Process-oriented tolerancing using the extended stream of variation model

Current works on process-oriented tolerancing for multi-station manufacturing processes (MMPs) have been mainly focused on allocating fixture tolerances to ensure part quality specifications at a minimum manufacturing cost. Some works have also included fixture maintenance policies into the tolerance allocation problem since they are related to both manufacturing cost and final part qual- ity. However, there is a lack of incorporation of other factors that lead to increase of manufacturing cost and degrade of product quality, such as cutting-tool wear and machine-tool thermal state. The allocation of the admissible values of these process variables may be critical due to their impact on cutting-tool replacement and quality loss costs. In this paper, the process-oriented tolerancing is ex- panded based on the recently developed, extended stream of variation (SoV) model, which explicitly represents the influence of machining process variables in the variation propagation along MMPs. In addition, the probability distribution functions (pdf) for some machining process variables are ana- lyzed, and a procedure to derive part quality constraints according to GD&T specifications is also shown. With this modeling capability extension, a complete process-oriented tolerancing can be con- ducted, reaching a real minimum manufacturing cost. In order to demonstrate the advantage of the proposed methodology over a conventional method, a case study is analyzed in detail

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

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

Derivation and application of the stream of variation model to the manufacture of ceramic floor tiles

One of the main problems in the manufacture of floor tiles is the dimensional variability of the ceramic product, which leads to the product having to be classified into different dimensional qualities with an increase in cost. In this paper we propose a novel way of modelling the dimensional variability of ceramic floor tiles by the adaptation of the Stream of Variation model. The proposed methodology and its potential applicability contributes to the integration of process knowledge in the ceramic tile industry and allow tile manufacturers have a new methodology for process improvement, variation reduction and dimensional control

Using an MBSE approach for automation control system selection in long steel products hot rolling plants

Abstract: Automation systems in long steel products hot rolling plants are prone to performance failures with the potential of serious negative impact on the business. The selection process of these automation systems therefore requires careful consideration of various selection factors to maximize plant performance. The need was therefore identified to investigate the use of a suitable management approach to guide engineering automation teams in the long steel products hot rolling plants in the selection of automation systems. At the core is the need for an in-depth understanding of the issues surrounding distributed and hierarchical automation systems in long steel products plants. This includes identifying the challenges during the selection process, using sound engineering management principles. Current automation selection techniques were investigated through a survey, interviews and a case study. It was then decided to use a Model Based Systems Engineering (MBSE) approach, which utilises systems engineering principles together with digital technology to create models to simplify the understanding of complex problems and relationships. This was then used to develop a management framework for automation systems selection in support of the business case of long steel products hot rolling plants

Extension of the Stream-of-Variation Model for General-Purpose Workholding Devices: Vices and Three-Jaw Chucks

Nowadays, advanced manufacturing models, such as the stream-of-variation (SoV) model, have been successfully applied to derive the complex relationships between fixturing, manufacturing, and datum errors throughout a multistage machining process. However, the current development of the SoV model is still based on 3-2-1 fixturing schemes, and although some improvements have been done, e.g., N-2-1 fixtures, the effect of general workholding systems, such as bench vices or three-jaw chucks, has not yet been included into the model. This article presents the extension of the SoV model to include fixture and datum errors considering both bench vices and three-jaw chucks as fixturing devices in multistage machining processes. The model includes different workholding configurations, and it is shown how to include the workholding accuracy to estimate part quality. The extended SoV model is validated in a three-stage machining process by both machining experimentation and CAD simulations

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

Tooling adjustment strategy for acceptable product quality in assembly processes

This paper develops an approach to minimize the number of process tooling adjustments and deliver an acceptable fraction of non-conforming products based on given product quality specification limits in assembly processes. A linear model is developed to describe the relationships between product quality and process tooling locating positions. Based on the model, the process mean shifts of tooling locating positions are estimated for both deterministic and stochastic cases by using the least-square estimation or linear mixed model estimation, respectively. A simultaneous confidence interval is obtained to construct the estimation region of a process mean shift under the given false alarm rate. Furthermore, a tooling adjustment strategy is proposed to determine when the process adjustment is essentially needed in order to ensure an acceptable fraction of non-conforming units based on the given product quality specification limits. Finally, a case study is conducted to illustrate the developed methodology by using a real-world autobody assembly process. Copyright © 2010 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/78245/1/1128_ftp.pd

