{"results":[{"id":"ss_b33c4055dccdda289fc9d73dc9444a3a51ca13a2","title":"Evolution in the Design of Working Tools for Tillage Machines","authors":[{"name":"D. Popov"},{"name":"D. Mironov"},{"name":"Y. Tsench"}],"abstract":"This paper presents a systematic, multidimensional analysis of the historical and engineering evolution in the design of working tools for tillage machines, covering the period from the origins of agriculture to contemporary high-tech solutions. (Research purpose) The study identifies developmental patterns in design approaches, tracing the transition from artisanal production of basic agricultural implements, based on empirical knowledge, to the emergence of scientifically grounded methods of calculation and design, built on the advances in mechanics, materials science, and agrophysics. (Materials and methods) The research demonstrates that the introduction of new materials with improved performance characteristics, combined with the shift toward engineering calculations and virtual modeling (CAD/CAE/CAM), laid the foundation for the transition from universal to adaptive and smart design solutions. (Results and discussion) The study highlights the role of digitalization in improving the reliability, energy efficiency, and environmental sustainability of modern tillage machine components. It identifies key differences between the Soviet engineering school and international design methodologies. The paper underscores the contribution of scientific schools and research institutes to the development of soil-cutting theory and draft resistance calculations. Progress in the hardening of working tools is illustrated through the use of thermal and vibrational treatments, surfacing techniques, and coatings based on hard alloys. These technologies are presented not as isolated processes, but as integral components of the broader evolution in engineering design. Contemporary design trends are examined, including the application of digital twin technology, parametric geometry, precision agriculture technologies, and artificial intelligence. The study also addresses issues related to environmental sustainability, climate-adaptive engineering solutions, and sustainable agricultural practices. (Conclusions) The study concludes that an interdisciplinary approach is essential for the effective design of tillage implements, integrating agronomy, mechanical engineering, materials science, and digital technologies.","source":"Semantic Scholar","year":2025,"language":"en","subjects":null,"doi":"10.22314/2073-7599-2025-19-3-66-73","url":"https://www.semanticscholar.org/paper/b33c4055dccdda289fc9d73dc9444a3a51ca13a2","is_open_access":true,"citations":2,"published_at":"","score":69.06},{"id":"doaj_10.3390/engproc2025090024","title":"Assessing Uninstalled Hydrogen-Fuelled Retrofitted Turbofan Engine Performance","authors":[{"name":"Jarief Farabi"},{"name":"Christos Mourouzidis"},{"name":"Pericles Pilidis"}],"abstract":"Hydrogen as fuel in civil aviation gas turbines is promising due to its no-carbon content and higher net specific energy. For an entry-level market and cost-saving strategy, it is advisable to consider reusing existing engine components whenever possible and retrofitting existing engines with hydrogen. Feasible strategies of retrofitting state-of-the-art Jet A-1 fuelled turbofan engines with hydrogen while applying minimum changes to hardware are considered in the present study. The findings demonstrate that hydrogen retrofitted engines can deliver advantages in terms of core temperature levels and efficiency. However, the engine operability assessment showed that retrofitting with minimum changes leads to a ~5% increase in the HP spool rotational speed for the same thrust at take-off, which poses an issue in terms of certification for the HP spool rotational speed overspeed margin.","source":"DOAJ","year":2025,"language":"","subjects":["Engineering machinery, tools, and implements"],"doi":"10.3390/engproc2025090024","url":"https://www.mdpi.com/2673-4591/90/1/24","is_open_access":true,"published_at":"","score":69},{"id":"doaj_10.58482/ijeresm.v4i4.4","title":"Machine Learning–Based Prediction of Organic Solar Cell Performance Using Molecular Descriptors","authors":[{"name":"Mohammed Saleh Alshaikh"}],"abstract":"The performance of Organic Solar Cells (OSCs) is intrinsically linked to the molecular, electronic, and structural properties of donor and acceptor materials. This study employs various machine learning techniques, namely the Generalized Regression Neural Network (GRNN), Support Vector Machine (SVM), and Tree Boost, to predict key performance metrics of OSCs, including power conversion efficiency (PCE), short-circuit current density (JSC), open-circuit voltage (VOC), and fill factor (FF). The models are trained and evaluated using an experimentally reported dataset compiled by Sahu et al. Correlation analysis demonstrates that material characteristics such as polarizability, bandgap, dipole moment, and charge transfer are statistically associated with OSC performance. The predictive performance of the GRNN model is compared with that of the SVM and Tree Boost models, showing consistently lower prediction errors within the considered dataset. In addition, sensitivity analysis is performed to assess the relative importance