Hasil untuk "Engineering machinery, tools, and implements"

Mairieli Wessel, Daniel Feitosa, Sangeeth Kochanthara

Rising publication pressure and the routine use of generative AI tools are reshaping how software engineering research is produced, assessed, and taught. While these developments promise efficiency, they also raise concerns about skill degradation, responsibility, and trust in scholarly outputs. This vision paper employs Design Fiction as a methodological lens to examine how such concerns might materialise if current practices persist. Drawing on themes reported in a recent community survey, we construct a speculative artifact situated in a near future research setting. The fiction is used as an analytical device rather than a forecast, enabling reflection on how automated assistance might impede domain knowledge competence, verification, and mentoring practices. By presenting an intentionally unsettling scenario, the paper invites discussion on how the software engineering research community in the future will define proficiency, allocate responsibility, and support learning.

Cláudio Lúcio do Val Lopes, João Marcus Pitta, Fabiano Belém et al.

The integration of Artificial Intelligence (AI) into clinical settings presents a software engineering challenge, demanding a shift from isolated models to robust, governable, and reliable systems. However, brittle, prototype-derived architectures often plague industrial applications and a lack of systemic oversight, creating a ``responsibility vacuum'' where safety and accountability are compromised. This paper presents an industry case study of the ``Maria'' platform, a production-grade AI system in primary healthcare that addresses this gap. Our central hypothesis is that trustworthy clinical AI is achieved through the holistic integration of four foundational engineering pillars. We present a synergistic architecture that combines Clean Architecture for maintainability with an Event-driven architecture for resilience and auditability. We introduce the Agent as the primary unit of modularity, each possessing its own autonomous MLOps lifecycle. Finally, we show how a Human-in-the-Loop governance model is technically integrated not merely as a safety check, but as a critical, event-driven data source for continuous improvement. We present the platform as a reference architecture, offering practical lessons for engineers building maintainable, scalable, and accountable AI-enabled systems in high-stakes domains.

Erhong Zhang, Jiao Yan, Jiahao Du et al.

Lactic acid bacteria (LAB) are pivotal microorganisms in the food industry. Current approaches for functional gene validation and trait improvement in LAB primarily rely on traditional gene editing and homologous recombination techniques. These methods are often cumbersome, inefficient, and time-consuming, hindering the rapid and precise customization of strains. This limitation has, to some extent, constrained the rapid selection and industrial application of functional LAB strains. The engineering of LAB through gene editing technologies has significantly advanced both fundamental and applied research. Among these, CRISPR/Cas gene editing has successfully achieved precise modification of multiple genes in various LAB species. Compared to conventional methods, it offers superior editing efficiency and lower operational costs, opening new avenues for functional gene identification and genetic improvement in LAB. However, the application of exogenous CRISPR/Cas systems in LAB faces technical challenges such as high off-target rates, chromosomal abnormalities, and cytotoxicity. The development of endogenous CRISPR/Cas-based editing tools for LAB provides novel pathways for precise regulation, rational design, and flexible application. This paper first outlines the structural components and mechanistic principles of CRISPR/Cas gene editing tools. It then explores the research progress and applications of both endogenous and exogenous CRISPR/Cas systems in LAB. Finally, it provides an outlook on the future application of CRISPR/Cas gene editing technology in LAB, offering a reference for its implementation in this field. The advent of gene editing technologies has significantly propelled functional gene validation and trait improvement in lactic acid bacteria (LAB), thereby advancing both fundamental research and industrial applications. Notably, the CRISPR/Cas system has emerged as a transformative tool enabling precise genetic modification in diverse LAB species, offering marked improvements in editing efficiency and cost reduction relative to conventional approaches. CRISPR/Cas-based editing strategies in LAB are broadly classified into exogenous and endogenous systems. Exogenous systems operate independently of the host’s native immune repertoire, conferring the advantages of broad strain applicability and high editing efficiency. These systems have been successfully deployed for functional gene characterization, metabolic pathway engineering, such as augmenting antimicrobial production, and probiotic safety enhancement via virulence gene deletion. Conversely, endogenous systems leverage the intrinsic CRISPR/Cas machinery of LAB, offering superior biocompatibility and minimized off-target risks. Notable applications include precise gene knockout and integration using the native Type I-E system in Lacticaseibacillus paracasei. This review provides a concise overview of CRISPR/Cas system architecture and mechanisms, followed by a systematic synthesis of research progress and applications for both exogenous and endogenous systems in LAB. Finally, future directions are outlined to guide the continued development and application of CRISPR/Cas technologies in this field.

