bluuebunny/crossref_metadata_2025_split huggingface dataset:
Dynamic Modelling and Current Control for Switched Reluctance Motor Drives in Electric Vehicles
Authors: Ibrahim Muhammed A , Alsammak Ahmed Nasser B 
Published: December 2023

Abstract
The inherent features of the Switched Reluctance Motor (SRM) make it a promising applicant for further exploration and examination in various industrial sectors, to mention but a few electric vehicles. The mechanical development of this motor is characterized by its simplicity and cost-effectiveness. Including current controllers within an SRM drive control system is of utmost importance due to their substantial influence on drive performance, dynamic response, and noise and vibration performance. This study aims to improve the SRM drive system to achieve reliability and efficiency. A complete SRM drive system is designed, constructed, and evaluated to accomplish this objective. The contribution of this study is that the consequence of the current control band of hysteresis current control (HCC) on the phase current has been intensely investigated on high-frequency torque ripple, average torque and average switching frequency. This investigation has been performed simultaneously with soft-hysteresis current control and hard-hysteresis current control. Simulation results proved that the soft HCC gives the best results. A trade-off between switching frequency and high-frequency torque ripple has been performed based on choosing the best current control of the proposed model.
Maximum Torque per Ampere Technique for Switched Reluctance Motor Drive
Authors: Ibrahim Muhammed A , Alsammak Ahmed Nasser B. 
Published: December 2023

Abstract
Numerous researchers have investigated switched reluctance motors (SRMs) as a competitor to other electrical motors for usage in electric and hybrid vehicle appliances. SRMs are fault-tolerant and have a broad operating speed range. However, they have numerous drawbacks, such as high nonlinearity in machine models, complex control, and torque ripple. This study aims to improve the SRM drive system to attain maximum torque per ampere in a wide speed range. In the design and control of SRMs, maximizing torque per ampere (MTPA) is a crucial objective. MTPA refers to the strategy of achieving the maximum possible torque output for a given current input (amperes). This is essential for enhancing the efficiency and performance of the motor. The paper focuses on achieving MTPA in an SRM drive system-based commutation angle control. Utilizing a bacterial foraging optimization algorithm (BFOA), the suitable commutation angles at each operation point are determined. The intended technique is straightforward to realize and does not require complex algorithms. Simulation comparisons on an 8/6 SRM are also presented to validate the intended control technique. The results indicate that the MTPA has been enhanced by 6.961% compared to the conventional method.
Smart Shopping Trolley with Automated Billing
Authors: Venkata Sai Prasad K. , Karthik K. , Charan Kumar O. , Prathiksha B S , Salimath Kavana 
Published: December 2023

Abstract
In today’s context, people prefer buying groceries from supermarkets/hypermarkets. However, locating essential items in a supermarket can be time- consuming, and customers often have to wait in long billing queues to complete their purchases. The COVID-19 pandemic has further complicated matters, as maintaining social distance during the billing process is challenging. To address this significant challenge, this research proposes a smart trolley based on the Internet of Things (IoT) with an advanced billing system, aiming to make shopping easier, more secure, and to eliminate the need for standing in long queues. The proposed system comprises a smart trolley equipped with an LCD display, barcode scanner, and a Raspberry Pi. This innovative model aims to revolutionize the shopping experience by streamlining the process and mitigating administrative issues. The proposed framework holds promise for widespread implementation in real-world scenarios.
Thermal Power Plant Pollution Monitoring and Control using Internet of Things
Authors: Manoharan Mahalakshmi , Padma S , Madhubalan S , Arunkumar S , Kesavan D 
Published: December 2023

Abstract
In our day-to-day life, environmental degradation and global warming are essential factors to consider. These problems were caused due to the contamination of water, air, and soil with the waste generated by industries. The pollutants level generated by the industries to be measured and controlled by central and state pollution control board. The air pollutants are the major cause for the deterioration. The existing pollutant monitoring system are complicated, inaccurate, unreliable, delayed intimation and less efficient. Hence in mettur thermal power plant, the internet of things-based pollutant gas monitoring system has been implemented. This system consists of numerous gas sensors, Arduino UNO controller, ESP8266 Wi-Fi module, ubidots server and alarm. The pollutants measured are carbon monoxide (CO), suspended particulate matters (SPM), sulphur oxides (Sox), nitrogen oxides (Nox) in air. When the pollutants level of industry is crossed the limit, the pollution control board has been intimated the exact location of the industry through internet. The pollution control board can automatically send alert SMS and email to the respective industry. This system is suitable for the recent technology industrial internet of things. The electrostatic precipitator has been installed for pollutant control. It is proved that the proposed system is a very good replacement for the conventional monitoring system done by the human. It is acted as user friendly interface for the operation of the system and is reliable. The system has attained improved pollutant control, increased stability, and consistency of the power plant and finally the safety of the employees working in the power plants and mine areas.
Microgrid Protection Challenges and Solution
Authors: Modi Sangeeta , Usha P 
Published: December 2023

