procedure. The proposed methodology consists of a recursive algorithm that minimizes the difference between the sample covariance of the measured Key Product Characteristic (KPC) deviations and its estimation, which is a function of a variation propagation matrix and the covariance of the deviation of the variation sources. To solve the problem with standard convex optimization tools, Schur complements and Taylor series linearizations are applied. The output of the algorithm is an adjusted model, which consists of a variation propagation matrix and an estimation of the aforementioned variation source covariance. In order to validate the performance of the algorithm, a simulated case study is analyzed. The results, based on Monte Carlo simulations, show that the estimation errors of the KPC deviation covariances are proportional to the measurement noise variance and inversely proportional to the number of processed parts that have been used to train the algorithm, similarly to other process estimators in the literature.Funding for open access charge: CRUE-Universitat Jaume

Modeling variation in multi-station compliant assembly using parametric space envelope

Non-rigid compliant parts are widely used in industries. One of the biggest challenges facing industries is geometric variation management of these compliant parts which can directly impact product quality and functionality. Existing rigid body-based variation modeling approaches are not suitable for compliant assembly while finite element analysis based methods have the disadvantage of requiring heavy computation efforts and detailed design information which is unavailable during preliminary design phase. Hence, this paper develops a novel methodology to evaluate geometric variation propagation in multi-station compliant assembly based on parametric space envelope (i.e. variation tool constructed from parametric curves). Three sources of variation: location-led positional variation, assembly deformation-induced variation and station transition caused variation are analyzed. In this study, geometric variations are modeled indirectly through a compact set of boundary control points. Compared with existing methods where geometric variation is modeled by tracking key feature points on the manufacturing part, the proposed approach brings many benefits. It can handle arbitrary complex compliant part, and it lowers computation requirement in many real applications. The method is illustrated and verified through an industrial case study on a multi-station compliant panel assembly. The developed method provides industries a new way to manage geometric variation from compliant assembly

Reliability Modeling and Optimization Strategy for Manufacturing System Based on RQR Chain

Accurate and dynamic reliability modeling for the running manufacturing system is the prerequisite to implement preventive maintenance. However, existing studies could not output the reliability value in real time because their abandonment of the quality inspection data originated in the operation process of manufacturing system. Therefore, this paper presents an approach to model the manufacturing system reliability dynamically based on their operation data of process quality and output data of product reliability. Firstly, on the basis of importance explanation of the quality variations in manufacturing process as the linkage for the manufacturing system reliability and product inherent reliability, the RQR chain which could represent the relationships between them is put forward, and the product qualified probability is proposed to quantify the impacts of quality variation in manufacturing process on the reliability of manufacturing system further. Secondly, the impact of qualified probability on the product inherent reliability is expounded, and the modeling approach of manufacturing system reliability based on the qualified probability is presented. Thirdly, the preventive maintenance optimization strategy for manufacturing system driven by the loss of manufacturing quality variation is proposed. Finally, the validity of the proposed approach is verified by the reliability analysis and optimization example of engine cover manufacturing system

Integrated Tolerance and Fixture Layout Design for Compliant Sheet Metal Assemblies

Part tolerances and fixture layouts are two pivotal factors in the geometrical quality of\ua0a compliant assembly. The independent design and optimization of these factors for compliant\ua0assemblies have been thoroughly studied. However, this paper presents the dependency of these\ua0factors and, consequently, the demand for an integrated design of them. A method is developed\ua0in order to address this issue by utilizing compliant variation simulation tools and evolutionary\ua0optimization algorithms. Thereby, integrated and non-integrated optimization of the tolerances and\ua0fixture layouts are conducted for an industrial sample case. The objective of this optimization is\ua0defined as minimizing the production cost while fulfilling the geometrical requirements. The results\ua0evidence the superiority of the integrated approach to the non-integrated in terms of the production\ua0cost and geometrical quality of the assemblies

Synthesis of Products, Processes and Control for Dimensional Quality in Reconfigurable Assembly Systems.

