Painting pretreatment is an important task in determining the life of painting as it removes rust or foreign substances from the painting surface and gives adhesion between the painting surface and the painting surface. Since painting pretreatment is an important task, IMO strictly requires that the painting pretreatment surface be maintained at a Sa 2.5 grade and the surface roughness is 30μm~75μm. Painting pre-processing is an important task that determines the lifespan of a painting, but it is done through visual inspection by the inspector, and the quality varies depending on the inspector. In this study, in order to develop a quality measurement system for the painting pretreatment surface, Matlab2023b was used to determine the range of appropriate quality brightness by comparing the brightness of the painting pretreatment surface and surface roughness.

Injection molding is a process of shaping resin materials by heating them to a temperature above their melting point and then using a mold. The resin material is injected into and cooled within the mold cavity, solidifying into the desired shape. The core and cavity components that make up the mold cavity are crucial elements for the precision molding in injection molding. In the case of precision mold production, the application of 5-axis machining technology is required to ensure high machining quality for complex shapes, and among these factors, the tool angle is a critical machining condition that determines the surface roughness of the workpiece. In this study, we aim to measure the surface roughness of the machined surface of KP4A specimens during machining processes with variations in the tool angle and analyze the correlation between the tool angle and surface roughness.

The cutting process, which is a key processing technology in various industrial fields is achieving continuous growth, and the demand for high-quality cutting surfaces is continuously demanded. Plasma cutting continues to be studied for its excellent workability and productivity, but problems with cutting surface quality such as dross formation occur, so research to secure excellent cutting surface quality through appropriate control of process variables is essential. In this study, we propose a method for predicting surface roughness using real-time current and cutting speed data obtained while performing plasma cutting on A106 B steel pipe. Surface roughness was predicted based on the RBF algorithm applicable to prediction and control models. It was shown that the surface roughness of the plasma cutting surface can be predicted with the arc current waveform and process speed data. This study can be used as a basic study to control the surface roughness of the cut surface in real time.

Carbon steel pipes, which are essentially used in the manufacturing industry, are used in various fields due to the advancement of the industry, and a cutting process is essentially applied to pipes manufactured in a nominal size. The cutting process is the most basic and first process used to obtain a material in a desired shape, and it can affect the quality of subsequent processes such as welding or painting, so high-quality cutting surfaces are essential. Therefore, due to the advantage of improving productivity, it is essential to study to secure the appropriate quality of the cutting surface of the plasma cutting process, which is widely used in the industrial field. In this study, the effect of cut surface quality according to process parameters in the plasma cutting process for carbon steel pipe materials was analyzed. The surface roughness was measured to determine the quality of the cut surface, and the relationship between the surface roughness and the process variables was confirmed by selecting the arc current and cutting speed, which are identified as the main factors forming the surface roughness, as process variables.

The surface of carbon films deposited with inverted plasma fireballs is analysed in this paper. Measurements were conducted with Raman spectroscopy, atomic force microscopy and nanoindentation. The latter was used to obtain Young’s modulus as well as Martens and Vickers hardness. The roughness of the film was measured by atomic force microscopy and its thickness was measured. It was shown with Raman spectroscopy that the films are homogeneous in terms of atomic composition and layer thickness over an area of about 125 × 125 mm. Furthermore, it was demonstrated that inverted plasma fireballs are a viable tool for obtaining homogeneous, large area carbon films with rapid growth and very little energy consumption. The obtained films show very low roughness.

Recently, halogen lamps for vehicle exterior lamp systems are being replaced by LEDs (Light Emitting Diode) in consideration of miniaturization, power consumption, life, luminance, and eco-friendliness. Due to regulations on the amount of light required, luminance, light uniformity, and glare prevention, it is required to develop a light guide for controlling a light source of an LED lamp for a vehicle. For the development of the light guides, the development of machining technology that can cut micro patterns of hundreds of micrometers scale into surface roughness of tens of nanometers scale must be preceded. In this study, the effect of variations in cutting conditions on surface roughness was analyzed through experiments. The micro patterns was manufactured by cutting into STAVAX material, and the surface of the micro patterns was super-finished using a ball-shaped PCD (polycrystalline diamond) tool without flutes. In experiments, the cutting conditions of the super-finishing process were varied, and the varied cutting conditions were feed rate, radial depth of cut, and spindle speed

