The Influence of Price, Service Quality, and Word Of Mouth on the Purchase Decision of PT Daniel Samudra Abadi's Loading and Unloading Services

PT Daniel Samudra Abadi is a company engaged in stevedoring services. The services provided are specialized in products, such as steel (cold rolled steel sheet, steel sheet in coil, steel plate, prime concast steel slab, etc.), pulp, and general cargo. The purpose of this research is to determine the influence of price, service quality and word of mouth on the purchase decision of stevedoring service. The population in this research are consumers of PT Daniel Samudra Abadi who have used service from 2018-2020 period with 31 companies as a sample. Determination of the sample in this research using saturated samples. This research uses multiple linear regression analysis. The results showed that all independent variables, namely price, service quality and word of mouth have a positive and significant effect on the purchase decision of PT Daniel Samudra Abadi stevedoring service.

A Model-Based Predictive Controller of the Level of Steel in the Mold with Disturbances Using a Repetitive Structure

Keeping the level of steel in the mold of the continuous casting process constant is fundamental for the quality of the steel produced and, consequently, its commercial value. It is challenging, considering the several disturbances that cause undesired variations in the mold level. The aim of this paper is to apply a repetitive structure composed of two controllers, a generalized predictive controller (GPC) and a repetitive GPC (R-GPC) with constraints to mitigate the bulging and clogging/unclogging disturbances and the casting speed variation in the mold level of the process. The R-GPC controller has the same characteristics as the GPC, such as performance, robustness to disturbances, and insertion of constraints, and its advantage is the elimination of periodic disturbances. The repetitive structure will be implemented with a robustness filter and tuned by a genetic algorithm (GA). The controller tests are performed by simulations of a nonlinear mathematical model of the mold level, validated using real data from the steel industry. The proposed controller reduces the bulging disturbance amplitude by 98.5% and at 25% of the frequency of reversions in the valve. Consequently, the proposed controller allows an increase in the valve life span, a reduction in maintenance costs, and quality improvement in the steel slab.

Analysis of a New SEN Design with an Inner Flow Divider

To minimize the product imperfections due to slag entrapment and surface defects, the fluid flow pattern inside the mold must be symmetric, commonly named double-roll flow. Thus, the liquid steel must enter into the mold evenly distributed. The submerged entry nozzle (SEN) is crucial in product quality in vertical steel slab continuous casting machines because it distributes the molten steel from the tundish into the mold. This work evaluates the performance of a novel bifurcated nozzle design named “SEN with flow divider”. The symmetry at the outlet ports is obtained by imposing symmetry inside the SEN. The flow divider is a solid barrier attached at the SEN bottom inner wall, the height of which slightly surpasses the upper edges of the outlet ports. The performance analysis is done first using numerical simulations, where the Computational Fluid Dynamics (CFD) technique and the Smoothed Particle Hydrodynamics (SPH) approach are used. Then, experimental tests on a scaled model are also used to evaluate the SEN performance. Numerical and physical simulations showed that the flow divider considerably reduces the SEN outlet jets’ broadness and misalignment, producing compact, aligned, and symmetric jets. Therefore, the SEN design analyzed in this work is a promising alternative to improve process profitability.

MODELING ROLLERS USING TERRESTRIAL LIDAR POINTS IN A HOT-ROLLING STEEL MILL

Terrestrial lidar scanners are increasingly being used in numerous indoor mapping applications. This paper presents a methodology to model rollers used in hot-rolling steel mills. Hot-rolling steel mills are large facilities where steel is processed to different shapes. In a steel sheet manufacturing process, a steel slab is reheated at one end of the mill and is passed through multiple presses to achieve the desired cross-section. Hundreds of steel rollers are used to transport the steel slab from one end of the mill to the other. Over a period of use, these rollers wore out and need replacement. Manual determination of the damage to the rollers is a time-consuming task. Moreover, manual measurements can be influenced by the operator’s judgment. This paper presents a methodology to model rollers in a hot-rolling steel mill using lidar points. A terrestrial lidar scanner was used to collect lidar points over the roller surfaces. Data from several stations were merged to create a single point cloud. Using a bounding box, lidar points on all the rollers were clipped and used in this paper. The clipped data consisted of the roller as well as outlier points. Depending on the scan angles of scanner stations, partial surfaces of the rollers were scanned. A right-handed coordinate frame was used where the X-axis passed through the centers of all the rollers, Y-axis was parallel to the length of the first roller, and the Z-axis was in the plumb direction. Using a standard diameter of the roller, model roller points were created to extract the rollers. Both the lidar data and the model points were converted to rectangular prism-shaped voxels of dimensions 15.24 mm (0.05 ft) × 15.24 mm in the X and Z directions and extending over the entire width of the roller in the Y-direction. Voxels containing at least 40 lidar points were considered valid. Binary images of both the lidar points and the model points were created in the X-Z axes using the valid voxels. The roller locations in the lidar image were located by performing 2D FFT image matching using the model roller image. The roller points at the shortlisted locations were fitted with a circle equation to determine the mean roller diameters and mean center locations (roller’s rotation axis). The outlier points were filtered in this process for each roller. The elevation at the top of every roller was determined by adding their radii and Z-coordinates of its centers. Incorrectly located and/or modeled rollers were identified by implementing moving-average filters. Positively identified roller points were further analyzed to determine surface erosions and tilts. The above methodology showed that the rollers can be effectively modeled using the lidar points.

Synthetic Data Generation for Steel Defect Detection and Classification Using Deep Learning

The paper presents a methodology for training neural networks for vision tasks on synthesized data on the example of steel defect recognition in automated production control systems. The article describes the process of dataset procedural generation of steel slab defects with a symmetrical distribution. The results of training two neural networks Unet and Xception on a generated data grid and testing them on real data are presented. The performance of these neural networks was assessed using real data from the Severstal: Steel Defect Detection set. In both cases, the neural networks showed good results in the classification and segmentation of surface defects of steel workpieces in the image. Dice score on synthetic data reaches 0.62, and accuracy—0.81.

A Multiscale-Based Approach to Understand Dendrite Deflection in Continuously Cast Steel Slab Samples

Dendrite bending angle measurements were conducted along two different directions on four steel slab samples collected from a conventional caster. The primary dendrites growing at the slab surface showed a transition in their growth direction as the distance from the surface increased. Numerical fluid flow simulation showed changes in the flow directions that might have caused the change in the growth direction. The bending angle measurements were also correlated with the casting process parameters. Thereafter, a multiscale approach was adopted to predict the dendrite deflection angles by correlating the macro-scale flow profile with the micro-scale bending angle formulation and subsequently corroborated with the industrial scale measurements.

Design Optimization of Explosion-Resistant System Consisting of Steel Slab and CFRP Frame

This study presents an explosion-resistant hybrid system containing a steel slab and a carbon fiber-reinforced polymer (CFRP) frame. CFRP, which is a high-strength material, acts as an impact reflection part. Steel slab, which is a high-ductility material, plays a role as an impact energy absorption part. Based on the elastoplastic behavior of steel, a numerical model is proposed to simulate the dynamic responses of the hybrid system under the air pressure from an explosion. Based on this, a case study is conducted to analyze and identify the optimal design of the proposed hybrid system, which is subjected to an impact load condition. The observations from the case study show the optimal thicknesses of 8.2 and 7 mm for a steel slab and a ϕ100 mm CFRP pipe for the hybrid system, respectively. In addition, the ability of the proposed hybrid system to resist an uncertain explosion is demonstrated in the case study based on the reliability methodology.