Industrial Machine Research Articles

IoT-Driven Intelligent Scheduling Solution for Industrial Sewing Based on Real-RCPSP Model

Applying IoT systems in industrial production allows data collection directly from production lines and factories. These data are aggregated, analyzed, and converted into reports to support manufacturers. Business managers can quickly and easily grasp the situation, making timely and effective management decisions. In industrial sewing, IoT applications collect production data from sewing lines, especially from industrial sewing machines, and transmit that data to cloud-based systems. This allows businesses to analyze production situations, thereby improving management capacity. This article explores the implementation of IoT applications at industrial sewing enterprises, focusing on data collection during the production process and proposing a data structure to integrate this information into the company’s MIS system enterprise. In addition, the research also considers applying the Real-RCPSP problem to support businesses in planning automatic production operations.

Cybersecurity Requirements for Industrial Machine Control Systems

The first part of this paper discusses the research context, taking a closer look at the development of Industry 4.0 and the growing importance of the IIoT, which entails new cybersecurity challenges. The issue of cyber threats and the need to increase the level of protection in machine control systems, which are particularly vulnerable to attacks due to their connection to the network, is also presented. The Introduction concludes with a presentation of the article’s objective, which is to analyze the requirements of security levels (SLs) and the implementation of relevant international standards. The next section reviews the current research on cybersecurity in machine control systems. This section also points out the research gaps that the article aims to fill. The next section presents the risk assessment used to ensure safety during machine operations based on ISO 12100. The article describes safety functions implemented in machine control systems, including the SIL (safety integrity level) and PL (performance level) specifications. An important part of the article is the creation of a relationship between PL and SL, showing how the safety functions of systems are related to protection against cyber threats. The last part of the article gives a case study in the form of examples of machines and their control systems performing safety functions, which require various SLs depending on the PLs.

Anomaly characterization for the condition monitoring of rotating shafts exploiting data fusion and explainable convolutional neural networks

Condition monitoring of rotating shafts is a critical task in the maintenance of mechanical systems. Rotating shafts are essential components in many machines, and their failure can result in serious consequences, including system downtime, production loss, equipment damage, and safety issues. Advanced sensor technologies and deep learning algorithms have facilitated data collection and processing, providing vital insights into system health. However, despite the vast availability of sensors, the data used to train these algorithms often consists of single-nature signals, and systems are typically damaged to simulate different faulty scenarios. Additionally, the perceived opacity of deep learning algorithms, often referred to as black-box models, has raised concerns about their credibility in critical domains. Hence, this paper addresses these challenges by (i) proposing various explainable artificial intelligence (XAI) methods in a rotating shaft case study, (ii) using a novel data-fusion approach to combine multiple signals, and (iii) leveraging data from an innovative experimental set-up mimicking real-world industrial machines. The employed experimental set-up is equipped with a diverse array of sensors capable of capturing signals of varying nature, and it streamlines the automated introduction of four distinct fault types in an innovative manner. Applied to convolutional neural networks, the employed XAI methods enhance transparency in deep learning models, providing practical insights for complex systems. This approach not only addresses the limitations associated with single-nature signals and simulated faults but also contributes to the credibility and interpretability of deep learning models in critical applications.

Impact of Manufacturing Technology on The Quality of School Uniforms Produced in Small and Medium Scale Industries in Kumasi, Ghana.

Purpose: This study assessed manufacturing technologies small/medium-scale industries use to produce quality school uniforms in the Kumasi Metropolis. Design/Methodology/Approach: The study adopted a descriptive survey design with a qualitative approach. The instruments used for the data collection were interviews and observation guides. Since the data was qualitative, it was analysed using thematic content analysis. Findings: The result showed that industrial sewing machines, overlocking machines, and cutting machines, among others, were the most common machines used to produce school uniforms. The technology used in producing school uniforms positively impacted the products, making the uniforms meet average standards for their clients. Research Limitation: The study is limited by the geographical coverage and the number of small medium scale firms involved. Practical Implication: Given the need for a technological support system for expertise and advanced machinery, small/medium-scale firms will be well-positioned to compete with well-resourced big manufacturing companies. Social Implication: Empowering small/medium-scale industries can increase productivity and create more employment, higher economic returns, and more equitable industrial norms. Originality/Value: The study offers a novel approach to assessing the impact of manufacturing technology on the quality of school uniforms produced in small and medium-scale industries in Kumasi, thereby contributing to unearthing the industry's challenges and gaps.

