Surface Roughness Evaluation of the Inner Surface of Automobile Engine Bores by RANSAC and the Least Squares Method

Analysis methods for plateau surfaces have been described in the ISO standards, JIS, and previous studies. The authors of a previous study proposed a method based on the concept of random sample consensus (RANSAC). This method achieved high analysis accuracy for plateau surfaces by setting detailed conditions. However, the process of setting optimal conditions is performed manually, which reduces productivity due to the manpower and man-hours required. In this study, we propose a new method for automating the setting of conditions. This method, which does not require human intervention, is expected to contribute to the improvement of productivity at production sites.

Minimization Method for 3D Surface Roughness Evaluation Area

3D surface roughness measurement is still a less mature procedure than its 2D version. The size of the evaluation area is not as standardized as the measurement length in the 2D version. The purpose of this study is to introduce a method for minimizing the evaluated surface area. This could help industrial applications in minimizing the time and cost of measurements. Machining experiments (hard turning and infeed grinding) and surface roughness measurements were carried out for automotive industrial parts to demonstrate the introduced method. Some frequently used roughness parameters were analyzed. Basic statistical calculations were applied to analyze the relationship between the surface area and the roughness parameter values and regression analyses were applied to validate the results in case of the applied technological data. The main finding of the study is that minimum evaluation areas can be clearly designated and, depending on the different roughness parameter–procedure version, different evaluation sizes (Sa: 1.3 × 1.3 mm; Sq: 1.4 × 1.4 mm; Ssk and Sku: 2 × 2 m; Sp and Sv: 1.7 × 1.7 mm) are recommended.

Precision Enhancement of Smartphone Sensor-Based Pavement Roughness Estimation by Standardizing Host Vehicle Speed

Condition assessment of pavement has a predominant part in delivering safety and comfort to users. Roughness is considered the most important characteristic as it affects road safety and vehicle operating costs. Authorities spend significant quantity of resources on using conventional methods for measuring roughness. Many researches are performed to estimate roughness by deploying smartphone sensors. However, no consideration is given to host vehicle speed influence in roughness evaluation using smartphones. This work explains a smartphone-sensor-based roughness evaluation technique by deploying the QCS model. The accuracy is checked with simultaneously collected IRI by a Roughometer. Results of the smartphone-based pavement roughness estimation experiment showed a high correlation value of 0.73, and proved the accuracy of the method. The data were segregated based on three speed ranges. The correlation between the smartphone-based and Roughometer-based IRI for all ranges was analyzed, and the R2 value of 0.75 was exhibited for 31-50 km/hr range.