Hasil untuk "Engineering machinery, tools, and implements"

Alun Sujjada, Rizki Rahmatulloh, Suganda et al.

The eye, in a dominant sense, can suffer disorders, such as myopia or nearsightedness, because of VDU radiation exposure. One symptom which is often caused by excessive use of VDU is eye strain. It is usually marked by an increase in the sensitivity of the eyes to light. It is known by comparing the diameter of the normal eye’s pupil and the strained eye’s pupil. People can prevent this disorder by detecting changes in the pupil’s diameter compared to the iris. Changes in the iris and pupil can be detected by using the Hough transformation to detect their shape and train perceptron neural network algorithms to recognize the patterns. As a VDI tool, an eye strain detection application can determine the condition of the user’s eyes. The level of accuracy of the method used to detect the iris and pupil using the Hough transformation is 100% for brown irises, 50% for blue irises, 33.3% for green irises, and it has a 100% accuracy in detecting an iris that is similar to the pupil and a 28.6% accuracy in detecting a pupil that is a similar color to the iris. There is also a difference in the level of accuracy of these case studies when different detection tools are used. The smartphone camera showed a 100% accuracy in detecting the iris and 28.6% accuracy in detecting the pupil. The SLR camera had a 100% accuracy in detecting the irises and 71.4% accuracy in detecting pupils, while the digital camera had 14.28% accuracy in detecting irises and a 0% accuracy in detecting a pupil. The accuracy of the perceptron algorithm in recognizing a pattern of eye strain is 70% with 20 sets of test data.

Filip Tsvetanov, Martin Pandurski

This paper examines some of the most popular protocols for transmitting sensor data to cloud structures from publish/subscribe and request/response IoT models. The selection of a highly efficient message transmission protocol is essential, as it depends on the specific characteristics and purpose of the developed IoT system, which includes communication requirements, message size and format, energy efficiency, reliability, and cloud specifications. No standardized protocol can cover all the diverse application scenarios; therefore, for each developed project, the most appropriate protocol must be selected that meets the project’s specific requirements. This work focuses on finding the most appropriate protocol for integrating sensor data into a suitable open-source IoT platform, ThingsBoard. First, we conduct a comparative analysis of the studied protocols. Then, we propose a project that represents an experiment for transmitting data from a stationary XBee sensor network to the ThingsBoard cloud via HTTP, MQTT-SN, and CoAP protocols. We observe the parameters’ influence on the delayed transmission of packets and their load on the CPU and RAM. The results of the experimental studies for stationary sensor networks collecting environmental data give an advantage to the MQTT-SN protocol. This protocol is preferable to the other two protocols due to the lower delay and load on the processor and memory, which leads to higher energy efficiency and longer life of the sensors and sensor networks. These results can help users make operational judgments for their IoT applications.

Francesca Maria Curà, Luca Corsaro, Ludovica Tromba

Mechanical gears are essential in power transmission systems across various industrial applications. Their performance is critically influenced by residual stresses from manufacturing processes like induction hardening, case hardening, and shot peening. Surface compressive residual stresses enhance resistance to pitting fatigue, bending fatigue and crack propagation, improving overall hardness. In the present work, a Non-Destructive Thermographic method (Active thermography), based on measurement of the thermal diffusivity parameter, is presented to characterize the surface treatments applied to gears. Surface hardness was measured using a micro-hardness tester, and residual stresses were determined with an X-Ray diffractometer, showing variations due to surface treatments. The variation in the thermal diffusivity parameter, obtained using the Slope Method, was found to be an indicator of the surface treatments’ effectiveness.

Ramos Ivy Mar J.

This study applied the DMAIC (Define, Measure, Analyze, Improve, Control) methodology to improve the operational efficiency of a facility’s bottle cleaning process. The Define Phase identified critical issues, including a high rework rate (15%), inconsistent cleaning quality (84% efficiency), and excessive water and detergent consumption. The Measure Phase established baseline data to quantify these inefficiencies. Root causes were identified in the Analyze Phase using a Fishbone Diagram and Failure Mode and Effects Analysis (FMEA), highlighting manual detergent dosing, unstable wash temperatures, lack of standard operating procedures (SOPs), and inconsistent employee performance as major contributors. During the Improve Phase, remedies that were specific to the problem were put into place. These included automating the delivery of detergent and controlling the temperature, adding real-time monitoring, updating standard operating procedures (SOPs), and giving employees more training. Because of this, the cleaning efficiency went up to 92%, the rework rate went down to 7.5%, and the inspection pass rate went up to 92%. Resource usage decreased significantly – detergent by 23% and water by 20% – resulting in annual cost savings of $23,404.24. These gains were sustained through the Control Phase via continuous monitoring, regular audits, and periodic staff retraining. The project shows that DMAIC may help improve operations and lower costs in a way that lasts. It gives other plants a model to follow if they want to improve their manufacturing processes while making them better for the environment and quality. Combining automation, data collection, analysis, and a standardized workflow within a framework for improvement led to rapid gains and long-term process control.

