Generation Industry Research Articles

Smart Contract Powered Framework for the Next Generation Industry 4.0 Business Model

Smart Contract Powered Framework for the Next Generation Industry 4.0 Business Model

Advanced IC Substrates for Heterogeneous Integration

“The need for high performance and smarter devices with increased functionalities is driving the industry to develop new, innovative and advanced solutions. While scaling continues, the need for bringing in more functionalities into the package is driving the increased adoption of chiplets and heterogeneous integration. While various packaging platforms are being adopted, the requirements of the application will drive the adoption of the suitable platform. With the increase in high performance computing, the need for more advanced substrates has become more critical, from technology as well as supply chain availability. The current presentation will focus on market drivers, industry challenges and solutions that AT&S is bringing to the market to address next generation industry needs.”

Evaluating Industry 4.0 Manufacturing Configurations: An Entropy-Based Grey Relational Analysis Approach

Worldwide manufacturing and service sectors are choosing to transform the existing manufacturing sector, particularly reconfigurable manufacturing systems using the technologies of the next generation Industry 4.0. In order to satisfy the demands of the fourth industrial revolution, model evaluation and assessing various candidate configurations in reconfigurable manufacturing systems was developed. The proposed model considers evolving consumer demands and evaluates manufacturing configurations using a gray relational approach. For the case at hand, it is evident that considering all possible dynamic market scenarios 1 to 6, the current manufacturing configuration, i.e., alternative 1, has 89% utilization, total 475 h of earliness and 185 h of lateness in the order demand delivery to the market, and a total of 248 throughput hours and around 1143 bottleneck hours. The main challenge is to make a perfect match between the market demands, variations in product geometry, manufacturing processes and several reconfiguration strategies/alternatives. Furthermore, it is evident that alternative 1 should be reconfigured and that alternative 3 is the best choice. Alternative 3 exhibits 86% system utilization, a total of 926 h of earliness and 521 h of lateness in the order demand delivery to the market, and a total of 127 throughput hours and around 853 bottleneck hours. A simulation framework is used to demonstrate the efficacy of each possible reconfigurable production setup. The sensitivity analysis is also carried out by adjusting the weights through principal component analysis and validating the acquired ranking order. Thus, if the decision makers want to provide a preference to all criteria, the order of the choices of configurations is found to be alternative 3, alternative 1, alternative 4, alternative 2 and alternative 5.

Thermal and mechanical characterization of under-2-µm-thick AlCrNbSiTi high-entropy thin film

High-entropy alloys (HEAs) exhibit extraordinary physical properties such as superior strength-to-weight ratios and enhanced corrosion and oxidation resistance, making them potentially useful in energy storage and generation industries. However, thermal and mechanical properties of HEAs with various compositions vary significantly. Furthermore, these properties have rarely been investigated simultaneously owing to material or instrumentation limitations. Herein, we synthesize an HEA (AlCrNbSiTi) coating with a thickness of less than 2 μm. We customize a frequency-domain photothermal testing system to characterize the thermal and mechanical properties of the proposed coating with high accuracy. Owing to the large mixing enthalpy of the Al-Ti, Nb-Si, and Ti-Si pairs in the coating, its hardness and elastic modulus are 15.2 and 254.7 GPa, respectively, which are higher than those of previously reported HEAs. The thermal conductivity of the AlCrNbSiTi coating is characterized to be 2.90 W·m−1·K−1, within the expected range and well explained by the free-electron consistency diversity and phonon scattering from the amorphous structure. Additionally, the coating exhibits adequate wear performance, with a wear rate of 5.4 × 10−8 mm3·N−1·m−1. This relatively low thermal conductivity, combined with extraordinary mechanical properties, makes the proposed material an excellent candidate as a protective coating material for nuclear reactor components which require high strength, irradiation resistance, and thermal protection.