Junaidi, Alfian, Fardinal et al.

The rapid development of manufacturing industry technology requires vocational education institutions to continuously adapt to industrial needs, particularly in the fields of Computer Aided Design (CAD), Computer Aided Manufacturing (CAM), and Computer Numerical Control (CNC). However, vocational high school students still face limitations in understanding the integrated workflow of mechanical design and manufacturing based on digital technology. This community service activity aimed to socialize the D3 Mechanical Engineering Study Program of Politeknik Negeri Padang and to introduce ZW3D software and CNC technology along with their applications in mechanical design and manufacturing to students of SMK Negeri 1 Padang. The implementation method consisted of preparation, socialization and introduction sessions, interactive discussions, and evaluation through feedback from participants.The activities included presentations on vocational higher education opportunities,demonstrations of ZW3D as an integrated CAD/CAM tool, basic CNC concepts,and an overview ofhydraulic and pneumatic systems used in industrial machinery. The results showed a positive response from students and teachers, reflected in increased understanding of CAD/CAM-CNC concepts, awareness of modern manufacturing technology, and improved motivation to pursue vocational higher education in mechanical engineering. In conclusion, this activity effectively enhanced students'knowledge and readiness to face the demands of modern manufacturing industries and supported the strengthening of linkages between vocational schools and higher education institutions.

G. G. Parkhomenko, S. Kambulov, N. V. Buzhinsky

The development of a new working tool for deep soil loosening requires a comprehensive approach to determining its design parameters and operating modes, based on interrelated and harmonized dependencies. The design process must account not only for the geometry of the tool itself but also for the heterogeneous physical and mechanical properties of the soil environment. It is also essential to ensure stable operation and achieve the required quality of work and energy efficiency of the technological process. ( Research purpose ) The aim of this study is to develop an anti-erosion deep soil loosener capable of bringing soil clods to the surface. ( Materials and methods ) The newly designed working tool is designed for primary tillage and deep loosening of soil to depths exceeding 25 centimeters, without inverting the soil layer. During anti-erosion treatment, it simultaneously forms molelike channels. A schematic diagram is presented to illustrate the interaction between the working tool and the soil. ( Results and discussion ) The scientific novelty of the study lies in establishing dependencies that describe the relationship between the tool’s design parameters and its operating modes during its interaction with the soil. These dependencies form the basis for developing an engineering method for calculating the tool, which is implemented in the form of reinforced bars that interact with the soil surface. The study also substantiates the mechanism of soil layer displacement and identifies the potential disruption modes, including the formation of secondary fracture planes that occur during the upward movement of the soil layer. ( Conclusions ) The parameters and operating modes of the working tool of the new deep soil loosener have been determined. To initiate effective soil descent, the clearance between the bars should not exceed 50 millimeters. For optimal soil clod trajectory, the shape of the bars should follow a first-order brachistochrone curve (a cycloid). The bar length of the working tool ranges from 0 to 0.4 meters, depending on soil conditions and operational requirements.

Daniel Rodrigues Silva, Wanessa Soares Luiz Silva, A. Maas et al.

This article aims to identify preventive measures for workers involved in mining activities, particularly those exposed to silica dust and at risk of developing silicosis, an occupational pulmonary disease that is progressive and irreversible. Mining environments contain various substances or products that pose health risks, including cement, rubber, wood, petroleum derivatives, epoxy resins, chromium, and nickel. Continuous exposure to these agents requires the implementation of control strategies, including respiratory protection measures, environmental monitoring, and occupational health actions, aiming to minimize occupational risks and prevent the onset of pneumoconioses. The role of the Specialized Service in Safety Engineering and Occupational Medicine (SESMT) is essential for risk identification and classification, as well as for defining the appropriate types of personal protective equipment (PPE) required for each job and sector. Periodic workplace inspections and health monitoring allow functional adjustments whenever changes are detected that may compromise workers' physical integrity. These measures also include continuous education and training on proper PPE use, emphasizing the importance of each device as a protective tool: “any device or product, for individual use by the worker, intended to protect against risks likely to threaten work safety and health” (BRAZIL, 1978). The effectiveness of preventive measures depends on multidisciplinary work involving safety, engineering, and occupational health professionals. Integration among these sectors not only reduces risks but also enables the implementation of continuous, evidence-based prevention actions. According to studies, applying knowledge from safety engineering and occupational medicine to all components of the work environment, including machinery, equipment, and processes, contributes to the elimination or reduction of health risks for workers (HAAG, 2001). The methodology adopted in this study consisted of a bibliographic review, including the analysis of scientific articles, specialized journals, technical literature, and regulatory standards (NR), aiming to understand occupational silica exposure, prevention mechanisms, and the role of occupational health and safety policies. This approach allowed the identification of effective strategies for silicosis prevention, highlighting the importance of inspection, periodic health monitoring, and proper PPE training. In conclusion, reducing the incidence of silicosis depends on a combination of engineering controls, individual protection, periodic medical follow-up, and worker awareness. Integration among SESMT, health professionals, and industrial managers is essential for implementing consistent and sustainable preventive actions, ensuring not only the protection of workers' health but also the maintenance of productivity and safety in mining environments.