of the predictor variables and to examine the influence of kernel functions on GRNN performance. The results indicate that machine learning models, particularly GRNN, can serve as effective data-driven tools for predicting the performance of organic solar cells and for supporting computational screening studies.","source":"DOAJ","year":2025,"language":"","subjects":["Transportation engineering","Systems engineering","Environmental engineering","Engineering economy","Plasma engineering. Applied plasma dynamics","Engineering machinery, tools, and implements","Mechanics of engineering. Applied mechanics","Materials of engineering and construction. Mechanics of materials","Structural engineering (General)","Engineering geology. Rock mechanics. Soil mechanics. Underground construction","Architectural engineering. Structural engineering of buildings","Computer engineering. Computer hardware","Nuclear engineering. Atomic power","Chemical engineering","Low temperature engineering. Cryogenic engineering. Refrigeration","Naval architecture. Shipbuilding. Marine engineering","Business"],"doi":"10.58482/ijeresm.v4i4.4","url":"https://ijeresm.com/44431-2/","is_open_access":true,"published_at":"","score":69},{"id":"ss_69c954783e193a704d2de8707abe3d02e4d0d982","title":"AgriAccess: Precision Farming Equipment Rentals for Enhanced Crop Management","authors":[{"name":"Mr P Rajapandian"}],"abstract":"ABSTRACT: Agriculture, as a labour-intensive field, relies significantly on efficient machinery to accelerate farming processes. Essential equipment like tractors, harvesters, tillage tools, and various implements play a vital role in modern agriculture. However, the high initial costs and expensive maintenance associated with these machines present substantial financial challenges for many farmers. To address this project, AgriAccess introduces an innovative agricultural machinery rental system designed to ease the financial burden on farmers. AgriAccess offers a user-friendly web dashboard and mobile app, equipping farmers with up-to-date information on farming techniques and available machinery. Through this platform, farmers can seamlessly rent essential equipment, allowing them to conduct farming activities from the comfort of their homes and reducing the costs of equipment ownership. This pioneering system not only enables timely and cost-effective crop harvesting but also allows individual farmers to rent out their machinery, creating an additional income stream. AgriAccess further serves as a marketplace for buying and selling used agricultural machinery, fostering a cooperative community among farmers. By focusing on the optimal utilization of available equipment, AgriAccess becomes a driving force for transforming traditional farming practices and promoting sustainable agriculture. Through efficient machinery usage and a collaborative platform, AgriAccess aims to enhance the sustainability and profitability of farming practices. Keywords: Smart farming, Easy equipment rentals, Helping farmers save money, Sharing farm tools, Farming made simple, Mobile and web access, Grow more with less, Modern agriculture, Community-powered farming, Tools when you need them","source":"Semantic Scholar","year":2025,"language":"en","subjects":null,"doi":"10.55041/isjem03823","url":"https://www.semanticscholar.org/paper/69c954783e193a704d2de8707abe3d02e4d0d982","is_open_access":true,"published_at":"","score":69},{"id":"ss_0fd0cb2754d5c37b8fabde6dfc735967a9d62fb1","title":"Rural Economy - Critical for Mechanization","authors":[{"name":"Prof. B.N. Tripathi"},{"name":"Prof. Pawan K. Sharma"}],"abstract":"Agricultural mechanization is broadly defined as the process of utilizing engineering and technological innovations such as farm tools, machinery, equipment, and power sources to perform agricultural operations more efficiently and effectively. According to the Food and Agriculture Organization (FAO), mechanization encompasses not only the use of tractors and harvesters but also implements for land preparation, irrigation, sowing, weeding, harvesting, processing, and storage. It is an essential component of agricultural modernization, enabling farmers to enhance productivity, reduce drudgery, save time, and ensure timeliness in crop operations.","source":"Semantic Scholar","year":2025,"language":"en","subjects":null,"doi":"10.52151/aet2025493.1851","url":"https://www.semanticscholar.org/paper/0fd0cb2754d5c37b8fabde6dfc735967a9d62fb1","is_open_access":true,"published_at":"","score":69},{"id":"ss_43741247440426d39ddca7abd3181bb518504b51","title":"A comparative analysis of machine and deep learning models for predictive maintenance and fault estimation of tools in industrial machinery within small-scale settings","authors":[{"name":"Yusuf Öztürk"}],"abstract":"This study presents a comparative evaluation of machine learning (ML) and deep learning (DL) models for predictive maintenance (PdM) in small-scale industrial systems. A low-cost Arduino-based testbed equipped with vibration, temperature, and rotational speed sensors was developed to emulate real-world conditions. The primary focus of the study is the detailed implementation and analysis of a Recurrent Neural Network with Long Short-Term Memory (RNN-LSTM). For benchmarking, two baseline models—Linear Regression