This article discusses the use of sensors in the measurement of surface roughness, covering the different types of sensors used for measuring surface roughness, their principles of operation, and their applications.
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Significance of Sensors in Surface Roughness Measurement
Surface roughness is an essential characteristic of a material's surface that affects its performance, function, and quality. It is a measure of the surface irregularities and deviations from the ideal form of a surface. Accurate and reliable surface roughness measurement is crucial in several fields, including manufacturing, engineering, and materials science.
Over the years, several methods have been developed for measuring surface roughness. One of the most common and widely used methods is using sensors. Sensors are devices that can detect and measure physical or chemical properties and convert them into electrical signals that can be processed and analyzed.
Sensors for measuring surface roughness have various applications in several fields, including manufacturing, engineering, and materials science. They measure the surface roughness of machined parts, electronic components, and medical implants. They are also used to monitor the wear and tear of surfaces and ensure product quality and performance.
Different Sensors Used in Surface Roughness Measurement
Several types of sensors can be used for measuring surface roughness. Contact sensors physically contact the surface being measured. Stylus-type sensors are one of the most-utilized contact sensors, which use a stylus or a probe to touch the surface being measured physically.
The stylus moves along the surface, and the resulting displacement is measured and used to determine the surface roughness. Stylus-type sensors are highly accurate and can measure surface roughness in the range of 0.01 to 50 µm. However, they are limited by the size and shape of the stylus, which can affect the measurement accuracy. They are also sensitive to vibrations and can be damaged if they come into contact with a hard surface.
Non-contact sensors do not physically touch the surface being measured. They are further classified into two categories, called optical and electromagnetic sensors. Optical sensors use light to measure surface roughness. These sensors use a confocal microscope to focus light on the surface being measured.
A detector captures the reflected light, and the resulting signal is processed to determine the surface roughness. Confocal sensors are highly accurate and can measure surface roughness in the range of 0.01 to 10 µm. However, these sensors are limited by the material properties of the surface being measured and can be affected by the presence of contaminants.
Interferometric sensors use interferometry to measure the surface roughness. The surface being measured is illuminated with a beam of light, and a detector captures the reflected light. Interferometric sensors are highly accurate and can measure surface roughness in the range of 0.001 to 1 µm. However, these sensors are also limited by the material properties of the surface and can be affected by vibrations and temperature fluctuations.
Principles of Operation of Sensors in Measuring Surface Roughness
In situ measurement for surface finish on a workpiece is increasingly demanded in many advanced manufacturing products such as mirror finishing, additive manufacturing, and free-form products. The conventional method for surface finish measurement is to scan surfaces with a contact stylus and capture surface height deviations in the form of a surface profile.
The stylus comprises a sharp diamond tip with a radius as small as 0.001 mm. The mechanical stylus produces consistent measurements and is easy to use. However, the sharp stylus tip can eventually scratch the surface, which causes permanent damage to the specimen.
Non-contact systems based on optical techniques are considered more suitable for in situ measurements as they can provide high scanning speed and accuracy. However, they are more expensive and complex than contact sensors.
Recent Studies
In a recent study by Hagemeier et al., the authors introduced a fiber-coupled laser interferometric confocal distance sensor for surface profiling. The sensor was defined by small geometrical dimensions and a quick lateral scanning rate. The team discussed the development of a tiny interferometric-confocal distance sensor (ICDS) that could perform the measurement at up to 80 kHz.
By making many measurements on a sinusoidal standard and two roughness standards with varying degrees of roughness using various lateral scan velocities up to 75 mm s-1, it was possible to measure specularly reflected and rough surface textures.
The measurement outcomes were contrasted with those attained using a tactile stylus tool in a measurement setting identical to the newly introduced optical sensor. The capacity for full-field measurements was also shown.
The sensor’s performance was verified by using it on various surface textures. It was demonstrated that the sensor could measure rough surface textures and calculate roughness parameters in addition to being able to measure on specular surfaces, which is frequently a need in industrial applications.
