Near optimal design of fixture layouts in multi-station assembly processes

This dissertation presents a methodology for the near optimal design of fixture layouts in multi-station assembly processes. An optimal fixture layout improves the robustness of a fixture system, reduces product variability and leads to manufacturing cost reduction. Three key aspects of the multi-station fixture layout design are addressed: a multi-station variation propagation model, a quantitative measure of fixture design, and an effective and efficient optimization algorithm. Multi-station design may have high dimensions of design space, which can contain a lot of local optima. In this dissertation, I investigated two algorithms for optimal fixture layout designs. The first algorithm is an exchange algorithm, which was originally developed in the research of optimal experimental designs. I revised the exchange routine so that it can remarkably reduce the computing time without sacrificing the optimal values. The second algorithm uses data-mining methods such as clustering and classification. It appears that the data-mining method can find valuable design selection rules that can in turn help to locate the optimal design efficiently. Compared with other non-linear optimization algorithms such as the simplex search method, simulated annealing, genetic algorithm, the data-mining method performs the best and the revised exchange algorithm performs comparably to simulated annealing, but better than the others. A four-station assembly process for a sport utility vehicle (SUV) side frame is used throughout the dissertation to illustrate the relevant concepts and the resulting methodology

A Genetic Algorithm for Fixture Synthesis and Variation

Concepts in manufacturing such as CIMS (Computer Integrated Manufacturing Systems), JIT (Just In Time), Lean Production, Virtual Manufacturing, and Flexible Fixturing have been proposed to meet the fundamental requirements of manufacturing - decrease the cost and satisfy the needs of customers. Fast fixture generation and fixture reusability are essential in the current manufacturing environment. The dissertation focuses on the models, methods, and algorithms for fixture synthesis and variation that satisfy the functional requirements specified by on-site industrial engineers. With the reusability of a fixture base combined with variation of other fixture components, fixture configuration can be rapidly adapted and accommodated to the new workpiece. The dissertation presents methods and algorithms for fixture base synthesis, which directly result in fixture reusability. Optimization functions are derived based on engineering requirements due to the mass production nature of automotive parts. Specific optimization algorithms are developed and their complexities, compared to other alternatives, are comprehensively evaluated according to different optimization functions. The fixture variation and reusability provide an engineering tool to rapidly generate and validate fixtures in production planning stage. It applies scientific reasoning methodology in combination with best knowledge of fixture designs, which heavily relies on designers\u27 manufacturing knowledge and experience. It also provides means to bridge the gap between CAD and CAM integration and therefore reduces the new product and production development cycle time and cost while maintaining the quality of fixtures

Optimization for finite element modeling of electronic components under dynamic loaDing

Usage of electronic components in the U.S. ARMY applications is becoming more challenging due to their usage in harsh environments. Experimental verification of these components is expensive and it can yield information about specific locations only. This research outlines the finite element modeling methodology for these electronic components that are subjected to high acceleration loads that occur over extremely short time such as impact, gun firing and blast events. Due to their miniature size these finite element models are computationally expensive. An optimization engine was presented to have an efficient analysis procedure that provides a combination of accuracy, computational speed and modeling simplicity. This research also involves experimental testing of the electronic components mounted on the circuit boards. Testing was conducted at different strain levels in order to study the behavior of boards. Finite element models were developed for these tests and compared with experimental results

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

Individualizing assembly processes for geometric quality improvement

Dimensional deviations are a consequence of the mass production of parts. These deviations can be controlled by tightening production tolerances. However, this solution is not always desired because it usually increases production costs. The availability of massive amounts of data about products and automatized production has opened new opportunities to improve products\u27 geometrical quality by individualizing the assembly process. This individualization can be conducted through several techniques, including selective assembly, locator adjustments, weld sequence optimization, and clamping sequence optimization in a smart assembly line for spot-welded sheet metal assemblies. This study focuses on two techniques of individualizing the assembly process, selective assembly, and individualized locator adjustments in assembly fixtures. The existing studies and applications of these methods are reviewed, and the research gaps are defined. The previous applications of selective assembly are limited to linear and rigid assemblies. This study develops the application of selective assembly for sheet metal assemblies. This research addresses another research gap regarding the selective assembly of sheet metals by reducing the calculation cost associated with this technique. This study also develops a new locator adjustment method. This method utilizes scanned geometries of mating parts to predict the required adjustments. Afterward, a method for individualized adjustments is also developed. Considering applied and residual stresses during the assembly process as constraints is another contribution of this research to locator adjustments. These methods are applied to three industrial sample cases and the results evaluated. The results illustrate that individualization in locator adjustments can increase geometrical quality improvements three to four times.Accumulation of the potential improvements from both techniques in a smart assembly line is also evaluated in this study. The results indicate that combining the techniques may not increase the geometrical quality significantly relative to using only individualized locator adjustments.A crucial factor in the achievable improvements through individualization is the utilized assembly fixture layout. This study develops a method of designing the optimal fixture layout for sheet metal assemblies. Different design and production strategies are investigated to acquire the maximum potential for geometrical improvements through individualization in self-adjusting smart assembly lines

An on-demand fixture manufacturing cell for mass customisation production systems.

