Quantitative Surface Quality Assessment of Car Outer Panels with a Virtual Light Room

Sheet Metal Forming


“Quantitative Surface Quality Assessment of Car Outer Panels with a Virtual Light Room” was published by ESI, Autaza & AEA for the International Body Congress in Liviona, in 2019.

Abstract. Perception is reality. A customers’ perception about the quality of a car is closely linked to the aspect of its outer panels and that reality translates into what motivates customers to choose one car over another. The smoothness of the panels, how much gap is visible and how that looks; these visual cues are considerations within a customer’s decision-making process. The process of choosing a new car is certainly subjective and varies from customer to customer, but OEMs agree that the visual appeal of the body of a car is a key factor. Building a car only to find surface imperfections results in huge costs in postproduction rectifying those perceived defects. The further into production these defects are found the more expensive it is to fix them. Some OEMs release cars into the buying cycle knowing that their engineering teams are still struggling to fix surface defects. There is a solution to this though. Numerical simulation. This action can and should be used to find surface defects very early in the design process. Surface quality assessment via numerical simulation is achieved by analysing different contours that relate stress or strain values. Other subjective criteria exist, but it is often very difficult to ascertain if a surface defect is severe enough to be detected by human eye. This paper describes a simulation methodology that, when used along with a software that reads surface light distortions and rates them, will give reliable results allowing engineers to fix problems before a car is built. This technology was developed using actual data supplied by a partnering OEM. Current examples will be presented, comparing parts “as designed” with parts “as manufactured”. After manufacturing parts will show variations caused by the stamping process such as skid marks, bumps, hollows, etc. All that can be accurately predicted by simulation.

Ing. Arthur Camanho, Ing. Harald Porzner (ESI Group, USA)
Ing. Renan Padovani (AUTAZA, Brazil)
Dr. Carlos Sakuramoto (AEA, Brazil)

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