and K-Nearest Neighbors (KNN)—were also implemented. According to the evaluation results, RNN-LSTM achieved the highest performance, with 95.31% accuracy, 0.047 MSE, 0.217 RMSE, 0.047 MAE, and 23.4% SMAPE. In comparison, Linear Regression and KNN yielded lower accuracies (92.30% and 93.27%) and higher error values (e.g., SMAPE of 58.7% and 41.2%). These findings confirm the superiority of RNN-LSTM in modeling temporal dependencies, while baseline models demonstrated limited generalization. Overall, the study shows that advanced DL models can be deployed on resource-constrained embedded systems, supporting the wider adoption of Industry 4.0 practices in small and medium-sized enterprises.","source":"Semantic Scholar","year":2025,"language":"en","subjects":null,"doi":"10.33769/aupse.1729175","url":"https://www.semanticscholar.org/paper/43741247440426d39ddca7abd3181bb518504b51","is_open_access":true,"published_at":"","score":69},{"id":"ss_83d65cfc616f30a53cbaef13cb7f7c2945c4a5d9","title":"Empowering Supply Chain Management with AIBased Tools in the Inspection Machinery Industry","authors":[{"name":"J. Muñoz"},{"name":"Mateo Del Gallo"},{"name":"Gerardo Minella"},{"name":"Samuel Olaiya Afolaranmi"},{"name":"M. Elahi"},{"name":"Yasir Rathore"},{"name":"Marcos Rico Vañó"},{"name":"Pedro Alfaro Fernández"},{"name":"Beatriz Andrés Navarro"},{"name":"F. Ciarapica"},{"name":"J. L. M. Lastra"}],"abstract":"The manufacturing industry is increasingly adopting Artificial Intelligence (AI)-based solutions to improve production planning and operational efficiency. This article reflects the work carried out in the context of the AIDEAS project. AIDEAS aims to develop AI solutions for the lifecycle of industrial equipment, within the manufacturing phase focusing on three of the key processes within the Supply Chain Management of procurement, fabrication and delivery. The AIProcurement Optimizer module supports purchasing decisions by considering supply constraints and cost targets, while AIFabrication Optimizer module improve production planning and scheduling through a combined approach of mathematical optimization and reinforcement learning. Finally, AI-Delivery Optimizer optimizes delivery logistics to reduce delays and transport costs. A holistic framework, AIDEAS Manufacturing Framework, is proposed that integrates all solutions, showing the connections between them and their workflow. The proposed framework undergoes testing in a real company from the inspection machinery industry through a structured implementation plan, highlighting both the benefits and challenges of adopting AI in small and medium enterprises. The findings underscore the role of AI in driving greater agility, sustainability, and resilience across manufacturing operations.","source":"Semantic Scholar","year":2025,"language":"en","subjects":null,"doi":"10.1109/ICE/ITMC65658.2025.11106528","url":"https://www.semanticscholar.org/paper/83d65cfc616f30a53cbaef13cb7f7c2945c4a5d9","is_open_access":true,"published_at":"","score":69},{"id":"ss_ffa4ad60fb56578be20cd85c9085e780ee3ed90f","title":"Proposed Model to Improve Efficiency in a Textile SME Through the Application of TPM Tools with an IoT-Based Approach","authors":[{"name":"Axel Leonardo Huallpa-Palomino"},{"name":"Joseph Geanpierre Sanchez-Chiza"},{"name":"José Luis Álvarez-Arteaga"},{"name":"Luis Enrique Peña-Mendoza"}],"abstract":"The textile industry plays a critical role in the global economy. In this context, small and medium-sized manufacturing enterprises (SMEs) have shown notable growth, yet they continue to face significant operational challenges-particularly delays in order fulfillment. These delays are primarily attributed to recurrent machinery breakdowns and high rates of defective products. To address these issues, this study proposes an integrated model that combines tools from Planned Total Productive Maintenance (TPM) with an Internet of Things (IoT)-based approach, along with Autonomous TPM to reduce failure frequency, and Standard Work to minimize variability and product defects. The implementation of this model aims to enhance machine availability and standardize production processes. The effectiveness of the model was measured using the Production Efficiency indicator, which improved significantly from 66.6% to 78.1 %. Beyond solving the specific challenges of the case study company, this work aims to serve as a replicable framework for addressing operational inefficiencies and improving delivery performance across the textile sector.","source":"Semantic Scholar","year":2025,"language":"en","subjects":null,"doi":"10.1109/INTERCON67304.2025.11244681","url":"https://www.semanticscholar.org/paper/ffa4ad60fb56578be20cd85c9085e780ee3ed90f","is_open_access":true,"published_at":"","score":69},{"id":"ss_13f1f7a8069b6605a83fd3089628b5fb9ab6f1c3","title":"Agricultural Implement Industry Using WPM Method","authors":[{"name":"Sangeetha Rajkumar"},{"name":"M. Ramachandran"},{"name":"Vimala Saravanan"},{"name":"Prabakaran Nanjundan"}],"abstract":"Agricultural implements are agricultural Human labor in activities that Reduce field crop yield Tools that can be used to improve is reapers, Traction, disc harrows, Cultivators, seed drills, Harrows, Spades, Baggage, Plows, and other agricultural Tools are very common. In Modern