Characterizing hidden solder joint shapes is essential for electronics reliability. Active thermography is a methodology to identify hidden defects inside an object by means of surface abnormal thermal response after applying a heat flux. This research focused on understanding, modeling, and predicting hidden solder joint shapes. An experimental model based on active thermography was used to understand how the solder joint shapes affect the surface thermal response (grand average cooling rate or GACR) of electronic multi cover PCB assemblies. Next, a numerical model simulated the active thermography technique, investigated technique limitations and extended technique applicability to characterize hidden solder joint shapes. Finally, a prediction model determined the optimum active thermography conditions to achieve an adequate hidden solder joint shape characterization. The experimental model determined that solder joint shape plays a higher role for visible than for hidden solder joints in the GACR; however, a MANOVA analysis proved that hidden solder joint shapes are significantly different when describe by the GACR. An artificial neural networks classifier proved that the distances between experimental solder joint shapes GACR must be larger than 0.12 to achieve 85% of accuracy classifying. The numerical model achieved minimum agreements of 95.27% and 86.64%, with the experimental temperatures and GACRs at the center of the PCB assembly top cover, respectively. The parametric analysis proved that solder joint shape discriminability is directly proportional to heat flux, but inversely proportional to covers number and heating time. In addition, the parametric analysis determined that active thermography is limited to five covers to discriminate among hidden solder joint shapes. A prediction model was developed based on the parametric numerical data to determine the appropriate amount of energy to discriminate among solder joint shapes for up to five covers. The degree of agreement between the prediction model and the experimental model was determined to be within a 90.6% for one and two covers. The prediction model is limited to only three solder joints, but these research principles can be applied to generate more realistic prediction models for large scale electronic assemblies like ball grid array assemblies having as much as 600 solder joints.

The goal of the research conducted for this master's thesis is to understand if active thermography is a suitable technique to detect (identify) and measure (approximate depth and or size) defects in additive manufacturing (AM) processes. Although other non-destructive measurement techniques exist, active thermography is an attractive option for AM applications because of the short measurement times that could be implemented between each layer of a print, and because of the relatively inexpensive equipment required. However, pulse thermography is typically applied to detect larger defects (>1 mm) in materials with high thermal conductivity. It was uncertain if active thermography was sensitive enough to detect the small defects (μm) commonly introduced during AM. Defects of this size are common in AM, and their presence significantly impacts the mechanical properties of the final part. For this reason, the detection limits of active thermography in common AM materials were investigated. Numerical models were created to simulate the heat transfer during active thermography in AM structures (polymer and stainless steel) with defects of varying size. The models included non-ideal conditions such as spectral in-depth absorption of the irradiative pulse and free convection from the object's surface. The spectral properties of acrylonitrile butadiene styrene (ABS), polylactic acid (PLA), and polyamide 12 (PA 12) were measured (see chapter 2) and used in the numerical models. The numerical data indicates that active thermography is sensitive enough to detect the existence of defects smaller than 100 μm in AM materials (see chapter 3). Furthermore, it demonstrates that the defect aspect ratio (defect diameter divided by defect depth) for which traditional 1D thermography models may be used to approximate the depth of defects in 3D systems is approximately 6 (see chapter 4). In addition, the depth of defects with lower aspect ratios (~4) may also be approximated with relatively low error (~10% error). Non-ideal systems (those with convection and spectral in-depth absorption) were simulated, and figures are provided which facilitate the approximation of defect depth using simple, ideal thermography models. Active thermography has shown potential as being an efficient technique for detecting and measuring small defects common in AM.

This diploma thesis is focused on using infrared camera within methods of active thermography. The first part of work is an introduction to the passive and active thermography; and about four basic methods of active thermography: step heating themography, Lock-in thermography and pulse phase thermography. The second part is a proposal of experimental working place. There are also presented results of practical implementation pulse and pulse phase methods tested for application of detection of subsurface defect.