During the last years, the use of Portable Coordinate Measuring Machines (PCMMs) in industry has increased considerably, mostly due to their flexibility for accomplishing in-line measuring tasks as well as their reduced costs and operational advantages as compared to traditional coordinate measuring machines (CMMs). However, their operation has a significant drawback derived from the techniques applied in the verification and optimization procedures of their kinematic parameters. These techniques are based on the capture of data with the measuring instrument from a calibrated gauge object, fixed successively in various positions so that most of the instrument measuring volume is covered, which results in time-consuming, tedious and expensive verification procedures. In this work the mechanical design of an indexed metrology platform (IMP) is presented. The aim of the IMP is to increase the final accuracy and to radically simplify the calibration, identification and verification of geometrical parameter procedures of PCMMs. The IMP allows us to fix the calibrated gauge object and move the measuring instrument in such a way that it is possible to cover most of the instrument working volume, reducing the time and operator fatigue to carry out these types of procedures.
The demand for faster and more reliable measuring tasks for the control and quality assurance of modern production systems has created new challenges for the field of coordinate metrology. Thus, the search for new solutions in coordinate metrology systems and the need for the development of existing ones still persists. One example of such a system is the portable coordinate measuring machine (PCMM), the use of which in industry has considerably increased in recent years, mostly due to its flexibility for accomplishing in-line measuring tasks as well as its reduced cost and operational advantages compared to traditional coordinate measuring machines. Nevertheless, PCMMs have a significant drawback derived from the techniques applied in the verification and optimization procedures of their kinematic parameters. These techniques are based on the capture of data with the measuring instrument from a calibrated gauge object, fixed successively in various positions so that most of the instrument measuring volume is covered, which results in time-consuming, tedious and expensive verification and optimization procedures. In this work the mathematical calibration procedure of a capacitive sensor-based indexed metrology platform (IMP) is presented. This calibration procedure is based on the readings and geometric features of six capacitive sensors and their targets with nanometer resolution. The final goal of the IMP calibration procedure is to optimize the geometric features of the capacitive sensors and their targets in order to use the optimized data in the verification procedures of PCMMs.
La metodología Lean Six Sigma es popularmente conocida en el giro industrial como una forma de reducir la variación en los procesos productivos, donde, mediante el uso de herramientas de las filosofías Esbelta y Seis Sigma, se busca mejorar la calidad de los productos o servicios al menor coste posible. Si bien existen diferentes estrategias, Lean Six Sigma se ha vuelto sumamente llamativa en los últimos años por la eficiencia y magnitud de sus resultados. En este trabajo se presenta la aplicación de dicha metodología en una empresa dedicada a la fabricación de artículos de construcción, donde, el enfoque se ha dirigido al área de páneles modulares con el fin de reducir el alto porcentaje de desperdicios presentados. Durante la aplicación se logró identificar diferentes variables que poseían un efecto significativo en el producto final, de esta manera, fue posible determinar las mejores herramientas que apoyaran al proceso productivo para mejorarlo y finalmente controlarlo. Entre estas herramientas se encuentran dos estudios R&R, los cuales, identificaron que el sistema de medición poseía un 81% de confiabilidad y confirmaron su incremento al 98% después de las mejoras aplicadas. Asimismo, se desarrolló un diseño de experimentos que estableció un nuevo estándar para disminuir el principal defecto presentado en el producto, logrando obtener un modelo matemático capaz de predecir respuestas a nuevas observaciones en un 90.45%. Finalmente, después de 3 meses de observación se obtuvo una reducción del 66% de los DPMO, lo que confirmó la efectividad de la metodología y la magnitud de sus beneficios.
This article describes a method called 'overlap' to perform two main tasks. The first is the performance evaluation of a coordinate measurement system (CMS) with a measuring range larger than the length of artifacts. The second is the calibration of one-dimensional artifacts using a CMS with a measuring range smaller than the length of the artifacts. Examples of one-dimensional artifacts are ball bars, step gauges, or scale bars with lines or circle marks. For the first task, the method uses the translation between errors in a line inside the volume of the CMS. For the second task, it uses the rotation and translation between 3D coordinates of elements belonging to the artifact. Validation of the method for the first task is done by evaluating the performance of a coordinate measuring machine (CMM) in parallel lines to two of its axes. For this, we use a step gauge and a ball bar covering the range of the CMM's lines under evaluation not using overlap. Later, we evaluate the same lines using a segment of the same step gauge and ball bar but this time using overlap. For the second task, the validation is similar; we use a ball bar whose length falls inside the measurement range of a high-accuracy CMM, then the ball bar can be measured in its entire length with the CMM not using overlap and later in segments using overlap.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.