Taihei YAMAMOTO, Yuji HATTORI

The Ranque-Hilsch vortex tube (RHVT) is a device which can separate compressed gas into high and low energy flows only through its own fluid motion by generating a high-speed swirling flow inside a cylindrical vortex chamber. The energy separation phenomenon of RHVT is called the Ranque-Hilsch effect (RH effect). Although numerous studies have been conducted over the past 90 years since the discovery of the RH effect (1933), the energy separation mechanism has not been elucidated. Previous studies have suggested that the RH effect is caused by the unsteadiness of a high-speed swirling flow inside the cylinder, such as turbulence and acoustic phenomena. However, the unsteady characteristics have not been clarified because it is difficult to obtain time evolution data sufficiently guaranteed physical reliability. In this paper, high-speed unsteady swirling flow inside the cylindrical vortex chamber of RHVT is calculated by direct numerical simulation for the first time. Because the Reynolds number of the flow inside RHVT is high, it is difficult to use direct numerical simulation due to the high computational costs. To reduce the computational costs, we performed direct numerical simulation by setting the viscosity of the working fluid higher than the air. The viscous coefficient of the working fluid is set equal to or larger than 100 times that of the air. We find out that the turbulence intensity is axisymmetric, and the closer to the axial position of inlet, the stronger turbulence occurs. By comparing flows with different viscosities of the working fluid, we also find out that there is a relationship between the RH effect and turbulence in a high-speed swirling flow inside the cylinder, as the energy separation effect increases with increasing turbulence intensity.

Pablo Rivadeneira, William Chamorro, Jorge Medina et al.

Lithium-ion batteries have become a beacon in modern energy storage, powering from small electronic devices to electric vehicles (EVs) and critical medical equipment. Since their commercial introduction in the 1990s, significant advancements in materials science and engineering have enhanced battery capacity, safety, and lifespan. However, the complexity of lithium-ion battery dynamics has necessitated the development of advanced charging and control strategies to optimize performance, safety, and longevity. This work proposes a comparative analysis of three advanced control methods for lithium-ion battery charging: reinforcement learning, fuzzy logic, and classic proportional–integral–derivative (PID) control. Traditional charging methods often fail to address the complexities of battery dynamics, leading to suboptimal performance. Our study evaluates these intelligent control strategies using MATLAB-Simulink simulations to enhance charging efficiency, speed, and battery lifespan. The findings indicate that reinforcement learning offers superior adaptability, fuzzy logic provides robust handling of nonlinearity, and PID control ensures reliable performance with minimal computational resources.

Catalina Ortiz, Fatemeh Hatam, Michèle Prévost

Predicting <i>Legionella</i> concentrations reaching users through building water systems requires a comprehensive water quality modeling approach. We integrate various frameworks and data to test the effect of nutrient availability, temperature, chlorine, and biofilm interactions in modeling <i>Legionella</i>. We show that neglecting biofilm detachment underestimates concentrations up to 5.5 logs, while including it increases estimates by 4.2 logs. This study identifies critical factors and uncertainty sources for characterizing the <i>Legionella</i> fate and transport phenomena within buildings.

David Esneyder Bello Angulo, Elizabeth León Guzmán

This paper presents a novel multimodal recurrent model for time series forecasting leveraging LSTM architecture, with a focus on production forecasting in oil wells equipped with rod lift systems. The model is specifically designed to handle time series data with diverse types, incorporating both images and numerical data at each time step. This capability enables a comprehensive analysis over specified temporal windows. The architecture consists of distinct submodels tailored to process different data modalities. These submodels generate a unified concatenated feature vector, providing a holistic representation of the well’s operational status. This representation is further refined through a dense layer to facilitate non-linear transformation and integration. Temporal analysis forms the core of the model’s functionality, facilitated by a Long Short-Term Memory (LSTM) layer, which excels at capturing long-range dependencies in the data. Additionally, a fully connected layer with linear activation output enables one-shot multi-step forecasting, which is necessary because the input and output have different modalities. Experimental results show that the proposed multimodal model achieved the best performance in the studied cases, with a Mean Absolute Percentage Error (MAPE) of 8.2%, outperforming univariate and multivariate deep learning-based models, as well as ARIMA implementations, which yielded results with a MAPE greater than 9%.