Scalable design synthesis for automotive assembly system

Frequent product model changes have become a characteristic feature in new product development and modern manufacturing. This has triggered a number of requirements such as shortening new product development time and production ramp-up time with simultaneous reduction of avoidable engineering changes and overall vehicle development cost. One of the most significant challenges when reducing new model development lead time is the large number of engineering changes, that are triggered by failures during production ramp-up stage but are unseen during design. In order to reduce engineering changes during ramp-up stage and also increase Right-First-Time development rate, there is a critical demand for improving quality of integrated product and production system design solutions. Currently, this is obtained by carrying out design synthesis which focuses on design optimization driven by computer simulation and/or physical experimentation. The design synthesis depends on the quality of the used surrogate models, which integrate critical product variables, (also known as Key Product Characteristics (KPCs)), with key process variables (Key Control Characteristics (KCCs)). However, a major limitation of currently existing surrogate models, used in design synthesis, is that these simply approximate underlying KPC-KCC relations with any deviation between the actual and predicted KPC assumed to be a simple random error with constant variance. Such an assumption raises major challenges in obtaining accurate design solutions for a number of manufacturing processes when: (1) KPCs are deterministic and non-linearity is due to interactions between process variables (KCCs) as is frequently the case in fixture design for assembly processes with compliant parts; (2) KPC stochasticity is either independent of (homo-skedastic) or dependent on (hetero-skedastic) on process variables (KCCs) and there is lack of physics-based models to confirm these behaviour; as can be commonly observed in case of laser joining processes used for automotive sheet metal parts; and, (3) there are large number of KCCs potentially affecting a KPC and dimensionality reduction is required to identify few critical KCCs as commonly required for diagnosis and design adjustment for unwanted dimensional variations of the KPC. This thesis proposes a generic Scalable Design Synthesis framework which involves the development of novel surrogate models which can address a varying scale of the KPC-KCC interrelations as indicated in the aforementioned three challenges. The proposed Scalable Design Synthesis framework is developed through three interlinked approaches addressing each aforementioned challenge, respectively: i. Scalable surrogate model development for deterministic non-linearity of KPCs characterized by varying number of local maximas and minimas. Application: Fixture layout optimization for assembly processes with compliant parts. This is accomplished in this thesis via (1) Greedy Polynomial Kriging (GPK), a novel approach for developing Kriging-based surrogate models for deterministic KPCs focusing on maximization of predictive accuracy on unseen test samples; and, (2) Optimal Multi Response Adaptive Sampling (OMRAS) a novel method of accelerating the convergence of multiple surrogate models to desired accuracy levels using the same training sample of KCCs. GPK surrogate models are then used for fixture layout optimization for assembly with multiple sheet metal parts. ii. Scalable surrogate model development for stochasticity characterized by unknown homo-skedastic or hetero-skedastic behaviour of KPCs. Application: In-process laser joining processes monitoring and in-process joint quality evaluation. Scalable surrogate model-driven joining process parameters selection, addressing stochasticity in KPC-KCC relations, is developed. A generic surrogate modelling methodology is proposed to identify and characterize underlying homo- and hetero-skedastic behaviour in KPCs from experimental data. This is achieved by (1) identifying a Polynomial Feature Selection (PFS) driven best-fitting linear model of the KPC; (2) detection of hetero-skedasticity in the linear model; and, (3) enhancement of the linear model upon identification of hetero-skedasticity. The proposed surrogate models estimate the joining KPCs such as weld penetration, weld seam width etc. in Remote Laser Welding (RLW) and their variance as a function of KCCs such as gap between welded parts, welding speed etc. in RLW. This information is then used to identify process window in KCC design space and compute joining process acceptance rate. iii. Scalable surrogate model development for high dimensionality of KCCs. Application: Corrective action of product failures triggered by dimensional variations in KPCs. Scalable surrogate model-driven corrective action is proposed to address efficient diagnosis and design adjustment of unwanted dimensional variations in KPCs. This is realized via (1) PFS to address high dimensionality of KCCs and identify a few critical ones closely related to the KPC of interest; and (2) surrogate modelling of the KPC in terms of the few critical KCCs identified by PFS; and, (3) two-step design adjustment of KCCs which applies the surrogate models to determine optimal nominal adjustment and tolerance reallocation of the critical KCCs to minimize production of faulty dimensions. All the aforementioned methodologies are demonstrated through the use of industrial case studies. Comparison of the proposed methods with design synthesis existing for the applications discussed in this thesis, indicate that scalable surrogate models can be utilized as key enablers to conduct accurate design optimization with minimal understanding of the underlying complex KPC-KCC relations by the user. The proposed surrogate model-based Scalable Design Synthesis framework is expected to leverage and complement existing computer simulation/physical experimentation methods to develop fast and accurate solutions for integrated product and production system design

