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Metrology is one of the many applications of machine vision, which has the advantage that allows for the analyzing of a total production batch that leaves an assembly line without supposing a bottleneck. As a result, quality control become a priority in the inspection processes of industrial manufacturing. Due to the advancement of technology and the realizations of Industry 4.0, smart factories demand high precision and accuracy in the measurements and inspection of industrial products. Machine vision technology provide image-based inspection and analysis for such demanding applications. With the use of software, sensors, cameras and robot guidance, such integrated systems can be realized. Machine vision highlights a growing trend in industrial systems. As camera sensors become smarter, the quality of data produced offers accuracy into the systems operations. This thesis is a study of the typical vision system pipeline, in the different phases, necessary to achieve optimal inspection in an industrial operation. The first step is the study of the light alignment to monitor and achieve an optimal light alignment system, in order to eliminate the effects of misalignment. The algorithm was tested with a not-optimal system to ascertain its efficiency and effectiveness. In the second phase, a deep study of the calibration process is carried out to address the effect of different parameters as the camera focus among others. Endocentric and telecentric lenses are used in the image acquisition and a comparative analysis is obtained using a multivariable statistical analysis to study the influence of each parameter in the calibration process: camera focus, exposure time, calibration plate tilt and number of images used. In the third proposal, an object alignment algorithm is developed to address the challenge of object alignment during a measurement process. Object plane alignment is key point for achieving good repeatability of object measurements in all orientations. A complete study of the impact of every single pipeline phase is carried out in the proposals validation chapter. Finally, a complete 2D machine vision application is developed to determine the precise measurement of gears, at subpixel level, with the potential to improve quality control, reduce downtime and optimize the inspection process. The calibrated vision system was verified by measuring a ground-truth sample gear in a Coordinate Measuring Machine (CMM), using the parameter generated as the nominal value of the outer diameter. A methodical study of the global uncertainty associated with the process is carried out in order to know better the admissible zone for accepting gears. This thesis try to reach the optimal values in every single phase of the pipeline in order to improve the accuracy of the inspection. The different studies and algorithms developed in this thesis show that it is worthwhile to invest on achieving the optimal values during the different phases of an industrial inspection process.

Precisely positioned optical lenses are currently required for many highly repetitive mechanics and applications. Thus the need for micron-scale repetition between opto-mechanical units is evident, especially in industrial manufacturing and medical breakthroughs. In this thesis, a novel optical metrology system is proposed, designed, and built whose purpose is to precisely locate the center of a mechanical fixture and then to assemble a plano-convex optical lens into the located position of the fixture. Center location specifications up to ±3 µm decenter and ±0.001° tilting accuracy are required. Nine precisely positioned lenses and fixtures were built with eight units passing the requirements with a repetitive standard deviation of ±0.15 µm or less. The assembled units show satisfactory results.

The form verification of manufactured parts is a process composed of a set of operations that are expensive and yet add no value to the product. Yet, the resources used to inspect the parts add a small but significant amount of noise that can affect the outcome of the process. For this reason, this research provides guidelines to effectively perform the inspection process by suggesting new mathematical models and approaches that can be used for the creation of a decision support system that can assist in the verification of the accuracy of machined parts.

Coordinate metrology employs a discrete sampling of data points to verify the size, form, orientation, and location of features contained in parts. Usually data points are collected intuitively with simple schemes that attempt to cover the surface of the features as best as possible. Data fitting methods are used to determine the zones of deviations about the ideal feature. A multitude of linear and nonlinear optimization procedures and the least squares method have been used to estimate the tolerance zone for straightness, flatness, circularity, and cylindricity. More complex forms such as conicity have been largely ignored in the literature, in spite of the sufficient need to inspect them in parts such as nozzles and tapered rollers in bearings.