Transmission Infrared Microscopy and Machine Learning Applied to the Forensic Examination of Original Automotive Paint

Alternate least squares (ALS) reconstructions of the infrared (IR) spectra of the individual layers from original automotive paint were analyzed using machine learning methods to improve both the accuracy and speed of a forensic automotive paint examination. Twenty-six original equipment manufacturer (OEM) paints from vehicles sold in North America between 2000 and 2006 served as a test bed to validate the ALS procedure developed in a previous study for the spectral reconstruction of each layer from IR line maps of cross-sectioned OEM paint samples. An examination of the IR spectra from an in-house library (collected with a high-pressure transmission diamond cell) and the ALS reconstructed IR spectra of the same paint samples (obtained at ambient pressure using an IR transmission microscope equipped with a BaF2 cell) showed large peak shifts (approximately 10 cm−1) with some vibrational modes in many samples comprising the cohort. These peak shifts are attributed to differences in the residual polarization of the IR beam of the transmission IR microscope and the IR spectrometer used to collect the in-house IR spectral library. To solve the problem of frequency shifts encountered with some vibrational modes, IR spectra from the in-house spectral library and the IR microscope were transformed using a correction algorithm previously developed by our laboratory to simulate ATR spectra collected on an iS-50 FT-IR spectrometer. Applying this correction algorithm to both the ALS reconstructed spectra and in-house IR library spectra, the large peak shifts previously encountered with some vibrational modes were successfully mitigated. Using machine learning methods to identify the manufacturer and the assembly plant of the vehicle from which the OEM paint sample originated, each of the twenty-six cross-sectioned automotive paint samples was correctly classified as to the “make” and model of the vehicle and was also matched to the correct paint sample in the in-house IR spectral library.

Investigating the reduction of BTEX in automotive paint sludge combined with biological sludge by vermicomposting process using Eisenia fetida

Background: Due to the fact that in the process of car painting in the automotive industry, sludge containing dangerous compounds of benzene, toluene, ethylbenzene, xylene which cannot be released into the environment without purification, is inevitably produced, this study was conducted to investigate the feasibility of removing BTEX (benzene, toluene, ethylbenzene, and xylene) from the paint sludge of Saipa Automotive Company using Eisenia fetida worms. Methods: This is an experimental study. First, mixtures with different proportions of sludge were prepared and loaded in suitable boxes. After preparing the desired sludge, their quantitative and qualitative characteristics were determined in terms of type and amount of BTEX, volatile materials, moisture content, and C/N ratio. Then, to check the changes in BTEX, sampling was performed on different days during 90 days. BTEX measurements were performed using GC-MS method (NIOSH Method 1501). Results: The results showed that in the best mixing ratio of sludge, the amount of benzene decreased from 3 mg to less than 0.01 mg in 30 days, toluene decreased from 1.5 mg to zero over a 45-day period, ethyl benzene was reduced from 7 mg to zero mg over 70 days, and xylene decreased from 18 mg to 0.9 mg over 90 days. In addition, in the same optimal mixing ratio, the amount of volatile organic matter, pH, and C/N ratio also had a decreasing trend in the vermicomposting process. Conclusion: According to the results, E. fetida worms are able to work in mixed sludge and have the ability to break down BTEX.

Unsettled Topics Concerning Coating Detection by LiDAR in Autonomous Vehicles

Autonomous vehicles (AVs) utilize multiple devices, like high-resolution cameras and radar sensors, to interpret the driving environment and achieve full autonomy. One of these instruments—the light detection and ranging (LiDAR) sensor—utilizes pulsed infrared (IR) light, typically at wavelengths of 905 nm or 1,550 nm, to calculate object distance and position. Exterior automotive paint covers an area larger than any other exterior material. Therefore, understanding how LiDAR wavelengths interact with vehicle coatings is extremely important for the safety of future automated driving technologies. Sensing technologies and materials are two different industries that have not directly interacted in the perception and system sense. With the new applications in the AV industry, multidisciplinary approaches need to be taken to ensure reliability and safety in the future. Unsettled Topics Concerning Coating Detection by LiDAR in Autonomous Vehicles provides a transversal view of different industry segments, from pigment and coating manufacturers to LiDAR components and vehicle system development and integration. The report includes a structured decomposition of the different variables and technologies involved.