S. Satish, Tariq Umar

PurposeThis study investigates the factors that influence 3D printing technology adoption in the construction industry, with a focus on the economic, environmental, social and regulatory barriers to widespread integration. Despite the widely acknowledged potential of 3D printing to transform construction practices by lowering costs, increasing efficiency and enhancing sustainability, adoption has been slow. The research focuses on the economic, social, environmental and regulatory factors.Design/methodology/approachThe literature review discusses both the benefits – such as lower labour costs, faster construction times and less material waste – and the drawbacks – such as high initial costs, reliance on traditional methods and a lack of standardized regulations. A quantitative research methodology was used to investigate these issues, which included distributing a structured survey to 150 construction industry professionals. The survey’s purpose was to collect detailed quantitative data on 3D printing perceptions, preferences and experiences. The data were analysed with SPSS software using descriptive statistics, correlation analysis and reliability testing, which provided a thorough understanding of the factors influencing 3D printing adoption.FindingsThe study concludes with actionable recommendations to address these challenges, such as advocating for increased government support through subsidies and incentives, investing in training and education to reduce resistance to change and developing standardized regulations to ensure the safe and effective implementation of 3D printing. These strategies, which are consistent with the literature, are required for the construction industry to fully realize the benefits of 3D printing technology, ultimately increasing productivity, lowering costs and contributing to more sustainable construction practices.Research limitations/implicationsThis study has several limitations. The sample size was modest and geographically limited, which may restrict the generalizability of the findings to broader construction contexts. Additionally, some survey constructs demonstrated low internal reliability, suggesting the need for more refined measurement tools in future studies. The cross-sectional design also limits the ability to assess changes in perceptions over time. Despite these limitations, the study provides important insights into the economic, social, environmental and regulatory barriers to 3D printing adoption. The findings offer practical implications for industry stakeholders, policymakers and researchers seeking to advance innovation within the construction sector.Practical implicationsThe findings of this study provide actionable insights for construction industry stakeholders seeking to adopt 3D printing technologies. Addressing economic challenges through financial incentives, training and partnerships can enhance feasibility, particularly for SMEs. Environmental benefits such as material efficiency and reduced waste should be leveraged to support sustainable practices. Policymakers must develop clear regulatory frameworks to streamline approvals and ensure safety. Training programmes and awareness campaigns are essential to overcome social resistance and skill gaps. By addressing these areas, industry professionals and decision-makers can accelerate the responsible integration of 3D printing and drive innovation in construction processes.Social implicationsThe adoption of 3D printing in construction carries significant social implications, particularly in workforce transformation and public acceptance. While the technology may reduce demand for manual labour, it creates opportunities for skilled jobs in digital design, robotics and machinery operation. This shift highlights the urgent need for reskilling and upskilling programmes. Additionally, public perception plays a crucial role; concerns about safety, reliability and job displacement can hinder adoption. Raising awareness through education and demonstration projects can build trust and acceptance. Promoting inclusive access to training and employment in 3D printing can also support social equity and technological inclusion.Originality/valueThis research reveals that 3D printing in the construction industry has numerous advantages and a few evident issues that have to be resolved to let construction use this effective type of technology more often. What is needed is a more consolidated, coordinated and systematic approach through the formation of strategic alliances, the development of complete legal codes and the promotion of lessons with construction ideas and information. Such an approach will additionally help to eliminate current barriers and guarantee that the construction industry will be ready to meet future requirements and challenges effectively.

