J. Patrick Spicer

Characterization of Ultrasonic Metal Welding by Correlating Online Sensor Signals With Weld Attributes

Online process monitoring in ultrasonic welding of automotive lithium-ion batteries is essential for robust and reliable battery pack assembly. Effective quality monitoring algorithms have been developed to identify out of control parts by applying purely statistical classification methods. However, such methods do not provide the deep physical understanding of the manufacturing process that is necessary to provide diagnostic capability when the process is out of control. The purpose of this study is to determine the physical correlation between ultrasonic welding signal features and the ultrasonic welding process conditions and ultimately joint performance. A deep understanding in these relationships will enable a significant reduction in production launch time and cost, improve process design for ultrasonic welding, and reduce operational downtime through advanced diagnostic methods. In this study, the fundamental physics behind the ultrasonic welding process is investigated using two process signals, weld power and horn displacement. Several online features are identified by examining those signals and their variations under abnormal process conditions. The joint quality is predicted by correlating such online features to weld attributes such as bond density and postweld thickness that directly impact the weld performance. This study provides a guideline for feature selection and advanced diagnostics to achieve a reliable online quality monitoring system in ultrasonic metal welding.

Tool Wear Monitoring for Ultrasonic Metal Welding of Lithium-Ion Batteries

This paper presents a tool wear monitoring framework for ultrasonic metal welding which has been used for lithium-ion battery manufacturing. Tool wear has a significant impact on joining quality. In addition, tool replacement, including horns and anvils, constitutes an important part of production costs. Therefore, a tool condition monitoring (TCM) system is highly desirable for ultrasonic metal welding. However, it is very challenging to develop a TCM system due to the complexity of tool surface geometry and a lack of thorough understanding on the wear mechanism. Here, we first characterize tool wear progression by comparing surface measurements obtained at different stages of tool wear, and then develop a monitoring algorithm using a quadratic classifier and features that are extracted from space and frequency domains of cross-sectional profiles on tool surfaces. The developed algorithm is validated using tool measurement data from a battery plant. [DOI: 10.1115/1.4031677]

Cost Modeling of Flexible Assembly Systems

Flexible assembly systems have emerged as a key manufacturing strategy in many industries, such as automotive, electronic components and computers, to respond to stronger market competition and greater product proliferation. As more companies install flexible assembly systems, design and planning decisions for such flexible systems have to be considered together with other factors besides market demand and competitive forces. This paper presents a cost analysis tool that estimates the cost of flexible assembly lines. Specifically, it provides the capability to study the production of a given set of products while evaluating the impact of different flexibility strategies on total assembly cost. A mathematical description of the model is given, followed by a case study demonstrating its application to strategic decisions during the early planning stages of assembly system design.Copyright

Simultaneous Scalable-Reconfigurable Manufacturing System Design and Inventory Control Policy Decision Making

Scalable reconfigurable manufacturing systems (scalable-RMS) consist of standardized modular equipment that can be quickly added or removed to adjust the production capacity. Each modular machine, referred to as a scalable reconfigurable machine tool (scalable-RMT), is composed of identical modules that can be added to, or removed from the machine depending on its required throughput. In previous work, conceptual scalable-RMTs have been described. Additional scalable-RMTs are presented in this paper to highlight the applicability of this concept in manufacturing. As an extension to existing scalable-RMS literature, this paper incorporates multiple products in the system configuration design. Specifically, this paper proposes an integer programming based iterative algorithm for finding the minimum cost configuration of a multi-product system. It is shown that the proposed algorithm converges to the optimal solution under the majority of practical conditions. Then, a mathematical formulation to minimize the system investment and operational costs in a multi-product scalable-RMS is presented. A numerical example compares the solution obtained using the traditional approach of determining the system design and then the inventory control policy versus the proposed simultaneous approach. It is concluded that the simultaneous approach yields significant improvement over the traditional (decoupled) approach.© 2006 ASME

Characterization of ultrasonic metal welding by correlating online sensor signals with weld attributes

Online process monitoring in ultrasonic welding of automotive lithium-ion batteries is essential for robust and reliable battery pack assembly. Effective quality monitoring algorithms have been developed to identify out of control parts by applying purely statistical classification methods. However, such methods do not provide the deep physical understanding of the manufacturing process that is necessary to provide diagnostic capability when the process is out of control. The purpose of this study is to determine the physical correlation between ultrasonic welding signal features and the ultrasonic welding process conditions and ultimately joint performance. A deep understanding in these relationships will enable a significant reduction in production launch time and cost, improve process design for ultrasonic welding, and reduce operational downtime through advanced diagnostic methods. In this study, the fundamental physics behind the ultrasonic welding process is investigated using two process signals, weld power and horn displacement. Several online features are identified by examining those signals and their variations under abnormal process conditions. The joint quality is predicted by correlating such online features to weld attributes such as bond density and post-weld thickness that directly impact the weld performance. This study provides a guideline for feature selection and advanced diagnostics to achieve a reliable online quality monitoring system in ultrasonic metal welding.Copyright

Clustering Methods for Defect Tracking in Order to Assess the Performance of a Porosity Inspection System

Surface porosity inspection is important for quality assurance of critical mating surfaces on machined components. An important metric for assessing the performance of an automated surface porosity inspection system is repeatability. Traditional gage repeatability analysis is well defined for dimensional measurements of machined part features. However, the analysis becomes more difficult for surface porosity inspection. This is because surface porosity appears in random sizes and in random locations. Repeatability analysis requires painstaking effort in tracking individual pores through repeated measurements. Therefore, this paper presents an automated approach for tracking porosity for the purpose of repeatability analysis. Two different algorithms are proposed and evaluated. The first is a tolerance based method that uses pre-specified tolerances to determine if pores should be grouped together. The second algorithm is similar to hierarchical agglomerative clustering, using a similarity matrix to store differences between cluster centroids. However, this algorithm uses a training period to determine when to stop clustering instead of continuing until all pores are in one cluster. Experimental results describe differences in the accuracy of both approaches and effort required to obtain a solution. The computation time required for the first method is much shorter than that of the second method. However, the first algorithm requires a-priori information to specify the tolerances, whereas the second algorithm requires no prior knowledge.Copyright