Abstract
Microgrids are embryonic as inspiring solution to the various concerns such as environmental, economic, depletion of the resources for the fuel availability and power mismatch. Microgrids are going to become one of the core components of the upcoming power system. So, it is essential to understand various issues and challenges in microgrid. Power management, constant voltage and frequency, control of various distributed generators and Protection of the microgrid are major areas of concern. Not much work has been done on the protection side of the microgrid. Detecting fault and obtaining the appropriate setting of the protective gear for unconventional topology of the microgrid is very challenging. In this work a solution has been proposed and implemented in microgrid controller (MGC) for fault detection and classification in a microgrid. Effectiveness of proposed method is tested in MATLAB /SIMULINK environment.
Optimal Power Flow Management in Microgrids using Distributed Energy Resources
Authors: Nagananda R , Gopiya Naik S 
Published: December 2023

Abstract
The effective and stable functioning of microgrids using distributed energy resources (DERs) is greatly dependent on ideal power stream management. To get the optimal power flow in microgrids, this study provides an inquiry into the use of cutting-edge optimization techniques. To fulfil load demand and consider DER requirements, system running costs must be kept to a minimum. The microgrid integrates a variety of distributed energy resources, including solar photovoltaic systems, wind turbines, and energy storage systems. The research examines several optimization strategies, such as mixed-integer programming, evolutionary algorithms, and linear programming, to decide on the best DER dispatch and scheduling. The findings give important information on how to efficiently use and manage distributed energy resources to raise the performance and resilience of microgrids.
Design of Switched Capacitor Multilevel Inverter with Selective Harmonic Elimination
Authors: Buddinni Chaitra D , Usha P 
Published: December 2023

Abstract
In this paper, Selective Harmonic Elimination (SHE) technique is applied to control multilevel inverters that can be used to eliminate the low order dominant harmonics. This is considered a low frequency technique, in which the switching angles are predetermined based on solving a system of transcendental equations. Newton Raphson is one among the heuristic techniques utilised for selective harmonic elimination. The proposed SHE strategy is simulated along with Sinusoidal Pulse Width Modulation (SPWM) technique and the harmonic distortions are measured and compared for both techniques in MATLAB/Simulink software.
Enhancing industry 5.0 goals through laser based additively Manufactured High-Performance Metals
Authors: Nyamekye P , Westman S , Tepponen V 
Published: December 2023

Abstract
In the current industry 4.0 (I4.0) and emerging Industry 5.0 (I5.0) eras, the integration of digitalization and sustainable goals is driving nature-centric and human-centric products. The production of these products are also creating digital supply chain that boosts agility and resiliency in operations, management, and supply chain interlinks. Additive manufacturing (AM), an element of I4.0, for instance, offers an integrated connection of all process steps and value chains using computer-based designing, data-driven simulations, cloud-based processing, storing, and managing software along seamless digital threads to create agile and resilient supply chain. The integration of AM and other I4.0 technologies enables greater flexibility offering intrinsic sustainable, human-centric and resilience advantages. Laser based additive manufacturing (LBAM), one of the subcategories of AM, offers opportunities to manufacture new intricate and conventionally impractical metal product designs in an ecological and economic competitive stance. High performance metals (HPMs) suited for high-stress and corrosive demanding applications are tough to machine and prone to thermal cracking in case of welding. Conventionally difficult and lengthy to manufacture yet unavoidable HPMs such as nickel alloys and titanium alloys are effortlessly possible via LBAM. Different industrial sectors that utilize these grades of metal alloys continue to adopt AM for the offered design flexibility for achieving goals such as customization, lightweight, on-demand manufacturing, raw material efficiency and cost saving. This study uses literature review and manufacturing case studies to demonstrate the flexibility and digital nature of LBAM towards I5.0 goals. The study objectively highlights the promising responsiveness of AM in the eventuality of supply disruptions that may be caused by sudden changes. The novelty of the study lies in the pragmatic emphasis on the potentials of LBAM and paired I4.0 technologies in revolutionizing the industry towards industry 5.0 goals. The study shows how I4.0 elements can be paired to enable operational efficiencies, lower carbon emissions, and foster sustainability in promoting I5.0 transformation. This study offers a fundamental understanding of the role of LBAM in the advancement of sustainability, human-centricity, and resilience.
Preliminary study of knowledge transfer aspect in the creation of a framework for education and training of digital manufacturing technologies
Authors: Piili H , Huusko A , Kurvinen A 
Published: December 2023