Agricultural Practices Agricultural implements play an important role play These are commercial and Widespread in organic farming are used. This Tools are for sowing, field preparation, Planting, threshing, and irrigation and are used for harvesting. Agricultural machinery industry or Agricultural engineering is a profession as part of the industry, it is in agriculture or other agriculture used tractors, Agricultural machinery and Manufacturing agricultural implements maintain. This branch is mechanically Considered part of the profession.Agricultural implements are agricultural to carry out procedures The necessary tools are: In today's farming operations Many agricultural implements are used. Agriculture means crops and livestock Production, Aquaculture, Aquaculture and food and food Forests for non-food products Includes. Seated man Agriculture at the Rise of Civilization A major development was Raised by this Cultivation of species is food Generated surpluses, which helped people live in cities. Humans are at least 105,000 grains years ago Although started to collect, New farmers are about 11,500 years ago They started planting. Sheep Goats, pigs, and cattle About 10,000 years ago were raised. World's lowest Plants are native to 11 regions and Cultivated as fodder. twentieth in century, large-scale Based on monocultures Industrial agriculture with dominated agricultural production.The weighted product method is a multi-criteria decision-making process is there are many alternatives, and based on several criteria we must determine the best alternative.DuPont India, Rallis India Limited, Nuziveedu Seeds Limited, Lemken India Agro Equipments Private Limited, Advanta Limited.Technical capability, Product quality capability, Delivery capability, Financial/cost capability from the result, it is seen that Lemken India Agro Equipments Private Limited is got the first rank where as Nuziveedu Seeds Limited is having the lowest rank","source":"Semantic Scholar","year":2024,"language":"en","subjects":null,"doi":"10.46632/jemm/9/3/5","url":"https://www.semanticscholar.org/paper/13f1f7a8069b6605a83fd3089628b5fb9ab6f1c3","pdf_url":"https://doi.org/10.46632/jemm/9/3/5","is_open_access":true,"citations":3,"published_at":"","score":68.09},{"id":"ss_a91537d640a8c44f47c6539c21a6f7dca06b8ae7","title":"Exploring the Role of Smart Systems in Farm Machinery for Soil Fertility and Crop Productivity","authors":[{"name":"Ahad Ahmed Laskar"}],"abstract":"Agriculture is experiencing a period of technological change, driven by the addition of intelligent technologies into agricultural technology. The integration of smart systems into farm machinery has greatly improved soil fertility management and crop productivity. Advanced technologies such as sensors, IoT, AI, and precision agriculture tools enable real-time monitoring of critical soil parameters, leading to targeted interventions for improving soil health. Automated machinery with GPS and AI-driven algorithms ensures efficient seed placement, precise fertilizer application, and weed management, thereby minimizing resource wastage and environmental impact. Such insights based on data allow farmers to take appropriate decisions based on changing conditions and improve farming practices sustainably, but their large-scale adaptation can be impeded due to high implementation costs, issues with privacy over the data, and expertise over technicalities. But even these challenges are seen in light of increasing yield, input costs reduced, and sustainability-promoting benefits, thereby raising productivity and meeting the causes for environmental conservation and food security.","source":"Semantic Scholar","year":2024,"language":"en","subjects":null,"doi":"10.22214/ijraset.2024.66157","url":"https://www.semanticscholar.org/paper/a91537d640a8c44f47c6539c21a6f7dca06b8ae7","is_open_access":true,"citations":2,"published_at":"","score":68.06},{"id":"ss_98f501e28dd4707297509670c7b562a9f730e9f3","title":"Proposal to increase efficiency in the pizza production line in Peruvian MYPE using Lean Manufacturing tools and IoT","authors":[{"name":"Katherine Melissa De la Torre"},{"name":"Cesar Gabriel Vilela"},{"name":"José Velásquez"}],"abstract":"Despite representing a significant percentage of economic growth, the food sector in Peru faces numerous challenges. Companies in this sector confront issues such as low efficiency in their production lines, high delay times, poorly maintained work areas, and high machinery downtime. This article explores solutions using Lean Manufacturing tools such as work study, 5S, Poka Yoke, and TPM with an IoT approach. Additionally, a pilot program will be implemented using the 8-step change management model to assess and quantify improvements in order to established performance parameters. By analyzing a company struggling with efficiency, it evaluates how Lean Manufacturing tools can enhance workflow and competitiveness. The aim of this research is to implement Lean Manufacturing tools to enhance the efficiency of a food sector company. To achieve this, key objectives must be considered, including optimizing workflow through process standardization and reducing machinery downtime through TPM - Planned Maintenance to improve efficiency by 3.58%. It is expected that the final outcome exceeds this