Investigation of Functional Dependency between the Characteristics of the Machining Process and Flatness Error Measured on a CMM

Numerous studies have shown that the choice of measurement strategy (number and position of measurement points) when measuring form error on a coordinate-measuring machine (CMM) depends on the characteristics of the machining process which was used to machine the examined surface. The accuracy of form error assessment is the primary goal of verification procedures and accuracy is considered perfect only in the case of the ideal verification operator. Since the ideal verification operator in the “point-by-point” measuring mode is almost never used in practice, the aim of this study was to examine a relationship which had not been examined in earlier studies, namely how the machining process, surface roughness and a reduced number of points in the measurement strategy affect the accuracy of flatness error assessment. The research included four most common cutting processes applied to flat surfaces divided into nine different classes of roughness. In order to determine functional dependency between the observed input variables and the output, statistical regression models and neuro-fuzzy logic (artificial intelligence tool) were used. The analyses confirmed the significance of all three input parameters, with surface roughness being the most significant one. Both the statistical regression models and neuro-fuzzy models proved to be adequate, matching the experimental results. The use of these models makes it possible to determine flatness error measured on a CMM if input variables considered in the paper are known.

Efforts To Reduce Downgrade Of Steel Pipes In The Production Process Using The HEART Method

Pt X is a company that produces steel pipe of various shapes and size. In the production procces not only using machines but also using humans as operators. So in this case humans play an important role in maintaining the quality of production. The purpose of this study is to analyze the human error probability with Human Error Assessment and Reduction Technique method (HEART). HEART is a method designed as a fast and simple human reliability assessment in quantifying the risk of human error. From this research, it was found that 3 tasks had a high HEP value which caused the decline in pipe quality during the production process, namely task 3.2, 4.1 and task 3.4. Which has the highest HEP value on task 3.2, namely setting the machine with a value of 0,7680. The cause is due to the lack of operator expertice and the center roll was not carried out when installing it, so that training is needed to increase operator expertice.

Weak Calibration of a Visible Light Positioning System Based on a Position-Sensitive Detector: Positioning Error Assessment

Reduced deployment and calibration requirements are key for scalable and cost-effective indoor positioning systems. In this work, we propose a low-complexity, weak calibration procedure for an indoor positioning system based on infrastructure lighting and a positioning-sensitive detector. The proposed calibration relies on genetic algorithms to obtain the relevant system parameters in the real positioning environment without a priori information, and requires a low number of simple measurements. The achievable performance of the proposal was assessed by direct comparison with a formal offline calibration method requiring complex dedicated infrastructure and instruments. The comparative error assessment showed that the maximum accuracy reduction compared to the significantly more costly formal calibration was below 25 mm, and the overall absolute positioning error was smaller than 35 mm with orientation errors of around 0.25°. The performance achieved with the proposed weak calibration procedure is sufficient for many indoor positioning applications and largely reduces the cost and complexity of setting up the positioning system in real environments.