Industrial dimensional assessment presents instances in which early control is exerted among “warm” (approx. 600 ${}^{\circ }$C) pieces. Early control saves resources, as defective processes are timely stopped and corrected. Existing literature is devoid of dimensional assessment on warm workpieces. In response to this absence, this manuscript presents the implementation and results of an optical system which performs in-line dimensional inspection of revolution warm workpieces singled out from the (forming) process. Our system can automatically measure, in less than 60 s, the circular runout of warm revolution workpieces. Such a delay would be 20 times longer if cool-downs were required. Off-line comparison of the runout of T-temperature workpieces (27 ${}^{\circ }$C ≤ T ≤ 560 ${}^{\circ }$C) shows a maximum difference of 0.1 mm with respect to standard CMM (Coordinate Measurement Machine) runout of cold workpieces (27 ${}^{\circ }$C), for workpieces as long as 160 mm. Such a difference is acceptable for the forging process in which the system is deployed. The test results show no correlation between the temperature and the runout of the workpiece at such level of uncertainty. A prior-to-operation Analysis of Variance (ANOVA) test validates the repeatability and reproducibility (R&R) of our measurement system. In-line assessment of warm workpieces fills a gap in manufacturing processes where early detection of dimensional misfits compensates for the precision loss of the vision system. The integrated in-line system reduces the number of defective workpieces by $95\%$ .

This paper describes a method for calibrating multi camera and multi laser 3D triangulation systems, particularly for those using Scheimpflug adapters. Under this configuration, the focus plane of the camera is located at the laser plane, making it difficult to use traditional calibration methods, such as chessboard pattern-based strategies. Our method uses a conical calibration object whose intersections with the laser planes generate stepped line patterns that can be used to calculate the camera-laser homographies. The calibration object has been designed to calibrate scanners for revolving surfaces, but it can be easily extended to linear setups. The experiments carried out show that the proposed system has a precision of 0.1 mm.

In the recent years, due to the technological advancements brought by Industry 4.0, flexible in-line quality control processes that analyze 100% of produced parts targeting Zero Defect Manufacturing have become a reality. The electronics industry in general and the manufacturing of PCBAs in particular have also adopted this paradigm, where quality inspection applies especially to the most critical and error-prone feature: the solder joints between PCBs and components. When the literature related to AI-based solder joint quality inspection is analyzed, we find a lack of applications in real production lines, since most of them are limited to lab scenarios. Therefore, we present a deep learning-based method for solder joint defect detection, which is deployed in a real manufacturing line. The mentioned system is trained using the database we captured using an ad hoc robotic system with onboard front and lateral cameras designed also in this work, which we also describe in detail. The deployment of the defect detector in the real production line is done using that same robotic system too, ensuring the coherence and compatibility between the training data and the real inference scenario.