percentage and that it improves over time, as the tools employed have the potential to further boost the company's efficiency.","source":"Semantic Scholar","year":2024,"language":"en","subjects":null,"doi":"10.1145/3716097.3716123","url":"https://www.semanticscholar.org/paper/98f501e28dd4707297509670c7b562a9f730e9f3","is_open_access":true,"published_at":"","score":68},{"id":"doaj_10.3390/inventions8040088","title":"A Revision of Empirical Models of Stirling Engine Performance Using Simple Artificial Neural Networks","authors":[{"name":"Enrique González-Plaza"},{"name":"David García"},{"name":"Jesús-Ignacio Prieto"}],"abstract":"Stirling engines are currently of interest due to their adaptability to a wide range of energy sources. Since simple tools are needed to guide the sizing of prototypes in preliminary studies, this paper proposes two groups of simple models to estimate the maximum power in Stirling engines with a kinematic drive mechanism. The models are based on regression or ANN techniques, using data from 34 engines over a wide range of operating conditions. To facilitate the generalisation and interpretation of results, all models are expressed by dimensionless variables. The first group models use three input variables and 23 data points for correlation construction or training purposes, while another 66 data points are used for testing. Models in the second group use eight inputs and 18 data points for correlation construction or training, while another 36 data points are used for testing. The three-input models provide estimations of the maximum brake power with an acceptable accuracy for feasibility studies. Using eight-input models, the predictions of the maximum indicated power are very accurate, while those of the maximum brake power are less accurate, but acceptable for the preliminary design stage. In general, the best results are achieved with ANN models, although they only employ one hidden layer.","source":"DOAJ","year":2023,"language":"","subjects":["Engineering machinery, tools, and implements","Technological innovations. Automation"],"doi":"10.3390/inventions8040088","url":"https://www.mdpi.com/2411-5134/8/4/88","is_open_access":true,"published_at":"","score":67},{"id":"doaj_10.1299/jamdsm.2023jamdsm0012","title":"Optimization of fast tool servo diamond turning for enhancing geometrical accuracy and surface quality of freeform optics","authors":[{"name":"Lin ZHANG"},{"name":"Yusuke SATO"},{"name":"Jiwang YAN"}],"abstract":"Fast tool servo (FTS) in ultra-precision diamond turning is an efficient technique for high-precision fabrication of freeform optics. However, the currently adopted constant scheme for control point sampling takes no account of the shape variation of the desired surface, which might lose some micro features and result in low form accuracy and non-uniform surface quality. Facing this issue, this manuscript proposes a novel adaptive control points sampling strategy, which improves the form accuracy and keeps as many as the micro surface features. In the optimization method, the sampling stepovers between two adjacent control points are actively adjusted to adapt to the surface profile variation. By adopting this method, the control point sampling induced interpolation error is constrained within the desired tolerance and eliminates the lack/over-definition of control points in the machining area. The feasibility of the proposed optimization method is demonstrated by both theoretical simulations and fabrication experiments of sinusoid freeform surfaces. Compared with the constant sampling method, both the theoretical predicted and experimental measured form error of the proposed method is remarkably reduced by about 35 % with the same amount of control points. This technique provides a new route to allocating control points in FTS diamond turning to achieve high form accuracy and machining efficiency in the fabrication of freeform optics.","source":"DOAJ","year":2023,"language":"","subjects":["Engineering machinery, tools, and implements","Mechanical engineering and machinery"],"doi":"10.1299/jamdsm.2023jamdsm0012","url":"https://www.jstage.jst.go.jp/article/jamdsm/17/1/17_2023jamdsm0012/_pdf/-char/en","pdf_url":"https://www.jstage.jst.go.jp/article/jamdsm/17/1/17_2023jamdsm0012/_pdf/-char/en","is_open_access":true,"published_at":"","score":67},{"id":"doaj_10.3390/engproc2023029014","title":"High Gain Observer Based Active Disturbance Estimation ADE for Second Order Nonlinear Uncertain Systems (ex:Induction Motor)","authors":[{"name":"Saad Kelam"},{"name":"Mohamed Chennafa"},{"name":"Mohamed Belkheiri"},{"name":"Mohamed Amine Zaafrane"}],"abstract":"The main objective of this paper is to solve state observation and external disturbances estimation for a class of second-order nonlinear systems. The proposed method relies mainly on the high gain observer as an estimator that tries to estimate the state vector and at the same time identifies the system’s unknown combined structured and unstructured uncertainties. The efficiency of the proposed method is demonstrated by estimating the flux and the speed of the induction motor by simulation.","source":"DOAJ","year":2023,"language":"","subjects":["Engineering machinery, tools, and