A comparative review of machinability in AA2XXX and AA7XXX heat-treatable aluminum alloys: Focus on AA7050-T7451 and AA2050-T84

Heat-treated AA7050-T7451 with high fatigue strength and corrosion cracking resistance, which is frequently used in the aerospace industry, and third generation AA2050-T84 with low density, high fracture toughness, and high hardness strength is distorted due to high chip removal volumes during machining. During machining, depending on the cutting parameters, the cutting forces and high heat transfer during machining cause residual stresses to accumulate on the bulk material. Residual stresses are one of the main causes of distortion theory and one of the most important factors that increase the scrap rate in the machining industry. The observation of a higher risk of distortion in high-volume removal processes such as turning, drilling, milling, etc. has led to the optimization of cutting parameters with a view to reduce the residual stress. In AA7050-T7451 and AA2050-T84, high cutting forces and heat transfer movements vary depending on the material microstructure as well as the spindle speed, feed, cutting type, and cutting tool parameters. The aim of this review is to examine the microstructures and the causes of distortion during the milling of Al alloys, especially AA7050-T7451 and AA2050-T84 for using the aerostructures. The distortions during machining are influenced by the microstructure of the materials, the type of cutter due to chemical reactions, and the cutting parameters due to the heat and cutting forces generated. It can be observed and improved by machining variables such as chip morphology, cutting force analysis, and surface roughness. The effect of cutting parameters such as spindle speed, feed rate, and cutting speed on the distortion of AA7050-T7451 and AA2050-T84 are investigated. As a result, cutting parameters have a crucial effect on the residual stresses in AA7050-T7451 and AA2050-T84 materials.

Development of an individual warp beam tension control system for warp assembling machines in direct warp preparation

Warp preparation is a process prior to weaving that brings all the ends side by side and then winding them on a warp beam with the same tension and length at a predetermined width, density and length. Modern warp preparation machines employ feedback control systems controlling total or individual warp tension. Older warping machines in industry mostly do not have a precise feedback tension control systems and do not match with the quality requirements of today’s market expectations. This paper presents a research in which an individual section beam warp tension control system is explained for converting older beam-assembling machines to a new technology in a direct warping process. The total warp tension of each section warp beam was measured and breaking torque of the magnetic powder brake was adjusted when a deviation occured from the set tension value. It was shown that average loom efficiency increased by 5.65% despite an increase in average loom speed from 663 to 733 rpm in an air jet loom factory. The fabric fault ratio decreased from 3.45% to 1.04% per 100 m of fabric and warp waste decreased from 0.5% to below 0.2% with the new individual section warp beam tension control system adapted to an older version of the assembling machine having total tension control and section warp beam pneumatic brake system.

Optimize the Synthesis Error in an Eight‐Bar Peaucellier–Lipkin Mechanism Using an Objective Function Maximization Approach and Application to Load Lifting

ABSTRACTThis work deals with optimizing the synthesis error in an eight‐bar Peaucellier—Lipkin mechanism, for its dimensional synthesis and applications in load‐lifting machines. A new method for the formulation of the problem of maximizing the objective function is proposed and makes it possible to obtain from the PSO algorithm a minimum synthesis error emin = 9.07E−06 mm for the generation of the straight trajectory when the search interval for the lengths of the bars is [1 mm, 15 mm] and a minimum error emin = 1.47E−04 mm when the search interval is [1000 mm, 15,000 mm]. For 10 simulations in Case 1 the average convergence time is tm = 55 s with the largest iteration at 10 (for t = 159 s); for 100 simulations in Case 2, the tm = 229 s with the largest iteration at 136 (for t = 2294 s). The minimum error of Case 1 is compared with the results of authors in the literature on the generation of the right trajectory because the search space is approximately equal. In the literature, emin = 0.648358 mm with the GA‐DE algorithm in 2010, emin = 2.3667E−005 mm with the MKH algorithm in 2016, emin = 0.027145 mm with the SAP‐TLBO algorithm in 2017, emin = 3.7E−4 with the GA algorithm in 2019. This new method brings a plus, because even when the search space is very large, the algorithm converges quickly and it allows the study to be extended to the generation of circular trajectories by just modifying the ratio between the frame bar and the crank bar. The practical implications of achieving an error as low as 9.07E−06 mm are the design of high‐precision industrial machines with reduced vibration, noise, and premature wear of joints. The results of the post‐design FEM analysis show that for a 1.4571 steel (X6CrNiMoTi17‐12‐2) with a thickness of 50 mm and a joint with a radius of 500 mm, the mechanical device obtained can support a load of 1500 kg.

