S.J. Hu

Stream-of-Variation Theory for Automotive Body Assembly

Abstract Manufacturing systems usually consist of processes and machines in a multi-leveled hierarchy. As a result, dimensional variation in the final product is accumulated as the product moves along the manufacturing system. This paper discusses the prediction and diagnosis of dimensional variation in a multi-leveled automotive body assembly system. By combining engineering structural models with statistical analysis, the evolution of the variability and stiffness characteristics of sheet metal parts is studied in terms of assembly configurations, i.e., serial or parallel. The diagnosability of serial and parallel assembly systems is evaluated. These results are integrated with correlation clustering to form a complete diagnostic strategy. Predicting and diagnosing variation in a multi-leveled manufacturing system constitute the two aspects of what we call the “Stream-of-Variation Theory.”

Convertibility Measures for Manufacturing Systems

Abstract With increased consumer demands for a wider variety of products In changeable, unpredicted quantities, manufacturing system responsiveness has become increasingly Important for Industry competitiveness. Manufacturers need systems that can be rapidly adjusted with regard to both functionality and throughput capacity over the lifetime of the system. Convertibility is defined as the capability of a system to adjust production functionality, or change from one product to another. End-users of manufacturing systems are struggling with the Issue of how to measure and quantify convertibility. Metrics for convertibility are proposed in this paper so that different manufacturing systems can be compared with respect to this area of performance. These metrics are based on assessments of the configuration itself, and the system components such as machines and material handling devices. Metrics for quantifying convertibility are useful for comparing system configurations during the early phases of design, with out requiring detailed product or process plan Information.

Automated generation of assembly system-design solutions

Any assembly system-design problem which consists of generating system configurations and assignment of tasks to the stations has a set of solutions. The number of feasible solutions may be staggering, even for products consisting of a relatively small number of parts assembled on a small number of stations. As the size of the product and the assembly system grow, it may become difficult to develop a complete set of design solutions for in-depth analysis. Here an automated method is described by which the complete sets of system configurations and assembly sequences may be generated, and feasible solutions, consisting of a matched element from each set, can be rapidly derived. These solutions may then be tested for various performance metrics, some of which may not be expressed mathematically. In designing assembly systems, the layout of stations and the assignment of assembly tasks to these stations are important design problems. This paper proposes a set of algorithms to quickly generate the configurations of the assembly system and assign tasks to the configurations. Once the matching of tasks to the configurations is complete, the performance of these various design alternatives, such as productivity, can be evaluated to allow selection of configurations with the best performance.

Assembly Strategies for Remanufacturing Systems With Variable Quality Returns

This paper studies optimal policy for modular product reassembly within a remanufacturing setting where a firm receives product returns with variable quality and reassembles products of multiple classes to customer orders. High-quality modules are allowed to substitute for low-quality modules during reassembly to provide the remanufacturing system with flexibility such that shortage in lower quality modules can be smoothed out by higher quality module inventories. We formulate the problem as a Markov decision process and characterize the structure of the optimal control policy. In particular, we show that the optimal reassembly and substitution follow a state-dependent threshold-based control policy. We also establish the structural properties of the thresholds. Using numerical experimentation, we study how system performance is influenced by key cost parameters including unit holding cost, unit assembly cost and shortage penalty cost. Finally, we compare the optimal policy with an exhaustive reassembly policy and show that there is great benefit in module substitution and threshold-based assembly control.

Productivity of synchronized serial production lines with flexible reserve capacity

The configuration of a manufacturing system greatly influences its productivity because many configurations have multiple productive states. Flexible reserve production capacity is one method to increase the number of productive states. Reserve production capacity is the provision of non-dedicated standby machines in parallel to the main production line that are capable of performing any operation in the production line. Standby machines, like buffers, isolate failures in the production line, permitting production to continue. This paper develops models to predict the productivity of pure serial and parallel-serial production lines with reserve capacity. Combinatorial mathematics is applied to determine the magnitude of production and the probability of occurrence of system states. Productivity improvements are quantified and the productivity equivalency of reserve capacity to buffers is demonstrated.

