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Optimizing Galvanic Process: Wet Surface Estimation for Small Metal Parts to Avoid Material Waste

Part of the Lecture Notes in Mechanical Engineering book series (LNME)


The optimization of a galvanic process is a crucial task for many manufacturers in the field of electro-deposition industry. This is true for companies operating in the high fashion field, in which expensive materials are used and reducing material waste is crucial. In this paper, the estimation of the wet surface of small metal parts is treated. In fact, considering a single piece, the amount of material required to guarantee a desired plate thickness is directly proportional to its outer surface. Starting with a rapid overview on other methods to come up with this task, the attention in principally oriented to surface estimation by means of optical scanning. A preliminary test session has been carried out and two main issues arose. The first one is related to resolution and accuracy: due to pieces small dimensions and details, high performances are required to achieve valid results. The second and principal issue is related to the high reflectivity of pieces, even before electroplating. With the aim of avoiding the use of matting paint (which is difficult to remove), the attention has been focused on commercial solution dedicated to jewellery and dentistry fields. Three devices (based on white and blue LED structured light) have been tested on high reflective and specular pieces. From the analyses, only the one based on blue LED technology was able to retrieve high reflective surfaces without matting. Minor issues arose in case of specular surface. The device has been considered suitable for the task.


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Correspondence to Luca Puggelli .

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Puggelli, L., Carfagni, M., Furferi, R., Governi, L. (2023). Optimizing Galvanic Process: Wet Surface Estimation for Small Metal Parts to Avoid Material Waste. In: Gerbino, S., Lanzotti, A., Martorelli, M., Mirálbes Buil, R., Rizzi, C., Roucoules, L. (eds) Advances on Mechanics, Design Engineering and Manufacturing IV. JCM 2022. Lecture Notes in Mechanical Engineering. Springer, Cham.

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  • Print ISBN: 978-3-031-15927-5

  • Online ISBN: 978-3-031-15928-2

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