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A stream-of variation analysis (SOVA) model for 3D rigid body assemblies in single station is developed. Both product and process information such as part and fixture locating errors are integrated in the model. The model represents a linear relationship of the variations between Key Product Characteristics (KPCs) and Key Control Characteristics (KCCs). The generic modeling procedure and framework are provided, which involves: (1) an assembly graph (AG) to represent the kinematical constraints among parts and fixtures; (2) a unified method to transform all constraints (mating interface and fixture locators etc.) into a 3-2-1 locating scheme; and (3) a 3D rigid model for variation flow in a single station. The generality of the model is achieved by formulating all these constraints with a unified generalized fixture model. Thus, the new model accommodates various types of assemblies. This model provides a building block for complex multi station assembly model, in which the inter-station interactions are taken into account. The model has been verified by using Monte Carlo (MC) simulation and a standardized industrial software. It provides the basis for variation control through tolerance design analysis, synthesis and diagnosis in manufacturing systems.

This paper addresses sensor characterization to detect variations in part-to-part gap and weld penetration depth using photodiode-based signals during Remote Laser Welding (RLW) of battery tab connectors. Photodiode-based monitoring has been implemented largely for structural welds due to its relatively low cost and ease of automation. However, research in sensor characterization, monitoring and diagnosis of weld defects during joining of battery tab connectors is at an infancy and results are inconclusive. Motivated by the high variability during the welding process of dissimilar metallic thin foils, this paper aims to characterize the signals generated by a photodiode-based sensor to determine whether variations in weld quality can be isolated and diagnosed. Photodiode-based signals were collected during RLW of copper-to-steel thin-foil lap joint (Ni-plated copper 300 μm to Ni-plated steel 300 μm). The presented methodology is based on the evaluation of the energy intensity and scatter level of the signals. The energy intensity gives information about the amount of radiation emitted during the welding process, and the scatter level is associated with the accumulated and un-controlled variations. Findings indicated that part-to-part gap variations can be diagnosed by observing the step-change in the plasma signal, with no significant contribution given by the back-reflection. Results further suggested that over-penetration corresponds to significant increment of the scatter level in the sensor signals. Opportunities for automatic isolation and diagnosis of defective welds based on supervised machine learning are discussed.

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