implements"],"doi":"10.3390/engproc2023029014","url":"https://www.mdpi.com/2673-4591/29/1/14","is_open_access":true,"published_at":"","score":67},{"id":"doaj_10.3390/inventions8010032","title":"A Generalized Framework for Adopting Regression-Based Predictive Modeling in Manufacturing Environments","authors":[{"name":"Mobayode O. Akinsolu"},{"name":"Khalil Zribi"}],"abstract":"In this paper, the growing significance of data analysis in manufacturing environments is exemplified through a review of relevant literature and a generic framework to aid the ease of adoption of regression-based supervised learning in manufacturing environments. To validate the practicality of the framework, several regression learning techniques are applied to an open-source multi-stage continuous-flow manufacturing process data set to typify inference-driven decision-making that informs the selection of regression learning methods for adoption in real-world manufacturing environments. The investigated regression learning techniques are evaluated in terms of their training time, prediction speed, predictive accuracy (R-squared value), and mean squared error. In terms of training time (\u003cinline-formula\u003e\u003cmath xmlns=\"http://www.w3.org/1998/Math/MathML\" display=\"inline\"\u003e\u003csemantics\u003e\u003cmrow\u003e\u003cmi\u003eT\u003c/mi\u003e\u003cmi\u003eT\u003c/mi\u003e\u003c/mrow\u003e\u003c/semantics\u003e\u003c/math\u003e\u003c/inline-formula\u003e), \u003ci\u003ek\u003c/i\u003e-NN20 (\u003ci\u003ek\u003c/i\u003e-Nearest Neighbour with 20 neighbors) ranks first with average and median values of 4.8 ms and 4.9 ms, and 4.2 ms and 4.3 ms, respectively, for the first stage and second stage of the predictive modeling of the multi-stage continuous-flow manufacturing process, respectively, over 50 independent runs. In terms of prediction speed (\u003cinline-formula\u003e\u003cmath xmlns=\"http://www.w3.org/1998/Math/MathML\" display=\"inline\"\u003e\u003csemantics\u003e\u003cmrow\u003e\u003cmi\u003eP\u003c/mi\u003e\u003cmi\u003eS\u003c/mi\u003e\u003c/mrow\u003e\u003c/semantics\u003e\u003c/math\u003e\u003c/inline-formula\u003e), DTR (decision tree regressor) ranks first with average and median values of \u003cinline-formula\u003e\u003cmath xmlns=\"http://www.w3.org/1998/Math/MathML\" display=\"inline\"\u003e\u003csemantics\u003e\u003cmrow\u003e\u003cmn\u003e5.6784\u003c/mn\u003e\u003cmo\u003e×\u003c/mo\u003e\u003cmsup\u003e\u003cmn\u003e10\u003c/mn\u003e\u003cmn\u003e6\u003c/mn\u003e\u003c/msup\u003e\u003c/mrow\u003e\u003c/semantics\u003e\u003c/math\u003e\u003c/inline-formula\u003e observations per second (ob/s) and \u003cinline-formula\u003e\u003cmath xmlns=\"http://www.w3.org/1998/Math/MathML\" display=\"inline\"\u003e\u003csemantics\u003e\u003cmrow\u003e\u003cmn\u003e4.8691\u003c/mn\u003e\u003cmo\u003e×\u003c/mo\u003e\u003cmsup\u003e\u003cmn\u003e10\u003c/mn\u003e\u003cmn\u003e6\u003c/mn\u003e\u003c/msup\u003e\u003c/mrow\u003e\u003c/semantics\u003e\u003c/math\u003e\u003c/inline-formula\u003e observations per second (ob/s), and \u003cinline-formula\u003e\u003cmath xmlns=\"http://www.w3.org/1998/Math/MathML\" display=\"inline\"\u003e\u003csemantics\u003e\u003cmrow\u003e\u003cmn\u003e4.9929\u003c/mn\u003e\u003cmo\u003e×\u003c/mo\u003e\u003cmsup\u003e\u003cmn\u003e10\u003c/mn\u003e\u003cmn\u003e6\u003c/mn\u003e\u003c/msup\u003e\u003c/mrow\u003e\u003c/semantics\u003e\u003c/math\u003e\u003c/inline-formula\u003e observations per second (ob/s) and \u003cinline-formula\u003e\u003cmath xmlns=\"http://www.w3.org/1998/Math/MathML\" display=\"inline\"\u003e\u003csemantics\u003e\u003cmrow\u003e\u003cmn\u003e5.8806\u003c/mn\u003e\u003cmo\u003e×\u003c/mo\u003e\u003cmsup\u003e\u003cmn\u003e10\u003c/mn\u003e\u003cmn\u003e6\u003c/mn\u003e\u003c/msup\u003e\u003c/mrow\u003e\u003c/semantics\u003e\u003c/math\u003e\u003c/inline-formula\u003e observations per second (ob/s), respectively, for the first stage and second stage of the predictive modeling of the multi-stage continuous-flow manufacturing process, respectively, over 50 independent runs. In terms of R-squared value (\u003cinline-formula\u003e\u003cmath xmlns=\"http://www.w3.org/1998/Math/MathML\" display=\"inline\"\u003e\u003csemantics\u003e\u003cmsup\u003e\u003cmi\u003eR\u003c/mi\u003e\u003cmn\u003e2\u003c/mn\u003e\u003c/msup\u003e\u003c/semantics\u003e\u003c/math\u003e\u003c/inline-formula\u003e), BR (bagging regressor) ranks first with average and median values of 0.728 and 0.728, respectively, over 50 independent runs, for the first stage of the predictive modeling of the multi-stage continuous-flow manufacturing process, and RFR (random forest regressor) ranks first with average and median values of 0.746 and 0.746, respectively, over 50 independent runs, for the second stage of the predictive modeling of the multi-stage continuous-flow manufacturing process. In terms of mean squared error (\u003cinline-formula\u003e\u003cmath xmlns=\"http://www.w3.org/1998/Math/MathML\" display=\"inline\"\u003e\u003csemantics\u003e\u003cmrow\u003e\u003cmi\u003eM\u003c/mi\u003e\u003cmi\u003eS\u003c/mi\u003e\u003cmi\u003eE\u003c/mi\u003e\u003c/mrow\u003e\u003c/semantics\u003e\u003c/math\u003e\u003c/inline-formula\u003e), BR (bagging regressor) ranks first with average and median values of 2.7 and 2.7, respectively, over 50 independent runs, for the first stage of the predictive modeling of the