Geologic Carbon Storage of Anthropogenic CO2 under the Colorado Plateau in Emery County, Utah

Geologic Carbon Storage (GCS) is a promising technology for storing large volumes of anthropogenic CO2 effectively and permanently. Numerical simulations are an integral part of site selection and characterization for any potential GCS site. As part of the DOE-funded CarbonSAFE Rocky Mountains Phase I project, a regional GCS analysis was undertaken to understand the efficacy of storing CO2 emissions from the power generation and heavy industry in central Utah’s favorable geology. In this study, the injection of CO2 for geologic storage was simulated in the Navajo Sandstone Formation in Emery County, Utah. Carbon dioxide was sourced from regional power generation stations and heavy industries throughout Utah, with an emphasis on emissions reduction at the Hunter Power Plant near Castle Dale, Utah. A simulation grid was extracted from the project’s geological model encompassing an area around Price, Huntington, and Castle Dale in central Utah. The Navajo Sandstone Member of the Glen Canyon Group was the target of CO2 injection with the overlying Carmel formation providing the primary seal. A suite of simulations was performed assessing the viability of this area for permanent CO2 storage. Results indicate that the area can not only store 46 million metric tons of anthropogenic CO2, meeting the project goals, but this area has the capacity to securely store at least 1.3 billion tons of CO2, suggesting the injection site and surrounding geology are suitable locations for commercial-scale GCS.

Industrial Internet of things-based solar photo voltaic cell waste management in next generation industries.

Nowadays, modern industries generate their energy by using renewable solar. The rapid increase in photovoltaic (PV) module installations provides a better energy conversion, but their life cycle is a major concern. This research paper focuses on the recycling process for solar PV modules using the Internet of Things in industries. The smart bin with the Internet of Things (IoT) utilizes a machine learning approach to collect solar waste. The proposed smart bin uses k-Nearest Neighbor's algorithm (k-NN) and Long Short-Term Memory (LSTM), a network-based learning algorithm. These algorithms are useful in updating the level of the bin via alert messages. It also helps in identifying the type of waste material. The k-NN algorithm provides 83% accuracy in predicting the bin level in a real-time testing environment. The smart dust bin classifies the waste materials, and notifies its level to the collection center through the IoT platform when the level reaches a prescribed threshold, the signal corresponding to the level is passed to the common waste collection unit. IoT is connected to Cloud Server. It helps to predict the level of the smart bin. Delay is introduced in the order of 3-8s while the alert message is sent to the common waste collection unit. The system monitors the smart bin levels and sends the notifications to alert and initiate the collection unit. Real-time mobile app monitors the bin's level and location. The cloud IoT analytics analyze the solar e-waste in a different locations in industries.The proposed system works better and provides accurate results by using machine learning approach.

Investigations of the Ultimate and Proximate Analysis of Coal Samples From the Singrauli Coalfield, India

Coal is a mixture of heterogeneous sedimentary rock materials; it includes organic and inorganic constituents. The utilisation potential of any coal depends on its chemical constituents, and the gross calorific value defines its usefulness in thermal power generation and other industries. In this study, we collected non-coking coal from the Singrauli coalfields region in India. These coals have been analysed for proximate and ultimate analysis and GCV (gross calorific value). The grade of coal varies from G6 to G9. De-ashing and clean coal technology are required. This study will reveal the reasons for grade variation in coal, which will be useful in thermal power industries.

Modelling of Reactive and Non-Reactive Multiphase Flows

Multiphase flows are found in several industrial processes encompassing power generation, pharmaceutical and chemical industry and agriculture [...]

Inclusion of IoT, ML, and Blockchain Technologies in Next Generation Industry 4.0 Environment

Industry 4.0 is a revolutionizing concept which has a more significant implication to numerous industries of various sectors worldwide. Industry 4.0 acts as a technical catalyst in accelerated growth through systematic adaptation and new-generation cutting-edge engineering tools. IoT, Blockchain, Artificial Intelligence, Augmented Reality, 3D printing, Big-data analytics, cloud-computing are some of the primary technologies which work in join hand to make the vision of Industry 4.0 successful. In addition, it promotes autonomous industry, which in turn considerably reduces human intervention, thus resulting in efficient working and safeguard companies' interests. Due to the faster adaption of Industry 4.0, authors have attempted to define each and every concept related to it and to explore a futuristic roadmap, special focusing on its implication in the contemporary market. In the 21st century Industry, 4.0 is the need of the hour, so it has become essential to understand the state-of-the-art technologies involved.

