Industrial Machine Research Articles

Design and Fabrication of a Prototype Pneumatic Cylinder

Pneumatic systems play a crucial role in the manufacturing industry. Many industrial machine require pneumatic systems to operate at present. In industrial machine systems, pneumatic cylinders are one of the machine parts that operate with a high level of intensity; therefore, it is necessary to replace and maintain pneumatic cylinders. The purpose of this paper is to propose the design and production of a prototype pneumatic cylinder that is inexpensive and suitable for single-unit production. The article proposes an approach for determining, developing, and fabricating a pneumatic cylinder prototype for industrial equipment. From the initial working requirements given, a pneumatic cylinder prototype was designed and simulated on Solidwork software. To demonstrate the accuracy of the model, simulations of motion are also taken into account. Next, the dimensions of the pneumatic cylinder are calculated in full detail based on the preliminary model to ensure that they meet the design specifications. The control circuit system is then designed and calculated in accordance with the system's structure. Then, Automation Studio software is utilized to recalculate and validate the model's accuracy. Finally, a pneumatic cylinder prototype was constructed to validate the model. Prototypes are manufactured by CNC machining with the support of CAD/CAM technology. The prototype's smooth motion demonstrates the accuracy of the design process.

Sistem Kendali Upper Cylinder Berbasis Programmable Logic Control pada Area Section Mill di PT. Krakatau Baja Kontruksi

The smooth production process is one of the factors supporting the success of a manufacturing industry in producing quality products, timely completion of manufacture and low production costs. Industrial machines are an important factor in a manufacturing industry besides reliable human resources (HR) and other supporting facilities, so that industrial machines require special attention that is carried out on an ongoing basis in order to support the smooth operation of an industry. Practical work at PT. Krakatau Steel Construction is carried out in the Maintenance division, especially the electrical parts. In practice, the authors conducted field research by participating directly in the process of caring for and maintaining each machine instrument used by the company, especially field research that discussed the upper cylinder control system based on programmable logic control in the section mill area at PT. Krakatau Steel Construction. . In this study using field research techniques where the author conducted field research by participating directly in the process of caring for and maintaining each machine instrument used by the company accompanied by industrial supervisors and maintenance division employees for 1 month.

Torque characteristic improvement of dual-PM modular doubly salient permanent magnet machine using machine ratio adaptation

Machine ratio adaptation of a dual-PM modular doubly salient permanent magnet machine (DPM-DSPM) was proposed to improve the torque characteristics. An optimization design process was examined. Essential machine indicators were investigated to obtain an optimal machine ratio. It was found that p = 4 and a 0.8 rotor pole width ratio of the DPM-DSPM produced the best essential machine indicator that provided the appropriate magnetic flux path and high flux regulation quality. Then, the DPM-DSPM with an optimal machine ratio was investigated based on open-circuit and on-load tests and then compared to a reviewed DPM-DSPM. The results, based on 2-D finite element analysis, verified that the optimal proposed DPM-DSPM had better torque characteristics. In particular, the optimal proposed structure produced 35.04% higher electromagnetic torque with 61.75% lower ripple torque than the review structure. Therefore, an optimal machine ratio for a DPM-DSPM can be beneficial in solving industrial machine problems, especially noise reduction from vibration.

Prediction Model of Strip Crown in Hot Rolling Process Based on Machine Learning and Industrial Data

The strip crown in hot rolling has the characteristics of multivariablity, strong coupling and, nonlinearity. It is difficult to describe accurately using a traditional mechanism model. In this paper, based on the industrial data of a hot continuous rolling field, the modeling dataset of a strip steel prediction model is constructed through the collection and collation of the on-site data. According to the classical strip crown control theory, the important process parameters that affect the strip crown are determined as input variables for the data-driven model. Some new intelligent strip crown prediction models integrating the shape control mechanism model, artificial intelligence algorithm, and production data are constructed using four machine learning algorithms, including XGBoost, Random Forest (RF), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). The overall performance of the models is evaluated using error indicators, such as Mean Absolute Percentage Error (MAPE), Root Mean square Error (RMSE), and Mean Absolute Error (MAE). The research results showed that, for the test set, the determination coefficient (R2) of the predicted value of the strip crown model based on the XGBoost algorithm reached 0.971, and the three error indexes are at the lowest level, meaning that the overall model has the optimal generalization performance, which can realize the accurate prediction of the outlet strip crown in the hot rolling process. The research results can promote the application of industrial data and machine learning modeling to the actual strip shape control process of hot rolling, and also have important practical value for the intelligent preparation of the whole process of steel.