multi-stage continuous-flow manufacturing process, and RFR (random forest regressor) ranks first with average and median values of 3.5 and 3.5, respectively, over 50 independent runs, for the second stage of the predictive modeling of the multi-stage continuous-flow manufacturing process. All methods are further ranked inferentially using the statistics of their performance metrics to identify the best method(s) for the first and second stages of the predictive modeling of the multi-stage continuous-flow manufacturing process. A Wilcoxon rank sum test is then used to statistically verify the inference-based rankings. \u003cinline-formula\u003e\u003cmath xmlns=\"http://www.w3.org/1998/Math/MathML\" display=\"inline\"\u003e\u003csemantics\u003e\u003cmrow\u003e\u003cmi\u003eD\u003c/mi\u003e\u003cmi\u003eT\u003c/mi\u003e\u003cmi\u003eR\u003c/mi\u003e\u003c/mrow\u003e\u003c/semantics\u003e\u003c/math\u003e\u003c/inline-formula\u003e and \u003ci\u003ek\u003c/i\u003e-NN20 have been identified as the most suitable regression learning techniques given the multi-stage continuous-flow manufacturing process data used for experimentation.","source":"DOAJ","year":2023,"language":"","subjects":["Engineering machinery, tools, and implements","Technological innovations. Automation"],"doi":"10.3390/inventions8010032","url":"https://www.mdpi.com/2411-5134/8/1/32","is_open_access":true,"published_at":"","score":67},{"id":"doaj_10.3390/engproc2023055041","title":"Necessity of Notification System Application According to Elementary School Teacher’s Environmental Behavior","authors":[{"name":"Haruno Ishikawa"}],"abstract":"In rural regions of Japan, specifically in Shizuoka, the majority of elementary school classrooms lack ventilation systems, and the operation is manually conducted by teachers and students. Instead of relying on the implementation of high-performance hardware solutions, the aim is to strive for a harmonious coexistence of COVID-19 mitigation and Zero Energy Building (ZEB) realization through appropriate information dissemination and proactive environmental behavior. This paper investigates the environmental behavior of educators and its indicators, and assuming homeroom teachers in X City implement classroom ventilation based on threshold value notifications, it is demonstrated that a reduction of up to 20% in the current air conditioning heat load can be achieved.","source":"DOAJ","year":2023,"language":"","subjects":["Engineering machinery, tools, and implements"],"doi":"10.3390/engproc2023055041","url":"https://www.mdpi.com/2673-4591/55/1/41","is_open_access":true,"published_at":"","score":67},{"id":"doaj_10.1299/transjsme.21-00381","title":"Vortex structure behind highly heated tandem twin cylinders","authors":[{"name":"Yuji YAHAGI"}],"abstract":"Vortex structures behind two highly heated cylinders of equal diameter in tandem arrangements have been investigated experimentally. The experiments were performed under the following conditions: cylinders diameter, D = 4 mm; mean flow velocity of air, U∞ = 1.0 m/s; Reynolds number, Re = 250; cylinders spacing ratio, S/D = 1.0~10.0; and cylinder heat flux, q = 0~72.6 kW/m2. Two distinct flow structures are formed in the region of the cylinder clearance which depends on the S/D and the cylinder surface temperature, Tw. One is a quasi-stationary twin vortex at the small S/D condition (S/D\u0026lt;3.0~5.0) and the other is a shedding Karman vortex for large S/D condition (S/D\u0026gt;3.0~5.0). Behind the downstream cylinder, the Karman vortex street is formed in all conditions. The critical S/D changing to the Karman vortex increases with increasing the temperature of the upstream cylinder. The Strouhal number St under the twin vortex forming is in the range of 0.150 to 0.155 regardless of the S/D and heating conditions, while the St of the Karman vortex formed behind the downstream cylinder is decreased significantly as the S/D increases. For the large S/D, the Karman vortex is formed behind both of the cylinders then the upstream St agreed with the downstream St. St of the Karman vortex coincides with St in the single-cylinder condition taken into account of the cylinder heating conditions. For the small S/D and the upstream cylinder in a highly heated condition, the twin vortex structure behind the upstream cylinder plays a key role in the downstream shedding Karman vortex structure.","source":"DOAJ","year":2022,"language":"","subjects":["Mechanical engineering and machinery","Engineering machinery, tools, and implements"],"doi":"10.1299/transjsme.21-00381","url":"https://www.jstage.jst.go.jp/article/transjsme/88/908/88_21-00381/_pdf/-char/en","pdf_url":"https://www.jstage.jst.go.jp/article/transjsme/88/908/88_21-00381/_pdf/-char/en","is_open_access":true,"published_at":"","score":66},{"id":"ss_8c5e3c18c24c1ff1437c8343f97445e74bc73b16","title":"Abrasive Wear in Ground Engaging Tools and Its Remedial Measures","authors":[{"name":"Pankaj Dhiman"},{"name":"Dushyant Kaistha"},{"name":"Shabnam Dhiman"},{"name":"Swati Kaistha"}],"abstract":"Abstract: Wear is the degradation of material under plethora of service conditions and is considered as one of the major issue of the material used in engineering. So far, various types of wear have been recognized such as erosive wear, surface fatigue, fretting