The sensor's lower geometrical dimensions allowed for easier integration into current measuring systems and enhanced accessibility of challenging surfaces. Depending on the application, the sensor head with an aspherical focusing lens could be modified, for example, by placing a prism in front of the sensor head to create a 90° deflection of the focused beam. Because the ICDS could measure smooth and rough surfaces, it made it possible to determine roughness parameters even at higher roughness values.
In another recent study by Kursun et al., the team developed a piezo acoustic sensor to examine the signal processing-appropriate surface roughness measurements. The as-developed piezo acoustic disks simultaneously produced raw sound from elastic waves transmitted along metal surfaces brought about by the friction of a diamond point.
Three stages of evaluation, namely, a parametric analysis, a power spectrum analysis, and a comparative analysis using standard parameters, were used to assess the piezo acoustic data obtained for all samples and production operations.
The aluminum sample’s power spectrum had a frequency response consistent with the surface roughness profile determined by uniform measurements of the same substance. The examination of stainless steel samples revealed that the variation in frequency responses between the lathed and milled surfaces was the main distinction. It was determined that acoustic friction measurement offers promising results as a cutting-edge technique for determining the states of any material’s surface roughness.
Another study published in the Procedia CIRP discussed the combination of a robotic mass finishing cell with an internally created surface roughness measuring system that used a chromatic confocal sensor to achieve in situ and non-contact surface roughness measurement. The ISO 4287 and ISO 4288 standards were used to compute and assess the surface roughness characteristics. The system could measure the surface roughness of various manufactured components with a 5% relative error. By offering closed-loop process control and doing away with workpiece reloading operations, the measurement findings showed that the developed system could be a potential solution for in situ and on-machine surface quality monitoring in mass finishing processes.
Furthermore, Abidin et al. discussed the development of a non-contact method for measuring surface roughness in another recently published study. An optical sensor was employed as the microcontroller’s input signal to create the tool. A mathematical approach was used to convert the voltage signal from the optical signal into the measurement value of surface roughness.
The Arduino microcontroller's code instructions were written in the C++ programming language. The findings showed that the proposed tool could evaluate metal surfaces' roughness using a non-contact method.
Conclusion and Future Perspective
Overall, it was concluded that sensors are essential for measuring surface roughness to ensure the quality and performance of products in various industries. They allow for accurate and precise measurement of surface roughness, which can impact the function and durability of products. As technology advances, sensors for measuring surface roughness are becoming increasingly sophisticated and accurate.
Further research to increase the axial accuracy of the sensor includes the localization of potential vibration sources and the mitigation of their impact on measured height values. It is advised that future work should concentrate on improving the accuracy of motion control, analyzing robot arm vibration, and creating sophisticated noise-filtering algorithms.
As the demand for high-quality and high-performance products continues to grow, sensors for measuring surface roughness will play an increasingly important role in ensuring product quality and reliability. The development of new and innovative sensors for measuring surface roughness will continue to drive advances in several fields, enabling researchers and engineers to create new and exciting products that meet the needs of consumers and industries worldwide.
References and Further Reading
Hagemeier, S., et al. (2023). Miniaturized interferometric confocal distance sensor for surface profiling with data rates at ultrasonic frequencies. Measurement Science and Technology, 34, p. 045104. https://iopscience.iop.org/article/10.1088/1361-6501/acaf97/meta
Kursun, V., et al. (2022). Utilizing Piezo Acoustic Sensors for the Identification of Surface Roughness and Textures. Sensors, 22(12), p. 4381. https://www.mdpi.com/1424-8220/22/12/4381
Fu, S., et al. (2020). In-situ measurement of surface roughness using chromatic confocal sensor. Procedia CIRP, 11, 94, pp. 780–784. https://www.sciencedirect.com/science/article/pii/S2212827120313214?via%3Dihub
Abidin, Z. F. Z., et al. (2019). Portable non-contact surface roughness measuring device. IOP Conference Series: Materials Science and Engineering, 469, p. 012074. https://iopscience.iop.org/article/10.1088/1757-899X/469/1/012074
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