Random Interpolation Resize: A free image data augmentation method for object detection in industry

In the inspection task of industrial machine vision, insufficient data frequently occurs, and data enhancement can artificially incorporate the prior knowledge of human vision, expand the image data, and improve the model’s performance, which has become the model’s standard configuration. However, the majority of current data enhancement methods are intended for general scenes, and there are few image enhancement methods designed particularly for industrial object detection. Furthermore, many data enhancement methods require additional training time or annotation recalculation. This paper proposes a novel image data enhancement method, Random Interpolation Resize (RIR). We alter the interpolation method of the standard resize step in the preprocessing from fixed collocation to random selection in order to expand the image data and improve the model’s generalization and detection capabilities. There are no online editing options for annotations, and no additional training time is available. Multiple data sets were tested using various target detection algorithms, resulting in a 0.45% to 5.6% enhancement in mAP0.5 and an increase in the detection effect.

Investigating the differences in human behavior between conventional machining and CNC machining for future workforce development: A case study

Due to the high efficiency and consistent product quality, CNC machining has gained a dominant role in the modern machining industry. However, conventional machining still has its significance for certain production settings, e.g. prototyping, and machining workforce preparation. CNC and conventional machining require particular skills and knowledge, which can be unique to specific types of machine tools or overlap to a certain extent. With increasing production volume demands and an aging workforce, a need for efficient quantification, preservation and transfer of said skillsets arises to ensure effective preparation of future professionals and the undisrupted operation of the manufacturing industry. Moreover, the observed shift towards human-centered manufacturing systems in the Industry 4.0 necessitates obtaining an in-depth understanding of human roles in machining. The following paper proposes a novel research approach based on collection and analysis of eye-tracking and video data supplemented by verbal interviews, surveys and self-assessment. A conducted case study spans the entirety of the machining process, from part evaluation, cutting strategy determination, machining operations, to process re-evaluation and optimization. The results show that far greater variability in cutting strategy in terms of operation order, number of operations and used production tooling between consecutive production runs can be observed for conventional machining, with little variation in those terms noted for CNC-based production. Overall, the collected data has allowed to gain an insight into the machinist's decision-making processes and the rationale for observed cutting strategy changes, allowing for potential future application of the proposed research method in improving the machining training and potentially aiding process design by applying the outcomes of studies performed with the use of presented research method to expert systems and future CAM/CAE software solutions.

MATLAB-based fault diagnosis of industrial rotor-bearing systems

Continuous monitoring of critical rotor-bearing systems is crucial in order to prevent machine downtime, which would otherwise lower the overall output and quality. Complex modern machinery demands an upgraded intelligent fault diagnosis method that leaves minimal room for human error. This paper presents a MATLAB-based condition monitoring and fault diagnosis method for rotating machines used in sugar factories. The vibration responses are acquired through the use of data acquisition and the fast Fourier transform (FFT) analyser on real industrial machines. These signals are supplied as the input to a specially developed MATLAB program for processing in order to detect the fault and help to suggest remedies. The simple and user-friendly approach saves time and increases the effectiveness of condition monitoring in the reduction of downtime and the avoidance of catastrophic failure in industrial machines.