Microfluidic paper-based analytical soft actuators (μPAC).

Soft actuators have developed over the last decade for diverse applications including industrial machines and biomedical devices. Integration of chemical sensors with soft actuators would be beneficial in analyzing chemical and environmental conditions, but there have been limited devices to achieve such sensing capabilities. In this work, we developed a thin-film soft actuator integrated with a paper-based chemical sensor, termed a microfluidic paper-based analytical soft actuator (μPAC). μPAC consists of (1) a silicone thin film with a 3D-printed pneumatic chamber and (2) a cellulose paper. This cellulose paper offers dual functions: the strain-limiting layer of a soft actuator and the substrate for the chemical sensor for a paper-based analytical device (μPAD). We characterized the design parameters of the actuators-namely, (1) thickness of silicone thin film, (2) chamber length, and (3) Young's modulus of silicone thin film-to evaluate the actuation performance. These characterizations suggested that the cellulose paper served as a suitable self-straining layer of the actuator, making μPAC a chemical sensor that can actuate simultaneously. Highlighting the unique capability of μPAC, we demonstrated the local detection of pH on the curved target surface. Overall, this research demonstrated the rapid fabrication of actuating chemical sensors with a unique design by combining soft actuators and μPAD, enabling chemical sensing on various surface topologies by dynamically making conformal contact.

Experimental Studies into the Super Finishing of Nitronic-60 by One - Way Extrusion Honing Process

Abstract: The surface requirements of engineering components used varies widely depending on where the components surface is being applied for. Fabrication of surfaces with desired characterises on materials possessing high hardness is the need of the hour to effectively overcome the contemporary application challenges. The required surface generation of these difficult to cut material possessing intricate and unable to reach geometries could not be met by conventional finishing processes. So, several non-conventional finishing processes have been developed over the years to meet the ever-growing requirements of the industry, among them, Extrusion Honing or Abrasive Flow Machining where a flexible media loaded with abrasive particles acting as cutting tool can be successfully used to fine finish the surface. In the present study, the abrasive loaded flexible cutting media is extruded using one-way extrusion honing process and the effect of this on the surface characterises of Nitronic-60 is studied. The surface roughness is examined using a stylus type surface measuring instrument and the surface texture of the specimen before and after the process is analysed using SEM images. The results shows a significant enhancement in surface finish using EH/AFM process and the process found quite capable in removing the surface irregularities and producing surface with enhanced overall surface integrity.

Sound-Based Industrial Machine Malfunction Identification System by Deep Learning Approach

Sound-Based Industrial Machine Malfunction Identification System by Deep Learning Approach

Applying Jira - A Tool for the Organization and Optimization of Work Processes in the Machine Industry Based on the Experience of the IT Industry

This article explores the application of Jira, which is considered a widely used project management and issue-tracking tool, in the work processes of the machine and IT industries. Jira’s flexibility and adaptability make it a valuable tool for streamlining workflows, improving collaboration between employees, and improving overall efficiency in the dynamic and complex environment of the machine industry. The article gives a brief overview of the main Jira’s features, discusses its relevance to the machine industry. The findings highlight the potential benefits of integrating Jira into the machine industry workflows, highlighting its positive impact on project management and productivity. Jira, a web tool developed by Atlassian, is extensively used for project management across different organizations, significantly enhancing efficiency. This article outlines Jira’s features, explores its significance in project management, and showcases real-world examples from a machine industry firm to demonstrate its practical utility in organizing work. This research aims to broaden our knowledge of how Atlassian Jira can enhance project workflows and bolster project outcomes within the machine industry, based on insights gleaned from existing literature.