Designing Productive Manufacturing Systems without Buffers

Abstract Modem industrial practice is to minimize work-in-process in order to eliminate inventory-carrying costs and quickly detect quality problems. Reduced work-in-process results from eliminating in-process buffers between operations in serial lines, but is accompanied by decreased system efficiency. Inventories are created before system expansion in order to offset production lost during construction. Furthermore, serial line expansion implies doubling line output. In reconfigurable manufacturing systems, new configurations that have not yet been fully explored by industry can be used to compensate for loss of buffered system isolation failure, creation of inventories, and step-size production expansion. Numerical models are applied to predict productivity and explicitly show the equivalency of alternative configurations to buffered serial transfer lines. Parallel-serial configurations as well as the newly proposed reserve capacity configurations are examined.

Sequence Planning to Minimize Complexity in Mixed-Model Assembly Lines

Sequence planning is an important problem in assembly line design. It is to determine the order of assembly tasks to be performed sequentially. Significant research has been done to find good sequences based on various criteria, such as process time, investment cost, and product quality. This paper discusses the selection of optimal sequences based on complexity introduced by product variety in mixed-model assembly line. The complexity was defined as operator choice complexity, which indirectly measures the human performance in making choices, such as selecting parts, tools, fixtures, and assembly procedures in a multi-product, multi-stage, manual assembly environment. The complexity measure and its model for assembly lines have been developed in an earlier paper by the authors. According to the complexity models developed, assembly sequence determines the directions in which complexity flows. Thus proper assembly sequence planning can reduce complexity. However, due to the difficulty of handling the directions of complexity flows in optimization, a transformed network flow model is formulated and solved based on dynamic programming. Methodologies developed in this paper extend the previous work on modeling complexity, and provide solution strategies for assembly sequence planning to minimize complexity.

Concurrent design of product families and assembly systems

In order to gain competitive advantage, manufacturers require cost effective methods for developing a variety of products within short time periods. Product families, reconfigurable assembly systems and concurrent engineering are frequently used to achieve this desired cost effective and rapid supply of product variety. The independent development of methodologies for product family design and assembly system design has led to a sequential approach to the design of product families and assembly systems. However, the designs of product families and assembly systems are interdependent and efficiencies can be gained through their concurrent design. There are no quantitative concurrent engineering techniques that address the problem of the concurrent design of product families and assembly systems. In this paper, a non-linear integer programming formulation for the concurrent design of a product family and assembly system is introduced. The problem is solved with a genetic algorithm. An example is used to demonstrate the advantage of the concurrent approach to product family and assembly system design over the existing sequential methodology.Copyright

Functional Morphing in Multistage Manufacturing and Its Applications in High-Definition Metrology-Based Process Control

In multistage manufacturing processes equipped with high-definition metrology (HDM), part surface quality characteristics can be observed to change or “morph” from stage to stage. Such part surface variation propagations are caused by the physical processes, part attributes, and the interaction between stages. Previous research on variation propagation modeling focuses on part dimensional quality using discrete key product characteristics or vectors which have limitations in analyzing complex surface variation patterns contained in the HDM data. This paper proposes a new concept of functional morphing to characterize the surface changes and applies it to process control in high-precision manufacturing. Unlike conventional morphing algorithms that focus on transformations between geometries only, functional morphing integrates process physical insights into the geometric mappings, thus characterizing the complex HDM data patterns in physically meaningful ways. Specifically, a functional free form deformation approach including forward and backward mappings is developed to extract mapping functions between manufacturing stages to enable surface variation propagation analysis. The forward mapping function allows for accurate interstage adjustment that introduces shape deformation upstream to compensate for the end-of-line errors. The backward mapping function can predict surfaces at intermediate stages based on end-of-line measurements, leading to a cost-effective interstage process monitoring scheme. The interstage monitoring can also ensure the repeatability of a process controlled by the interstage compensation algorithm. The developed monitoring and adjustment methods are demonstrated via a case study of a two-stage machining process. Other potential applications of functional morphing such as process tolerance design are also discussed.

Assembly System Reconfiguration Planning Using Genetic Algorithm

Due to increased competition, the rate at which manufacturers introduce new product families to the market is increasing. However, the cost of changing manufacturing facilities to produce new product families can outweigh the benefits obtained from increased revenue. Reconfigurable Manufacturing Systems (RMSs) have been proposed as a cost effective strategy for manufacturing product families. Although methods for measuring RMS scalability and convertibility exist, there is a lack of methods for obtaining reconfiguration plans for assembly systems. This paper introduces assembly system reconfiguration planning (ASRP) as method to obtain reconfiguration plans for assembly systems. A genetic algorithm is developed for solving the ASRP problem.© 2008 ASME