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首页> 外文会议>Annual Symposium on Quantitative Nondestructive Evaluation; 19980719-24; Snowbird,UT(US) >ESTIMATION OF BOND LINE DIMENSIONS IN ADHERED METAL JOINTS USING ULTRASONIC LAMB WAVES: DEVELOPMENTS USING ARTIFICIAL NEURAL NETWORKS
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ESTIMATION OF BOND LINE DIMENSIONS IN ADHERED METAL JOINTS USING ULTRASONIC LAMB WAVES: DEVELOPMENTS USING ARTIFICIAL NEURAL NETWORKS

机译：使用超声波羔羊波估算粘附金属接头的粘结线尺寸：使用人工神经网络的发展

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摘要

The motivation for this work was to develop a scheme for the nondestructive quality assurance (QA) examination of adhered joints in experimental automotive body shell assemblies. The use of adhesives on automotive structures has potential for providing flexibility of design, including the use of mixed materials, reductions in vehicle weight, and improved impact and vibration performance; manufacturing cost'may also be reduced. A requirement for adhered joints is that the adhesive layer dimensions are within specified ranges and this leads to a requirement to determine bond dimensions non destructively for the purposes of QA. However, in many automotive assemblies direct access to the joint region is not possible due to structure geometry and cladding. Ultrasonic Lamb waves can provide remote access to otherwise inaccessible joints, the idea being to excite such waves in the metal adherend on one side of the joint, and to receive them from a second adherend on the other side of the joint; comparison of Lamb wave signals that have traversed the joint with signals that have propagated along plane sheet could lead to a means to determine joint dimensions. The physics of Lamb wave interactions with typical automotive joints is complex and generally not amenable to inverse solutions that would yield joint dimensions directly. In this work we have applied artificial neural networks (ANNs) to relate patterns in Lamb wave signals to joint dimensions. After training, tests of the networks on adhered samples not used in training showed that the networks could recognise key bond dimensions in more than 90% of trials. Simplification of the networks (minimisation) gave improved interpolation performance in the recognition of bond dimensions not included in network training. The weights associated with trained networks could be used to identify salient features in received Lamb wave signals in a manner that gave an indication of the physics underlying Lamb wave propagation across bonded assemblies. This aspect will be discussed in a companion paper in this volume.
机译：这项工作的动机是为实验性汽车车身外壳总成中的粘附接头制定无损质量保证（QA）检查方案。在汽车结构上使用粘合剂具有提供设计灵活性的潜力，包括使用混合材料，减轻汽车重量以及改善冲击和振动性能；制造成本”也可以降低。对于粘合接头的要求是粘合层的尺寸在规定的范围内，这导致要求无损地确定粘合尺寸以进行质量检查。但是，在许多汽车组件中，由于结构的几何形状和覆层，无法直接进入接头区域。超声波兰姆波可以远程访问原本无法到达的关节，其目的是在关节一侧的金属被粘物中激发这种波，并从关节另一侧的第二被粘物接收它们。将穿过接头的兰姆波信号与沿平面传播的信号进行比较可能会导致确定接头尺寸的方法。 Lamb波与典型汽车关节的相互作用的物理过程很复杂，通常不适合直接产生关节尺寸的逆解。在这项工作中，我们应用了人工神经网络（ANN）将兰姆波信号中的模式与关节尺寸相关联。训练后，对网络中未使用的粘附样本的测试表明，在超过90％的试验中，网络可以识别关键的键维。网络的简化（最小化）在识别网络训练中未包含的键维时提高了插值性能。与训练有素的网络关联的权重可以用来标识接收到的兰姆波信号中的显着特征，其方式可以指示出兰姆波在结合组件上传播的物理基础。这方面将在本卷的配套文件中进行讨论。

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