Toward Sustainable Machining: Design and Fabrication of Micro-Textures on Solid Carbide Endmill Using Laser Micromachining

The modern machining industry is in a constant state of search for innovative approaches that can effectively improve productivity maintaining sustainability. Micro-texturing on the cutting tools is one of the innovative methods to improve the performance of the tool. The critical aspect of research related to micro-textured surfaces includes identifying the right texturing pattern, texturing intensity, and method. This article investigates the exploration of implementing projected sinusoidal curves as a novel micro-texturing pattern on the rake face of solid carbide endmill flutes, with a specific focus on the immense potential it holds in revolutionizing machining processes. A simulation using the textured tool was performed to study the chip formation, principal stress, and deflection during the machining process and is compared in the non-textured tool. The result of the study shows a better performance of the micro-textured endmill in comparison with the non-textured endmill. Micro-texturing is accomplished by employing advanced laser technology to create precise and controlled sinusoidal curves on the end mill surfaces at a micro-scale level. The average width and depth of the micro-texture on the endmill is found to be 93 µm and 101 µm respectively.

Energy Audit of an Enterprise from Machine Industry

An energy survey of an enterprise operating in field of machine industry is carried out. During the energy audit, primary information about energy consumed by the enterprise for heating, ventilation, lighting and needs of production process for three year period was collected. Annual energy costs for three consecutive years were compared and highest energy costs were taken as a basis for determining the energy consumption baseline. Proposals for energy-saving measures are defined. The specific energy costs before and after energy-saving measures were calculated and comparisons between them were made. An assessment to optimize number of workers in the enterprise and increase its competitiveness was made. The effect of increasing the productivity of the enterprise was evaluated. Direct and indirect cost savings were estimated, realized during the implementation of energy-saving measures. Annual energy savings in machine industry enterprise as energy saved in kWh/year and in saved carbon emissions/year to the environment were presented.

Proposal and Implementation of an Integrated Monitoring Platform for Preventive Maintenance of Industrial Machines

In order to realise the efficient maintenance of industrial machines, Small and Medium-sized Enterprises (SMEs) need a system that utilises digital technology to handle everything from data collection to the visualisation of the collected data in an integrated manner. In this paper, an integrated monitoring platform using external sensor devices is proposed and implemented for the purpose of preventive maintenance of industrial machines. The proposed system performs edge processing to calculate features effective for monitoring on the sensor device, collects only the obtained features, and visualises them on a web server. In order to determine the features required by edge processing, a cycle waveform cut-out algorithm was proposed. As an evaluation experiment, the proposed system was used to detect the loosening of bolts on the support side of a ball screw. The results of the analysis showed that the dispersion value immediately after the start of uniform motion from the right end to the left end was valid, so the system was implemented as edge processing in the sensor device. In wireless transmission experiments on a testbed, an average of 20 consecutive cycles were used to achieve a 99.9% correct response rate and high detection accuracy, demonstrating the usefulness of the proposed system.

Design and Development of Small-Scale, Industrial Compression Molding Machine for Bamboo Biocomposite Boards

Bamboo production is one of the emerging sectors in the Philippines in terms of socioeconomic value and environmental sustainability. Its versatility in product design and sustainability from production to end-of-life has become popular in the local and foreign markets. However, challenges remain in utilizing bamboo and its potential as a sustainable material. During the post-processing of bamboo products, 33.5% to 70% of bamboo ends up as waste. One solution to the problem is utilizing bamboo wastes in biocomposite materials. However, the technology developed for bamboo-based biocomposite processing is only available in large industries. This project focuses on creating an industrial compression molding machine to efficiently transform bamboo wastes into biocomposite boards. The study employs a comprehensive methodology from computer-aided design and fabrication to machine and product testing and design of experiments. Results demonstrate optimal performance at 180°C and 45% waste content that meets industry standards. Beyond contributing to biocomposite production methodologies, this research addresses the imperative for sustainable practices in the evolving bamboo industry with broad potential applications in the industry.

Influence of Molybdenum Addition on the Structure, Mechanical Properties, and Cutting Performance of AlTiN Coatings

Though AlTiN coating has been intensively studied, there is still a need to develop AlTiN coating to meet the growing demand of industrial machining. One effective way to improve the performance of AlTiN coating is by adding alloying elements. In this study, AlTiN and AlTiMo coatings were deposited using multi-arc ion plating to investigate the influence of molybdenum addition on the structure, mechanical properties, and cutting performance of AlTiN coatings. Spherical droplets formed on the surfaces of both coatings, with the AlTiMoN coating exhibiting more surface defects than the AlTiN coating. The grazing incidence X-ray diffraction results revealed the formation of an (Al,Ti)N phase formed in the AlTiN and AlTiMoN coatings. Molybdenum doping in the AlTiMoN coating slightly reduced the grain size. Both coatings exhibited excellent adhesion to the substrate. The hardness (H), elastic moduli (E), H/E, and H3/E2 ratios of the AlTiMoN coating were higher than those of the AlTiN coating. The improvement in the mechanical properties was attributed to grain refinement and solution strengthening. Molybdenum doping improved the tribological properties and cutting performance of the AlTiN coatings, which was ascribed to the formation of MoO3 as a solid lubricant. These results show a path to increase the performance of AlTiN coating through molybdenum addition and provide ideas for the application of AlTiMoN coatings for cutting tools.

