Infrared Thermography for Investigation of Surface Quality in Dry Finish Turning of Ti6Al4V

The machining of titanium alloys always raises issues because of their peculiar chemical and physical characteristics as compared to traditional steel or aluminum alloys. A proper selection of parameters and their monitoring during the cutting operation makes it possible to minimize the surface roughness and cutting force. In this experimental study, infrared thermography was used as a control parameter of the surface roughness of Ti6A4V in dry finish turning. An analysis of variance was carried out to determine the effect of the main cutting parameters (cutting speed and feed rate) on the surface roughness and cutting temperature. In the examined range of the machining parameters, cutting speed and feed were found to have a primary effect on the surface roughness of the machined parts. Cutting speed also significantly affected the temperature of the cutting region, while feed was of second order. Higher cutting speeds and intermediate feed values gave the best surface roughness. A regression analysis defined some models to relate the cutting temperature and surface roughness to the machining parameters. Infrared thermography demonstrated that the cutting temperature could be related to roughness.

Towards the Automation of Infrared Thermography Inspections for Industrial Maintenance Applications

The maintenance of industrial equipment extends its useful life, improves its efficiency, reduces the number of failures, and increases the safety of its use. This study proposes a methodology to develop a predictive maintenance tool based on infrared thermographic measures capable of anticipating failures in industrial equipment. The thermal response of selected equipment in normal operation and in controlled induced anomalous operation was analyzed. The characterization of these situations enabled the development of a machine learning system capable of predicting malfunctions. Different options within the available conventional machine learning techniques were analyzed, assessed, and finally selected for electronic equipment maintenance activities. This study provides advances towards the robust application of machine learning combined with infrared thermography and augmented reality for maintenance applications of industrial equipment. The predictive maintenance system finally selected enables automatic quick hand-held thermal inspections using 3D object detection and a pose estimation algorithm, making predictions with an accuracy of 94% at an inference time of 0.006 s.

Non-Invasive Assessment of Mild Stress-Induced Hyperthermia by Infrared Thermography in Laboratory Mice

Stress-induced hyperthermia (SIH) is a physiological response to acute stressors in mammals, shown as an increase in core body temperature, with redirection of blood flow from the periphery to vital organs. Typical temperature assessment methods for rodents are invasive and can themselves elicit SIH, affecting the readout. Infrared thermography (IRT) is a promising non-invasive alternative, if shown to accurately identify and quantify SIH. We used in-house developed software ThermoLabAnimal 2.0 to automatically detect and segment different body regions, to assess mean body (Tbody) and mean tail (Ttail) surface temperatures by IRT, along with temperature (Tsc) assessed by reading of subcutaneously implanted PIT-tags, during handling-induced stress of pair-housed C57BL/6J and BALB/cByJ mice of both sexes (N = 68). SIH was assessed during 10 days of daily handling (DH) performed twice per day, weekly voluntary interaction tests (VIT) and an elevated plus maze (EPM) at the end. To assess the discrimination value of IRT, we compared SIH between tail-picked and tunnel-handled animals, and between mice receiving an anxiolytic drug or vehicle prior to the EPM. During a 30 to 60 second stress exposure, Tsc and Tbody increased significantly (p < 0.001), while Ttail (p < 0.01) decreased. We did not find handling-related differences. Within each cage, mice tested last consistently showed significantly higher (p < 0.001) Tsc and Tbody and lower (p < 0.001) Ttail than mice tested first, possibly due to higher anticipatory stress in the latter. Diazepam-treated mice showed lower Tbody and Tsc, consistent with reduced anxiety. In conclusion, our results suggest that IRT can identify and quantify stress in mice, either as a stand-alone parameter or complementary to other methods.

Application of Infrared Thermography in an Adequate Reusability Analysis of Photovoltaic Modules Affected by Hail

Infrared thermography, in the analysis of photovoltaic (PV) power plants, is a mature technical discipline. In the event of a hailstorm that leaves the PV system without the support of the power grid (and a significant portion of the generation potential), thermography is the easiest way to determine the condition of the modules and revive the existing system with the available resources. This paper presents research conducted on a 30 kW part of a 420 kW PV power plant, and demonstrates the procedure for inspecting visually correct modules that have suffered from a major natural disaster. The severity of the disaster is shown by the fact that only 14% of the PV modules at the test site remained intact. Following the recommendations of the standard IEC TS 62446-3, a thermographic analysis was performed. The thermographic analysis was preceded by an analysis of the I-V curve, which was presented in detail using two characteristic modules as examples. I-V curve measurements are necessary to relate the measured values of the radiation and the measured contact temperature of the module to the thermal patterns. The analysis concluded that soiled modules must be cleaned, regardless of the degree of soiling. The test results clearly indicated defective module elements that would result in a safety violation if reused. The research shows that the validity criterion defined on the basis of the analysis of the reference module can be supplemented, but can also be replaced by a statistical analysis of several modules. The comparison between the thermographic analysis and the visual inspection clearly confirmed thermography as a complementary method for testing PV-s.

A Review of Infrared Thermography for Delamination Detection on Infrastructures and Buildings

This paper provides a comprehensive review on the use of infrared thermography to detect delamination on infrastructures and buildings. Approximately 200 pieces of relevant literature were evaluated, and their findings were summarized. The factors affecting the accuracy and detectability of infrared thermography were consolidated and discussed. Necessary measures to effectively capture latent defects at the early stage of delamination before crack formation were investigated. The results of this study could be used as the benchmarks for setting standardized testing criteria as well as for comparison of results for future works on the use of infrared thermography for detection of delamination on infrastructures and buildings.