The function of coolant in machining is to reduce the frictional force in the contact area in between the tool and the material, and to increase the precision by cooling the work-piece and the tool, to make the machining surface uniform, and to extend the tool life. However, cutting oil is harmful to the human body because it uses chlorine-based extreme pressure additives to cause environmental pollutants. In this study, the effect of cutting temperature and surface roughness of titanium alloy for medical purpose (Ti-6Al-7Nb) in eco-friendly ADL slot shape machining was investigated using the response surface analysis method. As the design of the experiment, three levels of cutting speed, feed rate, and depth of cut were designed and the experiment was conducted using the central composite planning method. The regression expressions of cutting temperature and surface roughness were respectively obtained as quadratic functions to obtain the minimum value and optimal cutting conditions. The values from this formula and the experimental values were compared. As a result, this study makes and establishes the basis to prevent environmental pollution caused by the use of coolant and to replace it with ADL (Aerosol Dry Lubricant) machining that uses a very small amount of vegetable oil with high pressure.

생체용 마그네슘 합금은 전연성 부족과 열에 의한 팽창률 변화가 심하여 2mm 이하의 판재를 만드는 것이 매우 어려움 문제이다. 이를 해결하기 위해 압연 방식, 세이퍼 방식, 밀링 방식 등의 다양한 방법이 존재할 수 있다. 압연 방식을 적용하여 실험을 진행하였으나 Mg 합금은 전연성, 취성의 문제로 인해 파괴되는 현상이 발생하였다. 그리고 세이퍼 방식은 가공시 충격이 발생하는 단속절삭이기 때문에 표면에 자국이 남게 되고 시험편이 휘어지는 현상이 발생하는 문제가 발생하였다. 최종적으로 밀링 방식으로 전환하여 가공실험을 수행해 본 결과 매우 만족할 만한 결과값을 얻게 되었고, 이 결과는 절삭조건을 절삭회전수 1000rpm, 이송속도 127mm/rev, 절삭깊이 0.5mm로 엔드밀 사용하여 가공하였을 때 Ra = 0.44㎛의 표면거칠기값을 얻게 되었다. 본 논문에서는 생체 마그네슘 합금재료로 미소판재를 가공하였을 때 매우 좋은 표면을 유지하며 2mm 이하의 미소 두께를 지속적으로 가공이 가능하도록 하였으며, 다양한 절삭조건, 2날과 4날 엔드밀 날수 변화 등을 통해 최적의 가공조건을 알아보는 실험을 진행하였다.

Carbon fiber and its composites are increasingly used in many fields including defence, military, and allied industries. Also, surface quality is given due importance, as mating parts are used in machineries for their functioning. In this work, the turning process is considered for Carbon Fiber Reinforced Polymer (CFRP) composites by varying three important cutting variables: cutting speed, feed, and depth of cut. Correspondingly, the surface roughness is measured after the completion of turning operation. As well, a prediction model is created using different fuzzy logic membership function and Levenberg–Marquardt algorithm (LMA) in artificial intelligence. Later, the surface roughness values from the developed models are compared against the experimental values for its correlation and effectiveness in using different membership functions of fuzzy logic and ANN. Thus, the experimental results are analyzed using the effect graphs and it is presented in detail.

Intertidal mud crab (Macrophthalmus japonicus) is an organism with a hard chitinous exoskeleton and has function for an osmotic control in response to the salinity gradient of seawater. Crustacean exoskeletons change in their natural state in response to environmental factors, such as changes in the pH and water temperature, and the presence of pollutant substances and pathogen infection. In this study, the ecotoxicological effects of irgarol exposure and heavy metal distribution were presented by analyzing the surface roughness of the crab exoskeleton. The exoskeleton surface roughness and variation reduced in M. japonicus exposed to irgarol. In addition, it was confirmed that the surface roughness and variation were changed in the field M. japonicus crab according to the distribution of toxic heavy metals (Cd, Pb, Hg) in marine sediments. This change in the surface roughness of the exoskeleton represents a new end-point of the biological response of the crab according to external environmental stressors. This suggests that it may affect the functional aspects of exoskeleton protection, support, and transport. This approach can be utilized as a useful method for monitoring the aquatic environment as an integrated technology of mechanical engineering and biology.

