{"title":"Forces Acting on A Bearing of an Electric Motor for The Railway Carriage Rounding A Curve","authors":"R. Usubamatov","doi":"10.32474/ARME.2018.01.000104","DOIUrl":"https://doi.org/10.32474/ARME.2018.01.000104","url":null,"abstract":"Most of the textbooks of machine dynamics and books that dedicated to gyroscope theory content the typical examples with solving of gyroscope effects [1-3]. However, the practice demonstrates that the known mathematical models for acting forces on the rotating objects do not match their actual forces and motions [4,5]. Recent investigations in the physical principles of gyroscopic motions have presented the new mathematical models of forces acting on a gyroscope [6-8]. The action of the external load on a rotating object generates several inertial resistance and precession torques based on the action of the rotating mass elements of the rotating object. Resistance torque is generated by the action of the centrifugal and Coriolis forces of the rotating object’s mass Abstract Recent investigations in gyroscope effects have demonstrated that their origin has more complex nature that represented in known publications. On a gyroscope are acting simultaneously and interdependently eight inertial torques around two axes. These torques are generated by the centrifugal, common inertial and Coriolis forces as well as the change in the angular momentum of the masses of the spinning rotor. The action of these forces manifests the inertial resistance and precession torques on any rotating objects. New mathematical models for the inertial torques acting on the spinning rotor demonstrate fundamentally different approaches for solving of gyroscope problems in engineering. This is the very important result because the stubborn tendency in engineering is expressed by the increasing of a velocity of rotating objects. The numerous designs of the movable machines and mechanisms contain spinning components like turbines, rotors, discs and others lead to the proportional increase of the magnitudes of inertial forces that are forming their processes of work. This work considers the inertial torques acting on the on a rotor of an electric railway carriage rounding a curve, which expresses the gyroscopic effects.","PeriodicalId":203129,"journal":{"name":"Advances in Robotics & Mechanical Engineering","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127926049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
F. Silva, W. F. S. Araújo, R. Campilho, A. Baptista, G. Pinto
{"title":"How to Become a Manufacturing Cell Fully-Automated Without Robots: Case-Study in the Automotive Components Industry","authors":"F. Silva, W. F. S. Araújo, R. Campilho, A. Baptista, G. Pinto","doi":"10.32474/arme.2018.01.000102","DOIUrl":"https://doi.org/10.32474/arme.2018.01.000102","url":null,"abstract":"final components or subassemblies suppliers (Tier 1, 2 and 3), distributors, retailers and customers. The OEM’s are constantly looking for suppliers to whom they can delegate responsibilities in areas such development, sourcing and planning, and this constant search induces pressures in the suppliers to lower prices and make deliveries within the stipulated deadlines, without compromising the products quality assurance. Indeed, quality and delivery time are indicators that highly affect the evaluation of the preferred product supplier [7,9]. In the specific case of the car seat, the evolution from mass to a personalized production, according to customer needs can also be applied. Following the evolution of automobile production, the seat was traditionally produced as an integral part of the automobile, where the available possible configurations were Abstract Productivity is a key factor for companies manufacturing parts and sets to the automotive industry. Automation plays an important role in this matter, allowing development of entire manufacturing cells without the direct need of workers. Even in countries where the labour cost is relatively low, it becomes necessary to improve the level of automation applied to manufacture cells and reduce the dependence of the human labour unpredictability, also increasing the quality and reducing the costs. This case study was developed based on an industrial request in order to improve a semi-automatic cell devoted to seat suspension mat manufacturing. The original cell allows several automatic operations but it needs two workers for two specific operations not considered in the initial design. Thus, new concepts of wire feeding and manipulation were developed in order to allow a better material flow throughout the cell. The new cell was designed and built with success, allowing obtain a fully-automated system, which leads to a better productivity and reliability of the manufacturing process.","PeriodicalId":203129,"journal":{"name":"Advances in Robotics & Mechanical Engineering","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121920913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Introducing Deep Mind Learning Modeling","authors":"Sadique Shaikh, Shabeena Khan","doi":"10.32474/ARME.2018.01.000101","DOIUrl":"https://doi.org/10.32474/ARME.2018.01.000101","url":null,"abstract":"Since 2010 Google & Uber researchers and engineers are engaged in DeepMind project with registered company in UK and USA DeepMind. Google used to “Deep reinforcement learning†to implement DeepMind technology. We can see the results of DeepMind learning with live examples of Google Assistance, Google Echo- Smart Speaker and Google home assistance, Google AI-God and AI Church, Amazon Alexa, Amazon echo, Apple Siri. Google is one the strong player in DeepMind learning technology but two major players also Amazon and Apple after Google. This technology brings next wave in future about to 2029 but also spoiling human ethics when AI became more then of human intelligence. This short communication discussed fundamental aspect related to DeepMind designing and engineering with the help of model.","PeriodicalId":203129,"journal":{"name":"Advances in Robotics & Mechanical Engineering","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121949551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}