Docker Swarm and Kubernetes Containers for Smart Home Gateway

The Internet of Things (IoT) for warehouse automation in new generation industries and home gateway in smart homes have very tight latency requirements for data computation and response. To satisfy the requirements, many studies have moved the computation close to IoT devices by virtualizing the services using containers and deploying them on IoT gateways. They do not account for limited resources at IoT gateways, and without careful provisioning, IoT gateways can easily become overloaded and inevitably increase the computation time. To enable efficient service provisioning on IoT gateways, this article presents the empirical evaluation of docker swarm and Kubernetes (K8s) container solutions, on resource constrained devices such as Raspberry Pi boards. We have used multiple open source intrusion detection systems and deep learning based data analytic solutions in our experiments to evaluate creation time, CPU utilization, and memory usage. Our results reveal that creation time and memory usage of containers are critical factors in constrained environments. The lessons learned from our empirical case studies provide essential guidelines for micro-services architectures in IoT gateways.

Experimental Analysis and Control of a Wind-Generator System through a DC-DC Boost Converter for Extremum Seeking

The advancement in technology has significantly improved the living standard, having a direct impact on the quality of life, at the cost of ever-increasing energy demand. Renewable energy resources such as the Wind Energy Conversion System (WECS) provide variable power generation. The current paper presents the results of the experimental analysis on the control of the wind-generator system based on SCIG through a dc/dc boost converter for extremum power-seeking. Since the Squirrel Cage Induction Generator (SCIG) is widely used in wind industries for power generation, their performance will be compared. The proposed system can operate either on a variable or at a nearly constant speed, with higher load capacity, while its power consumption is comparatively lower. The research will enhance the wind power at different wind speeds, to acquire the extremum power point tracking system from the wind power energy system by using two different machines.

Experimental and Numerical Investigation into Residual Stress During Turning Operation for Stainless Steel AISI 316

RS (residual stresses) represent the main role in the performance of structures and machined parts. The main objective of this paper is to investigate the effect of feed rate with constant cutting speed and depth of cut on residual stresses in orthogonal cutting, using Tungsten carbide cutting tools when machining AISI 316 in turning operation. AISI 316 stainless steel was selected in experiments since it is used in many important industries such as chemical, petrochemical industries, power generation, electrical engineering, food and beverage industry. Four feed rates were selected (0.228, 0.16, 0.08 and 0.065) mm/rev when cutting speed is constant 71 mm/min and depth of cutting 2 mm. The experimental results of residual stresses were (-15.75, 12.84, 64.9, 37.74) MPa and the numerical results of residual stresses were (-15, 12, 59, and 37) MPa. The best value of residual stresses is (-15.75 and -15) MPa when it is in a compressive way. The results showed that the percentage error between numerical by using (ABAQUS/ CAE ver. 2017) and experimental work measured by X-ray diffraction is range (2-15) %.

Adaptive Spectrum Aggregation Regimen for Downlink NR-gNodeB and Device to Device Systems in 5G

Device to device communication (D2D) and internet of things are anticipated to rule the globe in the upcoming days. The ever increasing demand for data traffic and throughput pose a unique venture in fifth generation (5G) industry, including limited spectrum and mass device connectivity. The proposed system signifies the specifications putforth by third generation partnership project for D2D communication underlying cellular network. It is highly focused on design and development of its physical layer and link layer . This hybrid 5G non-standalone system is adaptive to operate in different frequency bands. Since the users of D2D and new radio g-NodeB apportion same spectrum, interference would be the resultant. To quell such interference effects and also to maintain high capacity and throughput, here two mechanisms, namely spectrum aggregation and scheduling are emphasized. More over scheduling on component carriers are implemented successfully. At the end, the experimental outcomes prove that the aforementioned two methods results in rapid data transfer rate and eminent capacity with 0.5 ms latency in the controlled path loss environment.