In the field of length, a gauge block is one of the representative gauges used as a standard for length. The main management items of the gauge block are central length, flatness, parallelism, hardness, and surface by visual inspection. The surface of the gauge block may wear over time due to repeated wringing. This phenomenon may deteriorate the precision accuracy and affect the reliability of the measurement results. In this study, the parameters of the surface roughness of the gauge blocks used repeatedly for about 10,000 hours were analyzed. The paired t-test of population mean difference was compared by using the gauge block that has changed over the years as a preliminary experiment and the gauge block with little frequency of use for less than 1 year as the reference value.

Ti-6Al-4V alloy has a wide range of applications, ranging from turbine blades that require smooth surfaces for aerodynamic purposes to biomedical implants, where a certain surface roughness promotes biomedical compatibility. Therefore, it would be advantageous if the high volumetric density is maintained while controlling the surface roughness during the LPBF of Ti-6Al-4V. In this study, the volumetric energy density is varied by independently changing the laser power and scan speed to document the changes in the relative sample density and surface roughness. The results where the energy density is similar but the process parameters are different are compared. For comparable energy density but higher laser power and scan speed, the relative density remained similar at approximately 99%. However, the surface roughness varies, and the maximum increase rate is approximately 172%. To investigate the cause of the increased surface roughness, a nonlinear finite element heat transfer analysis is performed to compare the maximum temperature, cooling rate, and lifetime of the melt pool with different process parameters.

The development of IOT technology and artificial intelligence technology is promoting the smartization of manufacturing system. In this study, data extracted from acceleration sensor and current sensor were obtained through experiments in the cutting process of SKD11, which is widely used as a material for special mold steel, and the amount of tool wear and product surface roughness were measured. SVR (Support Vector Regression) is applied to predict the roughness of the product surface in real time using the obtained data. SVR, a machine learning technique, is widely used for linear and non-linear prediction using the concept of kernel. In particular, by applying GSVQR (Generalized Support Vector Quantile Regression), overestimation, underestimation, and neutral estimation of product surface roughness are performed and compared. Furthermore, surface roughness is predicted using the linear kernel and the RBF kernel. In terms of accuracy, the results of the RBF kernel are better than those of the linear kernel. Since it is difficult to predict the amount of tool wear in real time, the product surface roughness is predicted with acceleration and current data excluding the amount of tool wear. In terms of accuracy, the results of excluding the amount of tool wear were not significantly different from those including the amount of tool wear.

PURPOSES : This study aims to develop and evaluate computer vision-based algorithms that classify the road roughness index (IRI) of road specimens with known IRIs. The presented study develops and compares classifier-based and deep learning-based models that can effectively determine pavement roughness grades. METHODS : A set road specimen was developed for various IRIs by generating road profiles with matching standard deviations. In addition, five distinct features from road images, including mean, peak-to-peak, standard variation, and mean absolute deviation, were extracted to develop a classifier-based model. From parametric studies, a support vector machine (SVM) was selected. To further demonstrate that the model is more applicable to real-world problems, with a non-integer road grade, a deep-learning model was developed. The algorithm was proposed by modifying the MNIST database, and the model input parameters were determined to achieve higher precision. RESULTS : The results of the proposed algorithms indicated the potential of using computer vision-based models for classifying road surface roughness. When SVM was adopted, near 100% precision was achieved for the training data, and 98% for the test data. Although the model indicated accurate results, the model was classified based on integer IRIs, which is less practical. Alternatively, a deep-learning model, which can be applied to a non-integer road grade, indicated an accuracy of over 85%. CONCLUSIONS : In this study, both the classifier-based, and deep-learning-based models indicated high precision for estimating road surface roughness grades. However, because the proposed algorithm has only been verified against the road model with fixed integers, optimization and verification of the proposed algorithm need to be performed for a real road condition.