Abstract
Digitalization is changing the industry. As this change accelerates its speed, it also requires a transformation process where knowledge transfer between industry and research institutes play a significant role. There is a need to be more fluent, flexible, and efficient in order to get the latest research results into industrial implementation as quickly as possible. The challenge in knowledge transfer is that its speed in the current stage is too slow compared to the speed of development and changes required by digitalization of traditional manufacturing industries. The motivation for this study is the gap in knowledge transfer. One emerging digital transformation is the establishment of modern digital manufacturing technologies, e.g., additive manufacturing (AM). There are different approaches to supporting the industry in this transformation. Knowledge transfer can happen, for example, through education (e.g., master students) and industrial training, but also the fluent transfer of the latest research results from research institutes to companies is needed. University education needs to support the requirements of the manufacturing industry by providing future experts with skills to smooth the transformation process and bring novel technology applications, such as AM, to industrial-scale use. The article discusses how university education can support future competence-building in the industry. Similar needs are also present in industrial training, which universities often provide. Both education and training need to be improved from fundamental approaches to explain how this new knowledge should be created, i.e., how knowledge transfer happens most efficiently. The outcome of this article is the basis of the framework for education and training of digital manufacturing technologies by using modern learning methods and tools. More detailed pedagogical and knowledge transfer models can be developed and applied when this framework is created.
Virtual Reality-Based Training in Additive Manufacturing: Developing a Service Concept that Adapts to Company-Specific Requirements
Authors: Huusko A , Susi J , Parchegani S , Hosseini H , Tuomisto A , Helin A , Piili H , Salminen A 
Published: December 2023

Abstract
Additive manufacturing (AM), commonly known as 3D printing, has vast amount of technological opportunities, but it also necessitates specialized expertise among its specialists, adopters, users etc. As AM continues to demonstrate its potential and has high growth rates in fields of its use, applications, systems etc., the importance of AM education and training becomes increasingly evident. This paper serves as a preliminary study outlining a novel training concept for industrial AM training including microlearning units and conducted in virtual reality, focusing on the accessibility and flexibility of users. The concept has been developed as part of the ongoing industrial training project. Microlearning and virtual tool-assisted training methods offer effective and agile approaches to enhance the continuous learning of new industrial skills, enabling companies to keep pace with evolving technologies. These tools facilitate extensive customization of training, making company-specific training even more crucial for effective skill development and knowledge transfer. The design of this training concept leverages the user story mapping (USM) method, enabling a deeper understanding of company-specific requirements and needs concerning additive manufacturing training. Moreover, through questionnaires and interviews conducted during the USM process helps identify technology readiness levels (TRLs) of companies in relation to their AM adoption. Measuring TRLs provides valuable insights into the technical maturity of companies regarding additive manufacturing, as readiness levels tend to vary significantly among different organizations. The preliminary framework provided serves as a foundation for designing a modern training concept that cater to the unique requirements of different companies, fostering their successful adoption of AM technologies.
Industrial IoT system for laser-wire direct energy deposition: data collection and visualization of manufacturing process signals
Authors: Martikkala Antti , Wiikinkoski Olli , Asadi Reza , Queguineur Antoine , Ylä-Autio Aapo , Flores Ituarte Iñigo 
Published: December 2023

Abstract
Industry 4.0, also known as the Fourth Industrial Revolution, is a term used to describe the current trend of automation and data exchange in manufacturing and other industries. The Internet of Things (IoT) plays a crucial role in Industry 4.0 by connecting devices, machines, and products to the Internet and enabling real-time data exchange. Moreover, additive manufacturing is a key developing manufacturing technology in Industry 4.0. New technologies such as data analysis with Artificial Intelligence and machine vision are widely used in optimization. However, in a lab environment, these technologies depend on the data collected from the process. For such work, the researchers should be able to focus on their core research rather than on the development of infrastructure to collect and analyse the data. This research presents an open software and hardware IoT solution to monitor a laser wire direct energy deposition system installed in a cartesian type 3-axis machine tool. The IoT solution adopts three open-source tools for core issues, such as 1) interoperability, flexibility, and availability; 2) data storage; and 3) data visualization of sensor data and manufacturing process signals. The system architecture is based on one or more edge devices connected to sensors and forwarding their data toward a local API endpoint. The endpoint is created with Node-RED, an open-source visual flow-based development tool for IoT data. Node-RED forwards the data to an open-source InfluxDB database. Finally, the data is visualized with an open-source Grafana application. The system is prototyped, designed, implemented, and tested in a lab environment to monitor a laser-wire direct energy deposition process. The significance of such a flexible IoT data collection system for research and development projects can be integral. Thus, providing savings in time and money can substantially speed up the development of new technologies where the value arises from the sensor data and its analysis.
In-situ monitoring for PBF-LB/M processes: Does multispectral optical tomography add value in recognizing process deviations?
Authors: Becker Tina , Breese Philipp P , Metz Christian , Altenburg Simon J 
Published: December 2023