Spyridon D. Mourtas, Shuai Li, Xinwei Cao et al.

The weights and structure determination (WASD) neuronet (or neural network) is a single-hidden-layer feedforward neuronet that exhibits an excellent approximation ability, despite its simple structure. Thanks to its strong generalization, fast speed, and ease of implementation, the WASD neuronet has been the subject of many modifications, including metaheuristics, and applications in a wide range of scientific fields. As it has garnered significant attention in the last decade, the aim of this study is to provide an extensive overview of the WASD framework. Furthermore, the WASD has been effectively used in numerous real-time learning tasks like regression, multiclass classification, and binary classification due to its exceptional performance. In addition, we present WASD’s applications in social science, business, engineering, economics, and medicine. We aim to report these developments and provide some avenues for further research.

Liang Chen, Fanxing Meng, Yuxuan Gu

According to high-precision damage identification of aircraft complex configuration fatigue key structures, a high-precision mathematical model of complex configuration structure is established with the use of digital twin technology to realize the real-time and accurate characterization of a physical entity from the macro to the micro state. Meanwhile, the damage identification information obtained by different sensor technologies and systems is used to deduce the exact state of the damage or crack. In other words, the advantage of a multi-source data drive is used to improve the effectiveness of the overall monitoring system and eliminate the limitations of single-sensor monitoring technology. Each sensor system directly transmits their filtered data information to the fusion center (digital twin system). There is no influence between each sensor; the digital twin carries out comprehensive processing and fusion analysis of each piece of information in an appropriate manner, according to the built-in algorithm and mechanism, and then outputs the final damage identification and fault diagnosis results.

Sana Ullah, Riccardo Nobile, Gennaro Scarselli et al.

Ceramic Matrix Composites (CMCs) have found numerous applications in aerospace, automotive and space vehicles due to their light weight and ability to withstand extreme temperatures. To develop a design criterion for CMCs, elastic properties at different scales need to be evaluated. In this research, elastic properties of CMCs are evaluated at the micro- and meso-level using representative volume element (RVE) in the Ansys Material Designer module. These properties are then validated using various analytical models including Rule of Mixture (ROM), the Chamis Model and the Mori–Tanaka Model. In-plane elastic properties (E11 and G12) of numerical models are in close agreement with the analytical models at both micro- and mesoscales. However, for out of plane properties (E22, G23), Mori–Tanaka Model provides the highest and the Chamis Model provides the lowest.

Simone Palladino, Renato Zona, Vincenzo Minutolo

A straightforward method for determining the causes of impact relics left by ancient projectiles on the city walls of Pompeii is proposed based on principles of plasticity and fracture mechanics. The inverse analysis begins with the measured craters caused by spherical projectiles or darts launched by the Roman army during the siege of 89 B.C. A Mathematica© notebook is presented, enabling the calculation of projectile impact velocity from the known dimensions of the projectiles and the mechanical properties of the wall material.

Uzair Bhatti, Qamar Jaleel, Umair Aslam et al.

The petrochemical industry faces significant safety challenges, necessitating stringent protocols and advanced monitoring systems. Traditional methods rely on manual inspections and fixed sensors, often reacting to hazards only after they occur. Multimodal AI, integrating visual, sensor, and textual data, offers a transformative solution for real-time, proactive safety management. This paper evaluates AI models—Gemini 1.5 Pro, OPENAI GPT-4, and Copilot—in detecting workplace hazards, ensuring compliance with Process Safety Management (PSM) and DuPont safety frameworks. The study highlights the models’ potential in improving safety outcomes, reducing human error, and supporting continuous, data-driven risk management in petrochemical plants. This paper is the first of its kind to use the latest multimodal tech to identify the safety hazard; a similar model could be deployed in other manufacturing industries, especially the oil and gas (both upstream and downstream) industry, fertilizer industries, and production facilities.