On Sensor Data Clustering for Machine Status Monitoring and Its Application to Predictive Maintenance

Predictive maintenance is one of the main approaches which Industry 4.0 is based on, since it aims at reducing unplanned downtime and maintenance costs of industrial machines. In this work, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">time-aware clustering-based approach to the analysis of sensor data</i> is presented for the purpose of monitoring the time evolution of the health status of an industrial machine. A possible application of the proposed framework to predictive maintenance is then proposed. As a relevant representative application scenario, the focus is on one of the key machines in a pharmaceutical plant: a freeze-dryer. The illustrated procedure allows to carry out a time segmentation of the properly sensed data. More precisely, the corresponding operational points (associated with features of the sensed data) are clustered using various algorithms, among which Density-Based Spatial Clustering of Applications with Noise (DBSCAN) turns out to be the best. The benefits of the proposed approach are (i) its general nature and (ii) the limited amount of needed features that have to be extracted from a single sensor signal. The proposed procedure is attractive when the collected data (e.g., from a single sensor) are not sufficient to build an accurate physical model of the monitored component.

Novel Adaptive Magnetic Springs for Reliable Industrial Variable Stiffness Actuation

Parallel elastic actuation is a highly promising concept for assisting preplanned trajectories such as repetitive tasks in industrial machines and robots. Nevertheless, due to the persisting challenges on spring lifetime, its full potential has yet to be leveraged in the industry. We propose a novel adaptive magnetic spring as a fatigue-free spring mechanism to enable variable stiffness actuators in long lifetime applications. The spring is designed to flexibly deal with variations in operating conditions, i.e., mass customization. We propose a co-design methodology which simultaneously optimizes the sizing of the magnetic spring (for the given machine and its operating conditions), together with the controls and ideal dynamic response of the system. This mechanism and the methodology are applied to a design problem of a weaving machine drivetrain, where the benefits of the adaptive magnetic spring are highlighted with respect to a fixed stiffness magnetic spring, and the current industrial benchmark without springs. Experimentally validated findings show a consistent and considerable improvement with respect to energy consumption and peak torque reduction of up to 47% and 64%, respectively, when comparing to the current industrial benchmark.

DESIGN AND FABRICATION OF A MANUAL CENTRIFUGAL CASTING DEVICE PROTOTYPE FOR POST COVID-19 RECOVERY GROWTH OF THE TRADITIONAL GOLD JEWELLERY INDUSTRY IN GHANA

Innovation in equipment and technology development has been identified as a powerful trigger for SMEs resilience and recovery in the context of the Covid-19 pandemic crisis. This paper presents the design, fabrication and operationalization of a simple but effective centrifugal casting device prototype, for the traditional gold jewellery industry in Ghana. The design process commenced by the conceptual design phase; detailed design phase followed by the embodiment design stage. The device was constructed using local materials. The device is easy to manufacture, maintain and relatively cheap. It can therefore be easily replicated or fabricated by ‘’wayside’’ welders. It is also easily transportable and can be used for teaching and demonstration purposes for students of tertiary institutions. The merit of this project is its innovative concept and constitutes a fundamental and important contribution to the research and development of new pioneering centrifugal casting machine for the local gold jewellery industry in Ghana.

Phone Charger Using Sun Tracking Solar Panel

Abstract: This design uses solar energy from the light, also stores it and eventually converts it into electric energy. Electric energy is used to power a wide variety of devices, from small appliances like toasters and blenders to large industrial machines. It is also used to charge batteries, power lights, and provide heat and cooling. Electric energy is also used to generate electricity for homes and businesses. In our design solar panels absorb the energy and temperature detectors descry the temperature and change the direction of the solar panel to follow the direction of the light. therefore, temperature detector plays an important part in our design. The electricity stored in the capacitor is released into the electronics through the circuit. The capacitor acts as a buffer, allowing the current to flow at a steady rate. This helps to protect the electronics from sudden surges in current that could damage them. In the preface, the main way of the design are handed, also, the purpose and significance of design are shown. In the discussion section, the details of rendering, solid work delineation and factual work are examined. The experimental setup lists the accoutrements that are used in the design and show how the process is carried out to achieve the pretensions.