Wiwik Purwadi, Ari Siswanto, Gita Novian Hermana

A study of the utilization of WC-Co tool tip scraps as reinforcement in MMC with a Hadfield austenitic manganese steel matrix was conducted using an in situ metal casting technique. This study concerns the effect of the heat-treatment process on the cast sample of MMC. The results show that the heating temperature affects the grain size of the austenite around the interface between Hadfield austenitic manganese steel and WC-Co tool tip scraps. Heating at high temperatures leads to an increase in the austenite grain size. Microstructure analysis also shows that the heat-treatment process does not affect the bond between WC-Co tool tip scraps and Hadfield austenitic manganese steel. However, mechanical property testing reveals that higher heat-treatment temperatures result in a decrease in the hardness of the MMC.

Joseph Cohen, Xun Huan, Jun Ni

In the era of industrial big data, prognostics and health management is essential to improve the prediction of future failures to minimize inventory, maintenance, and human costs. Used for the 2021 PHM Data Challenge, the new Commercial Modular Aero-Propulsion System Simulation dataset from NASA is an open-source benchmark containing simulated turbofan engine units flown under realistic flight conditions. Deep learning approaches implemented previously for this application attempt to predict the remaining useful life of the engine units, but have not utilized labeled failure mode information, impeding practical usage and explainability. To address these limitations, a new prognostics approach is formulated with a customized loss function to simultaneously predict the current health state, the eventual failing component(s), and the remaining useful life. The proposed method incorporates principal component analysis to orthogonalize statistical time-domain features, which are inputs into supervised regressors such as random forests, extreme random forests, XGBoost, and artificial neural networks. The highest performing algorithm, ANN–Flux with PCA augmentation, achieves AUROC and AUPR scores exceeding 0.94 for each classification on average. In addition to predicting eventual failures with high accuracy, ANN–Flux achieves comparable remaining useful life RMSE for the same test split of the dataset when benchmarked against past work, with significantly less computational cost

Seungyeon Han, Hyungmog You, Myeongill Kim et al.

In order to maintain a suitable road pavement level with limited resources, a management system must be established. In order to achieve this goal, a program using AI (artificial intelligence) was developed to manage and evaluate a sizable volume of survey data. A national highway pavement data management system (PDMS) built on the WEB was also constructed. By connecting several artificial neural networks, the AI crack analysis algorithm was created and taught to automatically recognize cracks in road photos and calculate crack rates. In the PDMS, the current condition of a national highway can be shown on a map, and all the data are updated to allow for verification in increments of 100 m for each lane. The system was also improved to enable the collection of information on the detailed survey section’s pavement repair specifics according to survey year.

Kaushal K. Jain, Peter H. Meckl, Pingen Chen et al.

This paper presents a diagnostics-oriented aging model for combined Selective Catalytic Reduction (SCR) and Ammonia Slip Catalyst (ASC) system, along with a model-based on-board diagnostic (OBD) method applied to both test-cell data and on-road data from commercial trucks. The key challenge with model development was unavailability of NOx and NH3 measurements between SCR and ASC. Since it would have been very difficult to calibrate both SCR and ASC dynamics without any measurements between SCR and ASC, therefore ASC was modeled using static look-up tables to determine ASC’s NH3 conversion efficiency and its selectivity to NOx and N2O as a function of temperature and flow rate. The traditional three-state single-cell ordinary differential equation (ODE) model was used for SCR. Hot Federal Test Procedure (hFTP) was used to calibrate the model. Cold FTP (cFTP) and Ramped Mode Cycle (RMC) were used for validation. Results show that the SCR-ASC model can capture the aging signatures in tailpipe NOx, NH3, and N2O reasonably well for cFTP, hFTP, and RMC cycles in the testcell data. After slight re-calibration and combining with a simple model for commercial NOx sensor’s cross-sensitivity to NH3, the model works reasonably well for on-road data from commercial trucks. A model-based on-board diagnostic (OBD) method has been presented with enable conditions designed to detect operating conditions suitable for detecting aging signatures, while minimizing false positives and false negatives. The OBD method is applied to both test-cell and real-world truck data with commercial NOx sensors. Results on test-cell data demonstrate the challenges of robust SCR monitoring even on the limited data set used in this work. The model-based enable conditions are shown to be robust but extremely restrictive as the OBD gets enabled at very few points in the test-cell data. Application on truck data showed that the proposed OBD method can be implemented on commercial trucks with limited sensors. In the truck data, the enable conditions were satisfied on many more points than the test-cell data. Results on truck data show encouraging trends between relative degradation level and the number of miles on four trucks. In future work, these trends will be validated using more data from commercial trucks with known aging levels.