Master of Science in Engineering. University of KwaZulu-Natal, Durban, 2017.Increased demand for customised products has given rise to the research of mass customisation production systems. Customised products exhibit geometric differences that render the use of standard fixtures impractical. Fixtures must be configured or custom-manufactured according to the unique requirements of each product. Reconfigurable modular fixtures have emerged as a cost-effective solution to this problem. Customised fixtures must be made available to a mass customisation production system as rapidly as parts are manufactured. Scheduling the creation/modification of these fixtures must now be treated together with the production scheduling of parts on machines. Scheduling and optimisation of such a problem in this context was found to be a unique avenue of research. An on-demand Fixture Manufacturing Cell (FxMC) that resides within a mass customisation production system was developed. This allowed fixtures to be created or reconfigured on-demand in a cellular manufacturing environment, according to the scheduling of the customised parts to be processed. The concept required the research and development of such a cell, together with the optimisation modelling and simulation of this cell in an appropriate manufacturing environment. The research included the conceptualisation of a fixture manufacturing cell in a mass customisation production system. A proof-of-concept of the cell was assembled and automated in the laboratory. A three-stage optimisation method was developed to model and optimise the scheduling of the cell in the manufacturing environment. This included clustering of parts to fixtures; optimal scheduling of those parts on those fixtures; and a Mixed Integer Linear Programming (MILP) model to optimally synchronise the fixture manufacturing cell with the part processing cell. A heuristic was developed to solve the MILP problem much faster and for much larger problem sizes – producing good, feasible solutions. These problems were modelled and tested in MATLAB®. The cell was simulated and tested in AnyLogic®. The research topic is beneficial to mass customisation production systems, where the use of reconfigurable modular fixtures in the manufacturing process cannot be optimised with conventional scheduling approaches. The results showed that the model optimally minimised the total idle time of the production schedule; the heuristic also provided good, feasible solutions to those problems. The concept of the on-demand fixture manufacturing cell was found to be capable of facilitating the manufacture of customised products

AES-EPO study program, volume II Final study report

Packaging, machine organization, error detection, and fabrication and test in determining solution to long-term and time-critical reliability of Apollo command module guidance-control compute

DNSS: Dual-Normal-Space Sampling for 3-D ICP Registration

Rigid registration is a fundamental process in many applications that require alignment of different datasets. Iterative closest point (ICP) is a widely used algorithm that iteratively finds point correspondences and updates the rigid transformation. One of the key variants of ICP to its success is the selection of points, which is directly related to the convergence and robustness of the ICP algorithm. Besides uniform sampling, there are a number of normal-based and feature-based approaches that consider normal, curvature, and/or other signals in the point selection. Among them, normal-space sampling (NSS) is one of the most popular techniques due to its simplicity and low computational cost. The rationale of NSS is to sample enough constraints to determine all the components of transformation, but this paper finds that NSS actually can constrain the translational normal space only. This paper extends the fundamental idea of NSS and proposes Dual NSS (DNSS) to sample points in both translational and rotational normal spaces. Compared with NSS, this approach has similar simplicity and efficiency without any need of additional information, but has a much better effectiveness. Experimental results show that DNSS can outperform the normal-based and feature-based methods in terms of convergence and robustness. For example, DNSS can achieve convergence from an orthogonal initial position while no other methods can achieve. Note to Practitioners-ICP is commonly used to align different data to a same coordination system. While NSS is often used to speed up the alignment process by down-sampling the data uniformly in the normal space. The implementation of NSS only has three steps: 1) construct a set of buckets in the normal-space; 2) put all points of the data into buckets based on their normal direction; and 3) uniformly pick points from all the buckets until the desired number of points is selected. The algorithm is simple and fast, so that it is still the common practice. However, the weakness of NSS comes from the reason that it cannot handle rotational uncertainties. In this paper, a new algorithm called DNSS is developed to constrain both translation and rotation at the same time by introducing a dual-normal space. With a new definition of the normal space, the algorithm complexity of DNSS is the same as that of NSS, and it can be readily implemented in all types of application that are currently using ICP. The experimental results show that DNSS has better efficiency, quality, and reliability than both normal-based and feature-based methods have