사고조사위원회 보고서를 통해서 본 사업장 원하청 구조와 안전관리에 대한 연구(발전업, 조선업종을 중심으로)

사고조사위원회 보고서를 통해서 본 사업장 원하청 구조와 안전관리에 대한 연구(발전업, 조선업종을 중심으로)

Computational Investigation of Interface Stresses in Duplex Structure Stainless Steels

Duplex structural stainless steels (DP) are highly corrosion-resistant with austenite (γ)-ferrite (δ) compositions and processing routes. These DP steels are used in chemical, petrochemical, marine, power generation, and nuclear industries due to their unique properties. Experimental data of 2D slices obtained from optical micrography were used in a multilevel finite element analysis to investigate the interface stresses between the two phases of DP material. Level #1 (macro-level) finite element analysis involves homogenizing the 2D slices based on their representative constituent properties to identify high-stress regions. Level #2 (micro-level) finite element analysis was conducted with representative austenite/ferrite microstructure identified from level #1 analysis. The results from finite element simulations from both macro- and micro-levels revealed that interface stresses are affected by the phase’s microstructure distributions. Micro-level analysis revealed maximum interface stress is much higher than macro-level analysis. It was also found that stress localization occurred at the ferrite/austenite interfaces based on their distributions.

Physical background and simulation of creep in steels

The simulative accelerated creep test (ACT) was developed as a response to an overall need of gaining in a short time useful physical data for determining long-term behaviour of materials exposed to operation under stress at elevated temperatures in power generation and chemical processing industries. Additionally, the recently frequent power plant shut-downs due to adding solar/wind power to the net call for creep-fatigue data which standard creep tests cannot provide. In response to these needs, a thermal-mechanical fatigue procedure ACT was designed, taking into account physical phenomena causing microstructure transformation during creep, in particular generation of dislocation substructures, their role in nucleation of voids and cracks, intensification of carbide precipitation and decay of mechanical properties during long-time exposure to elevated temperatures. The actual ACT procedure generates adequate data for calculating true lifetime of the tested creep resisting material for a nominal stress.

Influence of Cutting Speed on Residual Stresses by Machining of AISI 316L

RS have an important role in the performance of components and machined structures. The objective of this paper is to study the influence of cutting speed on RS in workpieces that are formed in orthogonal cutting. AISI 316L stainless steel since it has been used in many important industries such as chemical, petrochemical industries, power generation, electrical engineering, and food and beverage industry. Four cutting speeds are selected: (44, 56, 71 and 88) m/min. The alloy was machined by turning at constant depth of cut and various feed rate from (0.065 to 0.228) mm/rev. Residual stresses are examined by X-ray diffraction. The best results of RS obtained are (-3735.28, -1784.95, -330.142, -218.747, -890.758, -2999.632, -2990.401) MPa. Increasing the cutting speed from (44-56) m/min. reduces the compressive residual stress by (21.4 %), while from (71-88) m/min the RS is reduced by (19.3 %). Finally, the RS at cutting speeds are changed from compression to tension.

Role of hr development for productivity & efficiency-case for apparel manufacturing

Productivity and efficiency improvements are crucial for sustainable growth of manufacturing firms. Apparel manufacturing is a crucial industry for employment generation in developing economies due to its labour intensive nature. This paper highlights the significance of sound human resource (HR) management and development for ensuring optimum productivity and efficiency in apparel manufacturing. A qualitative and descriptive study using secondary sources has been conducted for the purpose of this paper. It is found that productivity and efficiency is linked to labour performance and influenced by training and development. It is imperative for firms to focus on improving the skills and quality of workers and managerial personnel. In addition, suggestions are presented for leveraging technology and digital tools for developing HR in apparel manufacturing firms.

AxFlow expands in Irish valve market

AxFlow Ireland has acquired Irish Pumps & Valves Ltd, a supplier of valves, actuators, pumps and ancillary equipment to the wastewater, power generation and process industries.

Study about Mechanical Properties of Materials Obtained by Thermoforming

Plastic materials have played a very important role in the development of our modern civilization. It can be said that plastic materials are a tribute to man’s creativity and inventiveness. Plastic materials are the key to the innovation of many products and technologies in various sectors such as electrical and electronic equipment, packaging, civil engineering, agriculture, power generation, medical and pharmaceutical industry, aerospace and not last automotive industry. An increasingly important aspect in the automotive industry, in terms of fuel consumption is the lower weight of the components, while maintaining a very high stiffness. The difficulties of the design process of thermoformed parts is mainly due to the lack of knowledge by designers about the constraints and forces that are required during the process of thermoforming.