Mandarapu Srikanth, Yellapragada Venkata Pavan Kumar, Sivakavi Naga Venkata Bramareswara Rao

Power electronic converter-based microgrids generally suffer from poor power delivery/handling capability during sudden load changes, especially during islanded operations. This is due to the lack of transient energy support to compensate for sudden load changes. The literature has suggested the use of adaptive droop control to provide compensation during transient conditions, thereby improving the power delivery capability. In this context, fuzzy logic-based adaptive droop control is a state-of-the-art technique that was developed based on empirical knowledge of the system. However, this way of designing the droop coefficient values without considering the mathematical knowledge of the system leads to instability during transient conditions. This problem further aggravates when dominant inductive load changes occur in the system. To address this limitation, this paper proposes an improved fuzzy logic-based adaptive droop control method. In the proposed methodology, the values of droop coefficients that are assigned for different membership functions are designed based on the stability analysis of the microgrid. In this analysis, the feasible range of active power–frequency droop values that could avoid instability during large inductive load changes is identified. Accordingly, the infeasible values are avoided in the design of the fuzzy controller. The performance of the proposed and the conventional fuzzy logic methods is verified through simulation in MATLAB/Simulink. From the results, it is identified that the proposed method has improved the power delivery capability of the microgrid by 14% compared to the conventional method.

M. Hikmatul Ridho, Prabowo

The energy consumption pattern in the world, and in Indonesia today, is still dominated by fossil energy in oil, gas, and coal. It contradicts the reduced production of fossil energy, especially petroleum. Therefore, the government is trying to increase the role of new and renewable energy. One of the renewable energy sources that can be developed is wind energy. Indonesia has the potential for wind energy of 60.6 GW with an average wind velocity of 3–6 m/s. Given these conditions, it is expected that the installation of vertical axis wind turbines (VAWT) in buildings in urban areas and remote islands will be able to take advantage of the wind speed flowing above or beside buildings or skyscrapers, where the wind conditions do not have obstacles such as trees, houses, and so on. As a result, analysis and experimentation are required to design a wind turbine with good performance that can be used in cities or remote islands at relatively low wind speeds. The method used in this study is numerical analysis with computational fluid dynamics (CFD) with poly-hexacore meshing type, and the geometry sample is an H-Darrieus turbine. The input parameter is wind speed, which ranges from 2.5 to 9 m/s. The final goal of this study is to determine whether the CFD simulation modeling used is credible or valid.

Haibo Wang, Zhuolin Xu, Shin Hwei Tan

Refactoring is the process of restructuring existing code without changing its external behavior while improving its internal structure. Refactoring engines are integral components of modern Integrated Development Environments (IDEs) and can automate or semi-automate this process to enhance code readability, reduce complexity, and improve the maintainability of software products. Similar to traditional software systems such as compilers, refactoring engines may also contain bugs that can lead to unexpected behaviors. In this paper, we propose a novel approach called RETESTER, a LLM-based framework for automated refactoring engine testing. Specifically, by using input program structure templates extracted from historical bug reports and input program characteristics that are error-prone, we design chain-of-thought (CoT) prompts to perform refactoring-preserving transformations. The generated variants are then tested on the latest version of refactoring engines using differential testing. We evaluate RETESTER on two most popular modern refactoring engines (i.e., ECLIPSE, and INTELLIJ IDEA). It successfully revealed 18 new bugs in the latest version of those refactoring engines. By the time we submit our paper, seven of them were confirmed by their developers, and three were fixed.

Mauro Marcelino, Marcos Alves, Bianca Trinkenreich et al.

[Context] An evidence briefing is a concise and objective transfer medium that can present the main findings of a study to software engineers in the industry. Although practitioners and researchers have deemed Evidence Briefings useful, their production requires manual labor, which may be a significant challenge to their broad adoption. [Goal] The goal of this registered report is to describe an experimental protocol for evaluating LLM-generated evidence briefings for secondary studies in terms of content fidelity, ease of understanding, and usefulness, as perceived by researchers and practitioners, compared to human-made briefings. [Method] We developed an RAG-based LLM tool to generate evidence briefings. We used the tool to automatically generate two evidence briefings that had been manually generated in previous research efforts. We designed a controlled experiment to evaluate how the LLM-generated briefings compare to the human-made ones regarding perceived content fidelity, ease of understanding, and usefulness. [Results] To be reported after the experimental trials. [Conclusion] Depending on the experiment results.

Christoph Treude, Marco A. Gerosa

Artificial intelligence (AI), including large language models and generative AI, is emerging as a significant force in software development, offering developers powerful tools that span the entire development lifecycle. Although software engineering research has extensively studied AI tools in software development, the specific types of interactions between developers and these AI-powered tools have only recently begun to receive attention. Understanding and improving these interactions has the potential to enhance productivity, trust, and efficiency in AI-driven workflows. In this paper, we propose a taxonomy of interaction types between developers and AI tools, identifying eleven distinct interaction types, such as auto-complete code suggestions, command-driven actions, and conversational assistance. Building on this taxonomy, we outline a research agenda focused on optimizing AI interactions, improving developer control, and addressing trust and usability challenges in AI-assisted development. By establishing a structured foundation for studying developer-AI interactions, this paper aims to stimulate research on creating more effective, adaptive AI tools for software development.