Arnold Hien, Nicolas Beldiceanu, Claude-Guy Quimper et al.

We describe an optimal linear time complexity method for extracting patterns from sliding windows of multivariate time series that depends only on the length of the time series. The method is implemented as an open-source Java library and is used to detect anomalies in multivariate time series.

Saúl Langarica, Christian Rüffelmacher, F. Núñez

Being able to detect, identify, and diagnose a fault is a key feature of industrial supervision systems, which enables advance asset management, in particular, predictive maintenance, which greatly increases efficiency and productivity. In this paper, an Industrial Internet app for real-time fault detection and diagnosis is implemented and tested in a pilot scale industrial motor. Real-time fault detection and identification is based on dynamic incremental principal component analysis (DIPCA) and reconstruction-based contribution (RBC). When the analysis indicates that one of the vibration measurements is responsible for the fault, a convolutional neural network (CNN) is used to identify the unbalance or bearing fault type. The application was evaluated in its three functionalities: fault detection, fault identification, and fault identification of vibration-related faults, yielding a fault detection rate over 99%, a false alarm rate below 5%, and an identification accuracy over 90%. Note to Practitioners—This paper focuses on designing and evaluating a real-time fault diagnosis application in an industrial setup. To this end, this paper also tackles the problem of developing a methodology for implementing advanced state-of-the-art fault detection techniques in real machinery, following industry standards and using a modern informatics architecture. The application here developed uses a statistical data-driven fault diagnosis technique, hence it requires a training stage using historical data to learn patterns and estimate parameters. A proof of concept in fault diagnosis for industrial motors is given; however, it should be noted that both the methodology and the deployed architecture are scalable and flexible enough to facilitate the implementation in other industrial environments. The implementation here presented was deployed using only open-source tools, which allows evaluating this tool without incurring in high expenses.

Leonardo Frizziero, Christian Leon-Cardenas, Giulio Galiè et al.

This study is on the conception of the DS700 HYBRID project by the application of the Industrial Design Structure method (IDeS), which applies different tools sourced from engineering and style departments, including QFD and SDE, used to create the concept of a hybrid motorbike that could reach the market in the near future. SDE is an engineering approach for the design and development of industrial design projects, and it finds important applications in the automotive sector. In addition, analysis tools such as QFD, comprising benchmarking and top-flop analysis are carried out to maximize the creative process. The key characteristics of the bike and the degree of innovation are identified and outlined, the market segment is identified, and the stylistic trends that are most suitable for a naked motorbike of the future are analyzed. In the second part the styling of each superstructure and of all the components of the vehicle is carried out. Afterwards the aesthetics and engineering perspectives are accounted for to complete the project. This is achieved with modelling and computing tools such as 3D CAD, visual renderings, and FEM simulations, and virtual prototyping thanks to augmented reality (AR), and finally physical prototyping with the use of additive manufacturing (AM). The result is a product conception able to compete in the present challenging market, with a design that is technically feasible and also reaches new lightness targets for efficiency.

Fatema Rezwana, Hamida Akter, Mohammed Abdus Samad et al.

The dairy sector in Bangladesh releases huge amounts of wastewater in the open environment. Dairy wastewater is enriched with hazardous contaminants, which can cause various health complications. The objective of this study was to evaluate the water quality of dairy wastewater by determining the physicochemical properties of tap water and wastewater from three farms from the Islamnagar zone, Savar, Dhaka, and also to assess the significant impacts of wastewater on the environment. The most important physicochemical properties investigated include pH, total dissolved solids (TDS), electrical conductivity (EC), dissolved oxygen (DO), and microbial colony count. The results revealed that in tap water, the pH ranged from 7.11 to 7.20, and in wastewater, it ranged from 7.30 to 7.77. The TDS in tap water ranged from 109 to 116 mg/L, and in wastewater, it ranged from 451 to 2000 mg/L. The EC values were found in tap water from 0.22 to 0.23 mS/cm, whereas in wastewater, they ranged from 0.86 to 13.20 mS/cm. Additionally, for DO, the tap water ranged from 4.21 to 6.25 mg/L; in wastewater, it ranged from 0.98 to 1.86 mg/L. The pH and TDS stayed within the standard limits in the physical–chemical parameters assessed. However, the EC and DO are not within the DoE (Department of Environment, Bangladesh)-allowed limits. In addition, more microbial colonies have occurred in wastewater than in tap water. The study demonstrates that the discharge of dairy wastewater in the open field is detrimental to our ecosystem, and a proper treatment facility is essential.