Proportional Derivative – Type Iterative Learning Algorithm for a Motion Control System

In this paper, Iterative Learning Control (ILC) combined with a Proportional Derivative (PD) regulator is proposed to deal with the problem of designing a control signal for motion control systems. The main idea in iterative learning control is to gradually improve the performance of the system by exploiting data from the previous iterations. The learning control algorithm can obtain a better tracking control performance for the next run and hence outperforms conventional control approaches such as Proportional Integral Derivative (PID) controller and feedforward control. The main area of application for ILC is control of industrial robots and CNC machine tool, printing, and other industrial applications. The learning algorithms can also be used in combination with other control techniques. For example, learning feedforward control is designed in the first iteration. Then iterative learning control is applied to improve performance in the subsequent iterations. In addition, the conventional feedback regulator is designed in combination with iterative control to deal with uncertainty. Simulation results demonstrate the potential benefits, sensitivity and robustness of the proposed method.

STRUCTURE OF THE COMPUTER MODULE OF THE KDAM PROGRAM FOR DETERMINING KINEMATIC AND DYNAMIC PARAMETERS OF THE INPUT LINK

The development of the computer module of the KDAM program for determining the kinematic and dynamic parameters of the input link of the mechanisms of light industry machines allows to determine the coordinates, projections of speeds and accelerations of the center of mass of the crank and the point of attachment of the links of the Asura groups. During the dynamic analysis of the operation of the input link, the projections of the full reaction in the hinge of the input link are determined. The obtained results are used in the program to assess the intensity of technological processes of the light and textile industry when determining the change in the relative tension of the thread in the filling zones on the technological equipment. Lever mechanisms are widely used in light and textile industry machines. The working bodies of the lever mechanisms come into contact with the threads during operation. Depending on the trajectory of the movement, the angle of coverage of the working bodies of the mechanisms of the light and textile industry machines changes. A change in the value of the angle of coverage leads to a change in the tension of the threads. When the coverage angle increases to critical values, the thread may break and the technological equipment may stop. The tension of the thread increases when moving through the refueling zones of the thread feeding system on the technological equipment. This increase is due to the interaction of the thread with the guides and tension devices. The maximum tension value will be in front of the working area. A sharp increase in tension leads to a violation of the normal course of the technological process of thread processing. The development of new schemes of the thread feeding system requires an operational assessment of the value of the tension in front of the working zone. The development of special computer programs for determining the tension in the working area makes it possible to determine the necessary technological parameters. The objective function in the tasks of optimization of technological processes is the minimum necessary tension. The variable parameter in the objective function is the sum of the angles of coverage of the working bodies by the thread. The use of a computer program allows you to determine the tension and change in relative tension in the filling zones of technological machines, which allows you to optimize the shape of the thread supply line even at the stage of designing the technological process. Computer determination of kinematic and dynamic parameters of flat mechanisms allows to determine the parameters used in determining the intensity of technological processes. The study of the influence of the design of the thread tension device on the conditions of its interaction with the thread, taking into account its unevenness in the diameter of the cross section, is important in determining the thread tension. The tension of the thread increases when moving through the refueling zones of the thread feeding system on the technological equipment.

Smart Home automation by using intelligent electricity dispatch

The internet of things (IoT) is connecting the devices and tools to the internet network to be controlled by websites and smart phone applications remotely, also, to control tools and instruments by codes and algorithms structures for artificial intelligence issues. In case we want to create advanced systems using python algorithms, Wi-Fi or Ethernet connection is connected to our tools, equipment, and devices controlling them by smart phone applications or internet websites. That’s actually the simplified definition of IoT. Farther than just using the IoT as a smart home to operate lamps or other home-use devices, it can be used as a security system or an industrial-use system, for example, to open or close the main building gate, to operate full automatic industrial machine, or even to control internet and communication ports. And more ideas can be done by using IoT technology. A huge industrial facilities or governmental institutions have much of lamps. Employees sometimes forget to turn them off in the end of the day. This research suggests a solution that can save energy by letting the security to control lighting of the building with his smart home by Blynk application. The lamps can be controlled by switches distributed in the building and Blynk application at the same time with a certain electrical installation. This research presents a simple prototype of smart home, or the easy way and low cost to control loads by Wi-Fi connection generally