Dehai Zhao, Zhenchang Xing, Qinghua Lu et al.

UI automation is a useful technique for UI testing, bug reproduction, and robotic process automation. Recording user actions with an application assists rapid development of UI automation scripts, but existing recording techniques are intrusive, rely on OS or GUI framework accessibility support, or assume specific app implementations. Reverse engineering user actions from screencasts is non-intrusive, but a key reverse-engineering step is currently missing - recognizing human-understandable structured user actions ([command] [widget] [location]) from action screencasts. To fill the gap, we propose a deep learning-based computer vision model that can recognize 11 commands and 11 widgets, and generate location phrases from action screencasts, through joint learning and multi-task learning. We label a large dataset with 7260 video-action pairs, which record user interactions with Word, Zoom, Firefox, Photoshop, and Windows 10 Settings. Through extensive experiments, we confirm the effectiveness and generality of our model, and demonstrate the usefulness of a screencast-to-action-script tool built upon our model for bug reproduction.

N. J. Amoanab, J. Y. Afrifa, Andrews Baba Agebure et al.

Aims: This study aimed to assess the degree to which in-house and contract maintenance practices are implemented in roofing sheet manufacturing firms located within the Kumasi Metropolis of Ghana, and to identify the factors that influence these firms' decisions to adopt specific maintenance strategies.  Study Design: The study utilized a descriptive design.  Place and Duration of Study: The research was conducted in the Kumasi Metropolis of the Ashanti Region, Ghana, from February to October 2024.  Methodology: A survey was conducted involving all 13 roofing sheet manufacturing companies, which represent the complete set of such firms. Data were analyzed with Microsoft Excel [Version 19], and the results were displayed in tables and charts.  Results: The findings of the study indicated that contract maintenance is utilised by nearly all roofing sheet manufacturing companies in Kumasi. Additionally, it was found that in-house maintenance practices are employed for particular equipment. The study also revealed that contract maintenance is preferred in scenarios that require external expertise or specialised maintenance equipment. Furthermore, it was determined that all roofing sheet manufacturing companies in Kumasi utilise both in-house and contract maintenance for their machinery.  Practical Implications: Maintenance strategies have significant practical implications for companies, especially those involved in manufacturing, infrastructure, energy, logistics, or any sector where equipment reliability is critical. The choice and implementation of maintenance strategies can directly impact operational efficiency, cost control, safety, compliance, and competitiveness. Conclusion: Based on this study's findings, both in-house and contract maintenance are crucial for the upkeep of company equipment. Nevertheless, contract maintenance is predominantly favoured for tasks necessitating external expertise or specialised maintenance tools. This research's outcomes can serve as a useful resource for stakeholders and companies in developing maintenance policies for their equipment. This study is limited in scope to roofing sheet manufacturing companies within the Kumasi Metropolitan Area in Ghana. It is suggested that future research should be extended nationwide to obtain a comprehensive overview of the maintenance strategies of all roofing sheet manufacturing companies in Ghana.

Minakshi Roy, Biraj Upadhyaya, Jyoti Rai et al.

One of the most widely used imaging techniques in medicine is magnetic resonance imaging (MRI). It is a tool that doctors use to comprehend human anatomy and carry out more accurate analyses. In the study of brain anatomy, image processing super resolution technology has become important to overcome physical restrictions due to image deterioration caused by hardware constraints, lengthier scanning periods, and artefacts. Super resolution is an approach to raise an image’s resolution while improving the image’s quality from a low-resolution (LR) image to a higher-resolution (HR) image. The study provides an overview of deep learning techniques for creating super-resolution (SR) MRI brain images. A widely used deep learning (DL) technique, accessible brain MRI dataset, and quantity evaluation matrices have been presented, mostly used for image super resolution. Factors affecting hardware constraints and artifacts, including magnetic field homogeneity, gradient nonlinearity, radiofrequency (RF) coil sensitivity, signal-to-noise ratio (SNR), and gradient coil performance, have been taken into account. This research focuses mostly on brain MRI images as a contribution to the medical industry for super resolution.