D. H. Sulistyarini, D. P. Andriani, Z. Darmawan et al.

One of the technologies that are developing rapidly is 3D printing. 3D printing machines can create objects easily, quickly and in detail. There are three main steps that a 3D printing machine goes through, namely design, printing and finishing. 3D printers using polylactic acid are widely used in various types of fields such as industrial machinery, spacecraft, consumer goods, electronic components, vehicles, medical industry, toy industry and others.In this research, we succeeded in making an educational game tool called Tetris with the 3D printing method using polylactic acid filaments. The process of consistently creating 3-dimensional objects from digital files by arranging many layers of thin metal in succession is called 3D printing. Using additive manufacturing technology, 3D digital designs are turned into virtual products by sequentially depositing metals. 3D printing provides reasonable feasibility to meet various parameters based on the engineering arena. By utilizing the advantages of PLA material, which has good tensile strength, good surface quality, is available in various colors and user-friendly, educational game tools are produced that have the advantage of being attractive, light, strong and easy to play. That way, making educational game tools using 3D printing made from PLA can solve the problems with previous educational game tools, which are not easy to play and less attractive to early childhood. Educational games that have been made in this study can be used as a platform for learning children at the kindergarten to elementary school levels. The way to play this educational game Tetris is by attaching shapes to the main part of Tetris and matching them with other shapes, just like playing a puzzle where each shape that is installed must match the other shapes

M. Shih, W. Sung

Agus Kurniawan, Yudha Samudra, Eko Prasetyo Nugroho et al.

The research tested the effects between feeding and surface roughness on shaft of steady rest which will be worked in CMZ T-360’s lathe machine. Material of St.60 was turned into shaft of steady rest in 2 CMZT-360’s machines with several of feeding like 0.045, 0.056, 0.068, 0.079, 0.90 and 0,102 mm/rev. Then the work piece was tested the surface roughness of the work piece with using roughness tester and being seen the shape of the surface with using photo macro. The result of the roughness’s test showed that CMZ-T360’s lathe machine could reach Ra 1.55 µm until 4.79 µm with several of feeding. The work piece would be assembled in steady rest and being used to support 58 kg weigth in 2 hours to know the effect of surface roughness. After testing on steady rest, would be done photo macro on the specimen to make easy on visual analysis. From the data which is obtained can conclude that feeding variation which used on this research not so affecting the quality of surface roughness of the workpiece, the value of surface roughness that resulted from this research is mostly N8 (3,25 µm- 5,29 µm) and only a few that valued N7 (1,55 µm-2,53 µm) . The second conclusion is the surface roughness which quickly wornt-out after assembled at steady rest is the rough surface (4,79 µm become 3,84 µm) compared with the smooth surface (2,23 µm become 2,03 µm).

R. Razali, Mohamad Shukor Che Malek

There are about 25% of the total road networks in Malaysia is an unpaved road which is contained gravel or earth road [1]. These are commonly referred to as the low volume roads or unpaved rural road or agriculture roads which required a very minimum standard of design and construction due the low traffic impact and function of the road as an accessibility tool to the local communities. Difficulties in transporting of the materials to the construction site due to the distance from the quarry or construction material source do occur together with the requirement of suitable machinery. Soil stabilisation was identified as an alternative method to facilitate the design and construction requirement in achieving the implementation of the project where respect to cost, time and quality. Currently, soil stabilisation method was not popular in Malaysian construction industry players especially among road designer because the conventional method of pavement design always been the most priorities. Perhaps this may due to the absence of the specification purposely for soil stabilisation works. This paper will present the Malaysian experiences of implementing cement as soil stabiliser in road construction project for rural and low volume roads. The study had been conducted in the road construction project of Jalan Pos Sinderut, Kuala Lipis, Pahang. The scope of the study covers all the stages that involved in the implementation of soil stabilisation using cement this project including design, procurement, and construction.