Industrial Machinery Components Classification: A Case of D-S Pooling

Industries are increasingly shifting towards unmanned and intelligent systems that require efficient processing and monitoring of structures across various applications, ranging from machine manufacturing to waste disposal. In order to achieve the goal of intelligent processing, it is crucial to accurately classify and differentiate various components and parts. However, existing studies have not focused on simultaneously classifying electro-mechanical machinery components. This poses a challenge as these components, including capacitors, transistors, ICs, inductors, springs, locating pins, washers, nuts, and bolts, exhibit high intra- and inter-class similarity, making their accurate classification a tedious task. Furthermore, many of these components have symmetrical shapes but are asymmetrical among different classes. To address these challenges, this article introduces a new double-single (D-S) pooling method that focuses on the higher resemblance of seventeen electro-mechanical component classifications with minimum trainable parameters and achieves maximum accuracy. The industrial machine component classification model (IMCCM) consists of two convolutional neural network (CNN) blocks designed with a D-S pooling method that facilitates the model to effectively highlight the differences for the higher similar classes, and one block of grey-level co-occurrence matrix (GLCM) to strengthen the classification outcome. The extracted fused features from these three blocks are then forwarded to the random forest classifier to distinguish components. The accuracy achieved by this proposed model is 98.15%—outperforming the existing state of the arts (SOTAs) models, and has 141,346 trainable parameters– hence, highly effective for industrial implementation.

Accuracy Characterization of a MEMS Accelerometer for Vibration Monitoring in a Rotating Framework

Active and passive vibration control systems are of paramount importance in many engineering applications. If an external load excites a structure’s resonance and the damping is too low, detrimental events, such as crack initiation, growth and, in the worst case, fatigue failure, can be entailed. Damping systems can be commonly found in applications such as industrial machines, vehicles, buildings, turbomachinery blades, and so forth. Active control systems usually achieve higher damping effectiveness than passive ones, but they need a sensor to detect the working conditions that require damping system activation. Recently, the development of such systems in rotating structures has received considerable interest among designers. As a result, the development of vibration monitoring equipment in rotating structures is also a topic of particular interest. In this respect, a reliable, inexpensive and wireless monitoring system is of utmost importance. Typically, optical systems are used to measure vibrations, but they are expensive and require rather complex processing algorithms. In this paper, a wireless system based on a commercial MEMS accelerometer is developed for rotating blade vibration monitoring. The proposed system measurement accuracy was assessed by means of comparison with a reference wired measurement setup based on a mini integrated circuit piezoelectric (ICP) accelerometer adapted for data acquisition in a rotating frame. Both the accelerometers were mounted on the tip of the blade and, in order to test the structure under different conditions, the first four blade resonances were excited by means of piezoelectric actuators, embedded in a novel experimental setup. The frequency and amplitude of acceleration, simultaneously measured by the reference and MEMS sensors, were compared with each other in order to investigate the viability and accuracy of the proposed wireless monitoring system. The rotor angular speed was varied from 0 to 300 rpm, and the data acquisitions were repeated six times for each considered condition. The outcomes reveal that the wireless measurement system may be successfully used for vibration monitoring in rotating blades.

Weaving with a new warp yarn break detection system using contact linear image sensors instead of drop wires

A thin metal plate called a drop wire is placed on warp yarns during a weaving process in order to stop the loom in case of a broken warp yarn and to fix it. Drop wires are placed on each warp yarn to ensure the flow of warp yarns at a certain tension level and to stop the loom when a warp yarn is broken. In some cases, the drop wire may not fall due to dirtiness or abrasion of the drop wire and the weaving process continues with a broken warp yarn which is an unwanted situation. In this study, an alternative method to detect broken warp yarn is proposed based on contact image sensor. Therefore the need for drop wires is eliminated. The contact image sensor is not at full scale of the weaving machine, it can only acquire a linear image only a small portion of the total warp yarns, therefore a carrier module is implemented on the machine to carry the sensor and cover all the warp yarns. A complete image is then formed at the end of each cycle, and the total number of warp yarns is evaluated. If this value is less than the expected number of warp yarns, a broken warp yarn is detected, and the system generates an alarm. The system is implemented on an industrial scale weaving machine and the warp yarns are detected with 99% performance.