Modeling industrial scale washing machine process using artificial neural networks

The paper presents a dynamic model of a typical industrial washing machine fed with hot water, cold water and steam. Since industrial scale washing machines are not expressly designed to be energy efficient, there is scope to reduce energy consumption and wastewater discharge through dynamic model analysis of washing programs and washing machine units as regards heating medium parameters and prices. The paper presents a construction of the dynamic model using Aspen Hysys software that provides data to determine optimized parameters of water and steam delivered to the washing unit. The model has been validated with real data delivered by the dedicated measuring software from the washing machine manufacturer. Finally, the paper presents an economic analysis and sensitivity analysis regarding energy prices with various media parameters. Results show that the price of the washing process can be reduced by 64% depending on the parameters of the heating medium.

Differences in the acoustic characteristics of DC bias alternating arcs in argon, helium, and nitrogen

Abstract The acoustic effects of gas discharge plasma have received much attention. Previous studies have shown that cold plasma and thermal plasma have different principles of sound generation. In this paper, the differences in the acoustic characteristics of DC bias alternating arc plasma (thermal plasma) in different gas environments (argon, helium, and nitrogen) are investigated by combining experiments and simulations. Many processes in industrial machining involve this arc plasma. It was found that the acoustic characteristics of the arcs of these three gases are significantly different. The two key parameters, electrical and thermal conductivity of the gas, determine the acoustic characteristics of the arc by influencing the electric power of the arc and the heat dissipation through the anode. At the same drive current, the nitrogen arc has the largest voltage drop and the helium arc has the highest electroacoustic conversion efficiency. This results in the acoustic pressure amplitude being helium, nitrogen, and argon in descending order. The research contributes to a deeper understanding of the vocalization mechanism of arc plasma and provides theoretical guidance on gas selection for arc acoustic wave applications.

Anomaly Detection Based on Graph Convolutional Network–Variational Autoencoder Model Using Time-Series Vibration and Current Data

This paper proposes a deep learning-based anomaly detection method using time-series vibration and current data, which were obtained from endurance tests on driving modules applied in industrial robots and machine systems. Unlike traditional classification models that depend on labeled fault data for detection, acquiring sufficient fault data in real industrial environments is highly challenging due to various conditions and constraints. To address this issue, we employ a semi-supervised learning approach that relies solely on normal data to effectively detect abnormal patterns, overcoming the limitations of conventional methods. The performance of semi-supervised models was first validated using a statistical feature-based anomaly detection approach, from which the GCN-VAE model was adopted. By combining the spatial feature extraction capability of Graph Convolutional Networks (GCNs) with the latent temporal feature modeling of Variational Autoencoders (VAEs), our method can effectively detect abnormal signs in the data, particularly in the lead-up to system failures. The experimental results confirmed that the proposed GCN-VAE model outperformed existing hybrid deep learning models in terms of anomaly detection performance in the pre-failure section.

A method for signal components identification in acoustic signal with non-Gaussian background noise using clustering of data in time-frequency domain

This paper presents a novel method for fault detection in vibration/acoustic signals contaminated with non-Gaussian noise, specifically addressing the challenge of random impulsive and wideband disturbances in industrial measurements. While damage detection in Gaussian noise environments is well understood, high-amplitude non-cyclic impulsive disturbances arising from random aspects of industrial processes, such as non-uniform operations and random impacts, pose significant analytical challenges.The proposed method analyzes the distribution densities of spectral vectors derived from spectrograms. It considers a simple additive model consisting of the signal of interest (SOI) and Gaussian and non-Gaussian noise. Using the density-based spatial clustering algorithm (DBSCAN), the method isolates distinct classes of spectral vectors from the spectrogram, effectively separating different signal behaviors and extracting fault-related information. The effectiveness of the proposed method was validated using an envelope spectrum-based indicator (ENVSI) and successfully demonstrated on real signals from an industrial machine with a faulty bearing.