Shape variation modelling, analysis and statistical control for assembly system with compliant parts

Modern competitive market demands frequent change in product variety, increased production volume and shorten product/process change over time. These market requirements point towards development of key enabling technologies (KETs) to shorten product and process development cycle, improved production quality and reduced time-to-launch. One of the critical prerequisite to develop the aforementioned KETs is efficient and accurate modelling of product and process dimensional errors. It is especially critical for assembly processes with compliant parts as used in automotive body, appliance or wing and fuselage assemblies. Currently, the assembly process is designed under the assumption of ideal (nominal) products and then check by using variation simulation analysis (VSA). However, the VSA simulations are oversimplified as they are unable to accurately model or predict the effects of geometric and dimensional variations of compliant parts, as well as variations of key characteristics related to fixturing and joining process. This results in product failures and/or reduced quality due to un-modelled interactions in assembly process. Therefore, modelling and prediction of the geometric shape errors of complex sheet metal parts are of tremendous importance for many industrial applications. Further, as production yield and product quality are determined for production volume of real parts, thus not only shape errors but also shape variation model is required for robust assembly system development. Currently, parts shape variation can be measured during production by using recently introduced non-contact gauges which are fast, in-line and can capture entire part surface information. However, current applications of non-contact scanners are limited to single part inspection or reverse engineering applications and cannot be used for monitoring and statistical process control of shape variation. Further, the product shape variation can be reduced through appropriate assembly fixture design. Current approaches for assembly fixture design seldom consider shape variation of production parts during assembly process which result in poor quality and yield. To address the aforementioned challenges, this thesis proposes the following two enablers focused on modelling of shape errors and shape variation of compliant parts applicable during assembly process design phase as well as production phase: (i) modelling and characterisation of shape errors of individual compliant part with capabilities to quantify fabrication errors at part level; and (ii) modelling and characterisation of shape variation of a batch of compliant parts with capabilities to quantify the shape variation at production level. The first enabler focuses on shape errors modelling and characterisation which includes developing a functional data analysis model for identification and characterisation of real part shape errors that can link design (CAD model) with manufacturing (shape errors). A new functional data analysis model, named Geometric Modal Analysis (GMA), is proposed to extract dominant shape error xixmodes from the fabricated part measurement data. This model is used to decompose shape errors of 3D sheet metal part into orthogonal shape error modes which can be used for product and process interactions. Further, the enabler can be used for statistical process control to monitor shape quality; fabrication process mapping and diagnosis; geometric dimensioning and tolerancing simulation with free form shape errors; or compact storage of shape information. The second enabler aims to model and characterise shape variation of a batch of compliant parts by extending the GMA approach. The developed functional model called Statistical Geometric Modal Analysis (SGMA) represents the statistical shape variation through modal characteristics and quantifies shape variation of a batch of sheet metal parts a single or a few composite parts. The composite part(s) represent major error modes induced by the production process. The SGMA model, further, can be utilised for assembly fixture optimisation, tolerance analysis and synthesis. Further, these two enablers can be applied for monitoring and reduction of shape variation from assembly process by developing: (a) efficient statistical process control technique (based on enabler ‘i’) to monitor part shape variation utilising the surface information captured using non-contact scanners; and (b) efficient assembly fixture layout optimisation technique (based on enabler ‘ii’) to obtain improved quality products considering shape variation of production parts. Therefore, this thesis proposes the following two applications: The first application focuses on statistical process control of part shape variation using surface data captured by in-process or off-line scanners as Cloud-of-Points (CoPs). The methodology involves obtaining reduced set of statistically uncorrelated and independent variables from CoPs (utilising GMA method) which are then used to develop integrated single bivariate T2-Q monitoring chart. The joint probability density estimation using non-parametric Kernel Density Estimator (KDE) has enhanced sensitivity to detect part shape variation. The control chart helps speedy detection of part shape errors including global or local shape defects. The second application determines optimal fixture layout considering production batch of compliant sheet metal parts. Fixtures control the position and orientation of parts in an assembly process and thus significantly contribute to process capability that determines production yield and product quality. A new approach is proposed to improve the probability of joining feasibility index by determining an N-2-1 fixture layout optimised for a production batch. The SGMA method has been utilised for fixture layout optimisation considering a batch of compliant sheet metal parts. All the above developed methodologies have been validated and verified with industrial case studies of automotive sheet metal door assembly process. Further, they are compared with state-of-the-art methodologies to highlight the boarder impact of the research work to meet the increasing market requirements such as improved in-line quality and increased productivity