A novel tool path planning and feedrate scheduling algorithm for point to point linear and circular motions of CNC-milling machines

The manufacturing process on CNC machines is directed by the Numerical Control (NC) files which include the main tool paths as a combination of numerous instructions such as lines and arcs specified by the G01 and G02/G03 commands. In the literature, the lines and arcs generally are converted to a parametric curve based on the global tool path smoothing or local corner smoothing algorithms. However, the smoothing methods cause the deflection from the tool path in NC-File. In cases where the deflection from the main tool path is not acceptable, point-to-point motion is necessary instead of the smooth trajectory. This study presents a novel trajectory planning and feedrate scheduling algorithm for point-to-point control of linear and circular paths in the NC-File. The proposed method prevents the feedrate fluctuations at the junction points and generates the tool path without deflection from the NC-File. Also, the feedrate scheduling algorithm decrease the operating time of the machine significantly compared to traditional method of point-to-point motion. This study is verified on an industrial 3-Axis CNC-Milling machine by the operating of the roughing milling, drilling, slotting, and face milling process. The measurements of final product and real-time trajectory tracking results show that the proposed method is effective in industrial applications of CNC-Milling machines in which point-to-point motion is necessary.

Interpretable SAM-kNN Regressor for Incremental Learning on High-Dimensional Data Streams

ABSTRACT In many real-world scenarios, data are provided as a potentially infinite stream of samples that are subject to changes in the underlying data distribution, a phenomenon often referred to as concept drift. A specific facet of concept drift is feature drift, where the relevance of a feature to the problem at hand changes over time. High-dimensionality of the data poses an additional challenge to learning algorithms operating in such environments. Common scenarios of this nature can for example be found in sensor-based maintenance operations of industrial machines or inside entire networks, such as power grids or water distribution systems. However, since most existing methods for incremental learning focus on classification tasks, efficient online learning for regression is still an underdeveloped area. In this work, we introduce an extension to the SAM-kNN Regressor that incorporates metric learning in order to improve the prediction quality on data streams, gain insights into the relevance of different input features and based on that, transform the input data into a lower dimension in order to improve computational complexity and suitability for high-dimensional data. We evaluate our proposed method on artificial data, to demonstrate its applicability in various scenarios. In addition to that, we apply the method to the real-world problem of water distribution network monitoring. Specifically, we demonstrate that sensor faults in the water distribution network can be detected by monitoring the feature relevances computed by our algorithm.

Cloud Based Fault Diagnosis by Convolutional Neural Network as Time-Frequency RGB Image Recognition of Industrial Machine Vibration with Internet of Things Connectivity.

The human-centric and resilient European industry called Industry 5.0 requires a long lifetime of machines to reduce electronic waste. The appropriate way to handle this problem is to apply a diagnostic system capable of remotely detecting, isolating, and identifying faults. The authors present usage of HTTP/1.1 protocol for batch processing as a fault diagnosis server. Data are sent by microcontroller HTTP client in JSON format to the diagnosis server. Moreover, the MQTT protocol was used for stream (micro batch) processing from microcontroller client to two fault diagnosis clients. The first fault diagnosis MQTT client uses only frequency data for evaluation. The authors' enhancement to standard fast Fourier transform (FFT) was their usage of sliding discrete Fourier transform (rSDFT, mSDFT, gSDFT, and oSDFT) which allows recursively updating the spectrum based on a new sample in the time domain and previous results in the frequency domain. This approach allows to reduce the computational cost. The second approach of the MQTT client for fault diagnosis uses short-time Fourier transform (STFT) to transform IMU 6 DOF sensor data into six spectrograms that are combined into an RGB image. All three-axis accelerometer and three-axis gyroscope data are used to obtain a time-frequency RGB image. The diagnosis of the machine is performed by a trained convolutional neural network suitable for RGB image recognition. Prediction result is returned as a JSON object with predicted state and probability of each state. For HTTP, the fault diagnosis result is sent in response, and for MQTT, it is send to prediction topic. Both protocols and both proposed approaches are suitable for fault diagnosis based on the mechanical vibration of the rotary machine and were tested in demonstration.