Optimized adhesive application
Source abstract
Abstract
In the manufacturing industry, the process of applying adhesive to join two substrates can be challenging. It requires precise placement of adhesive so that it is distributed in the desired pattern, ensuring a strong bond between the substrates. Overfilling is a common practice to ensure that the bond is not underfilled after the substrates are pressed together. However, this approach leads to unnecessary adhesive waste and can result in additional cleaning work. To address these issues, an optimization algorithm has been developed that can compute an initial adhesive pattern to flow into any desired geometry. The algorithm is based on a transient squeeze flow simulation, which iteratively removes fluid material as it flows out of the target bond domain. For example, to obtain a square bond geometry after a parallel squeezing process, the adhesive needs to be initially applied in a star shape. However, the algorithm is highly adaptable and can be used for many desired bond geometries as shown in this paper. The results are independent of the mesh resolution and fluid flow law, thus eliminating the need for complex rheological fluid characterization. The algorithm requires only two input parameters, namely the target bond geometry and the degree of compression. This highly simplifies the adaption of the proposed optimization method for manufacturers, and their design requirements can be considered with ease. The results are reliable and consistent despite the simulation’s complexity. Overall, this new optimization methodology represents a significant advance in adhesive application technology, eliminating the need of overfilling and thus making the manufacturing process more efficient, sustainable and cost-effective.
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Topology Optimization in Engineering · Innovations in Concrete and Construction Materials · Composite Structure Analysis and Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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