wear, adhesive wear, abrasive wear and sliding wear. Wear by abrasion accounts for more than 50% of the wear failures occurring in industries. Hard abrasive particles penetrate the components and cause damage in the form of material loss. The components of machines working undersevere conditions the soil engaging part of the earth-moving machinery, and various agricultural implements experience sever wear. The wear in agricultural equipments and tillage is the main problem. For combating with wear problem various methods have also been developed such as hardfacing, cryogenic treatment, coating and heat treatment of components, which are chosen on the basis of various conditions under which the component has to perform the desired function. Hardfacing and heat treatment is commonly used to resistthis failure, also considered as most appropriate and economically sound process for improving the life of tillage tool at farming level. In the present paper author as made the effort to review various types of wear and remedial measures","source":"Semantic Scholar","year":2022,"language":"en","subjects":null,"doi":"10.22214/ijraset.2022.46524","url":"https://www.semanticscholar.org/paper/8c5e3c18c24c1ff1437c8343f97445e74bc73b16","pdf_url":"https://doi.org/10.22214/ijraset.2022.46524","is_open_access":true,"published_at":"","score":66},{"id":"doaj_10.1299/transjsme.19-00174","title":"Target trajectory generation using clothoid curve and vehicle control for obstacle avoidance of automated driving","authors":[{"name":"Takeshi TAKIYAMA"},{"name":"Junichi FUJITA"}],"abstract":"In order to avoid an obstacle automatically for an automated vehicle, this paper investigated a method to generate a target trajectory using a clothoid curve and to control the vehicle. Although a mathematical constraints or a potential methods are often used to generate the target trajectory for an obstacle avoidance, it requires trial and error and experience, and it is also necessary to consider the vehicle’s drivability. The clothoid curve is often used for a road curve design, therefore, the curve is considered to be suitable for the characteristics of a vehicle driving. Although a clothoid curve passing through a target point is necessary for obstacle avoidance, such clothoid curve is often obtained by trial and error. Therefore, the numerical analyses were executed to obtain the characteristics of the clothoid curve, then, the method was investigated to generate the clothoid curve to pass through the target point. Furthermore, a method to generate a target avoidance trajectory was also investigated expanding the generated clothoid curve based on the traveling characteristics of a vehicle. For driving on the target trajectory satisfactory, both the position and turning angle of the vehicle are controlled by means of a steering manuplation. The controller was constructed using a 1-input 2-output system, therefore, it is very difficult to satisfy both value at the same time. Furthermore, it is also necessary to consider a nonholonomic characteristics of the vehicle. From these point, this paper investigated the optimal control using nonlinear least square probrem sequential quadratic programming(NLSSQP) by means of the time behaviour of input and output in the evaluation value. Well expected results are obtained and shown in the simulation and the experiment.","source":"DOAJ","year":2020,"language":"","subjects":["Mechanical engineering and machinery","Engineering machinery, tools, and implements"],"doi":"10.1299/transjsme.19-00174","url":"https://www.jstage.jst.go.jp/article/transjsme/86/883/86_19-00174/_pdf/-char/en","pdf_url":"https://www.jstage.jst.go.jp/article/transjsme/86/883/86_19-00174/_pdf/-char/en","is_open_access":true,"published_at":"","score":64},{"id":"doaj_10.30657/pea.2020.26.29","title":"Improving the quality level in the automotive industry","authors":[{"name":"Pacana Andrzej"},{"name":"Czerwińska Karolina"}],"abstract":"Currently, effective quality management of manufactured products is a factor determining the development of manufacturing companies. However, the identification of the source of non-compliance and the analysis of its causes are sometimes underestimated and are not followed by appropriate methodologies. The study aimed to streamline and improve the production process of aluminium pistons for passenger cars by solving the problem related to a significant number of non-compliant products. The analysis of types of nonconformities identified through penetration testing was performed. The use of histogram, brainstorming session and Pareto-Lorenz diagram was proposed, which allowed identifying the causes of the problem. The presented solution shows the practical effectiveness of a sequence of selected instruments to solve production problems. The proposed sequence of methods can be implied in other qualitative analyses in different companies.","source":"DOAJ","year":2020,"language":"","subjects":["Machine design and drawing","Engineering machinery, tools, and implements"],"doi":"10.30657/pea.2020.26.29","url":"https://doi.org/10.30657/pea.2020.26.29","is_open_access":true,"published_at":"","score":64}],"total":6522975,"page":1,"page_size":20,"sources":["CrossRef","DOAJ","Semantic Scholar"],"query":"Engineering machinery, tools, and implements"}