Abstract
Laser powder bed fusion of metallic components (PBF-LB/M) is gaining acceptance in industry. However, the high costs and lengthy qualification processes required for printed components create the need for more effective in-situ monitoring and testing methods. This article proposes multispectral Optical Tomography (OT) as a new approach for monitoring the PBF-LB/M process. Compared to other methods, OT is a low-cost process monitoring method that uses long-time exposure imaging to observe the build process. However, it lacks time resolution compared to expensive thermographic sensor systems. Monochromatic OT (1C-OT) is already commercially available and observes the building process layer-wise using a single wavelength window in the NIR range. Multispectral OT (nC-OT) utilizes a similar setup but can measure multiple wavelength ranges per location simultaneously. By comparing the classical 1C-OT and nC-OT approaches, this article examines the advantages of nC-OT (two channel OT and RGB-OT) in reducing the false positive rate for process deviations and approximating maximum temperatures for a better comparison between different build processes and materials. This could ultimately reduce costs and time for part qualification. The main goal of this contribution is to assess the advantages of nC-OT compared to 1C-OT for in-situ process monitoring of PBF-LB/M.
Towards data driven quality monitoring: alignment and correlation of photodiode-based co-axial melt pool monitoring signals to part quality in laser powder bed fusion
Authors: Reijonen Joni , Revuelta Alejandro , Nagarajan Hari P.N. 
Published: December 2023

AbstractOnline quality control of advanced manufacturing processes often utilize process monitoring as a qualification method to reduce or eliminate time consuming and costly destructive and non-destructive tests. The need for process monitoring based qualification becomes more apparent with the advent of laser based additive manufacturing for complex geometries and lot-size-one production. However, the usability of the process monitoring solution heavily relies on the accuracy of the established alignment and assignment of process signals to specific part and defects herein. This research proposes a data fusion strategy for data pre-processing to structure and align multi-source sensor data for improving correlation accuracy in process monitoring. A laser powder bed fusion system equipped with a photodiode-based co-axial melt pool monitoring is used for the study. First, the monitoring system collects photon emission data from the melt pool using photodiodes with high frequency (>100 kHz) which are aligned spatially and temporally using position data (xy-coordinates) of the galvanometer scanner and the laser ON/OFF signals. Next, a clustering approach is used to assign each photodiode signal with the individual parts on the build platform. Finally, correlation between melt pool monitoring signals and part level porosity is studied using micro-computed X-ray tomography (μCT) from a build job containing parts produced under varying process conditions.
Defect detection in laser-based powder bed fusion process using machine learning classification methods
Authors: Akmal Jan , Macarie Mihai , Björkstrand Roy , Minet Kevin , Salmi1 Mika 
Published: December 2023

Abstract
The aim of this study is to deploy machine learning (ML) classification methods to detect defective regions in additive manufacturing, colloquially known as 3D printing, particularly for the laser-based powder bed fusion process. A custom-designed test specimen composed of 316L was manufactured using EOS M 290 machine. Multinomial logistic regression (MLR), artificial neural network (ANN), and convolutional neural network (CNN) classification techniques were applied to train the ML models using optical tomography infrared images of each additively manufactured layer of test specimen. Based on the trained MLR, ANN, and CNN classifiers, the ML models predict whether the manufactured layer is standard or defective, yielding five classes. Defective layers were classified into two classes for lack of fusion and two classes for keyhole porosity. The supervised approach yielded impeccable accuracy (>99%) for all three classification methods, however CNN inherited the highest degree of performance with 100% accuracy for independent test dataset unfamiliar to the model for unbiased evaluation. The high performance and low cost of computing observed in this work can have the potential to detect and eliminate defective regions by tuning the processing parameters in real time resulting in significantly decreased costs, lead-time, and waste. The proposed quality control can enable mass adoption of additive manufacturing technologies in a vast number of industries for critical components that are design- and shape- agnostic.