Reconfigurable systems and tools have given manufacturers the possibility to quickly adapt to changes in the market place. Such systems allow the production of different products with simple and quick reconfiguration. Another advantage of reconfigurable systems is that the accuracy of the tools provides a unique opportunity to compensate errors and deviations as they occur along the manufacturing system, hence improving product quality. This dissertation deals with the design of products, processes and controllers to enhance dimensional quality of products produced in reconfigurable assembly processes. The successful synthesis of these topics will lead to new levels of quality and responsiveness. Fundamental research has been conducted in dimensional control of reconfigurable multistation assembly systems. This includes three topics related to the design of products, processes, and controls. These are: o Development of feedforward controllers: Feedforward controllers allow deviation compensation on a part-by-part basis using reconfigurable tools. The control actions are obtained through the combination of multistation assembly models, in-line measurements (used to measure deviations along the process), and the characteristics and requirements of products/processes, in an optimization framework. Simulation results show that the proposed control approach is effective on reducing variation. o Optimal selection and distribution of actuators in multistation assembly processes: The availability of reconfigurable tools in the process enables error correction; however, it is too expensive to install at every location. The selection and distribution of the actuators is focused on cost effectively reducing variation in multistation assembly processes. Simulations results prove that dimensional variation could be significantly reduced through an appropriate distribution of actuators. o Robust fixture design for a product family assembled in a reconfigurable multistation line: The assembly of a product family in a reconfigurable line demands fixtures sharing across products. The sharing impacts the products robustness to fixture variation due to frequent systems reconfiguration and tradeoffs made in the design of fixtures to accommodate the family in the single system. A robust fixture layout for a product family is achieved by reducing the combined sensitivity of the whole family to fixture variation and considering product and process constraints.Ph.D.Mechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/57657/2/leiv_1.pd

System-level Quality Planning and Diagnosis for Complex Multistage Manufacturing Processes.

The performance of a multistage manufacturing process (MMP) can be measured by quality, productivity and cost. All these measures are influenced by the variation of the key product characteristics (KPC’s). To remain competitiveness, variation of KPC’s should be reduced to ensure efficient delivery of quality products. However, the unprecedentedly high requirements on quality make variation reduction a very challenging problem. To reduce KPC variation, massive data are generated and collected from different phases of product realization, including quantitative data and qualitative data. The heterogeneous data poses great challenges to traditional quality assurance methodologies, which emphasize monitoring of manufacturing processes but provide limited diagnostic information. Taking advantage of readily available data, this research focuses on system-level methodology for effective quality assurance of MMP’s in the following aspects: (i) A mathematical variation propagation model is developed to describe the process induced variation and its propagation along multiple manufacturing stages. The generic formulation makes it capable to model a wide variety of processes where 3-D dimensional variation is of interest. The modeling concept and techniques can be extended and applied in early phases of product realization to effectively evaluate product and process design alternatives. (ii) A quality assured setup planning methodology is developed to address the quality assurance in the process design phase of product realization. Setup planning is formulized as an optimal sequential decision making problem and is solved based on analytical evaluation. This research creates the potential for future works on concurrent development of system-level setup and fixture planning. The setup planning results can be further utilized for process diagnosis in the manufacturing phase of product realization. (iii) An engineering-driven factor analysis methodology is developed to diagnose an MMP based on qualitative rather than quantitative representation of product/process interactions. By using the qualitative indicator vectors to direct the estimation of true spatial patterns from multivariate measurement data, the variation sources are identified. The diagnostic results are robust to unknown process changes. The proposed methodologies represent the initial research efforts in a general framework of unified methodology for quality assurance of MMP’s. Based on them, future research directions are identified and discussed.Ph.D.Mechanical Engineering and Industrial and Operations EnginUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/60659/1/jliuzz_1.pd

Object shape error response using Bayesian 3D convolutional neural networks for assembly systems with compliant parts

The paper proposes a novel Object Shape Error Response (OSER) approach to estimate the dimensional and geometric variation of assembled products and then, relate, these to process parameters, which can be interpreted as root causes (RC) of the object shape defects. The OSER approach leverages Bayesian 3D-Convolutional Neural Networks integrated with Computer-Aided Engineering (CAE) simulations for RC isolation. Compared with existing methods, the proposed approach (i)addresses a novel problem of applying deep learning for object shape error identification instead of object detection; (ii)overcomes fundamental performance limitations of current linear approaches for Root Cause Analysis (RCA) that cannot be used on point cloud data; and, (iii)provides capabilities for unsolved challenges such as ill-conditioning, fault-multiplicity, RC isolation with uncertainty quantification and learning at design phase when no measurement data is available. Comprehensive benchmarking with machine learning models demonstrates superior performance with R2=0.98 and MAE=0.05 mm, thus improving RCA capabilities by 29%