Journal of Theory and Practice of Engineering Science最新文献

筛选

英文中文

The Ways to Improve the Skills and Techniques of Machine Repair Fitters 提高机修钳工技能和技术的途径

Journal of Theory and Practice of Engineering Science Pub Date : 2023-10-31 DOI: 10.53469/jtpes.2023.03(10).02

Aimin Li, Shifang Yang, Zhiwei Zhou

{"title":"The Ways to Improve the Skills and Techniques of Machine Repair Fitters","authors":"Aimin Li, Shifang Yang, Zhiwei Zhou","doi":"10.53469/jtpes.2023.03(10).02","DOIUrl":"https://doi.org/10.53469/jtpes.2023.03(10).02","url":null,"abstract":"In this important understanding of machining, mechanical maintenance fitter occupies an important position. Machine repair fitters are mainly responsible for the processing and maintenance of various mechanical equipment, and use scientific control methods to restore mechanical properties. Therefore, we can see that the faults in machine maintenance need quick maintenance by fitters to solve various problems that often occur in machine maintenance. In this case, the fitter must master basic machine maintenance skills and use excellent information technology to assist in the installation of various machines. Therefore, this article gives a brief introduction to the machine fitters used for machine maintenance and shows how to increase their repair capacity to make machine maintenance more efficient. With the progress of scientific and the rapid increase in productivity, competition between enterprises is more and more intense. If enterprises want to maintain a high competitiveness and advantages, they need to focus on the research and development of core technology of their companies and improve core competitiveness. So in such a large background, many companies choose to focus on the development of their core business, and then they need other enterprises to complete their own non-core business resources. In such a development process, the logistics industry will gradually separated and then the third-party logistic enterprises show up.","PeriodicalId":489516,"journal":{"name":"Journal of Theory and Practice of Engineering Science","volume":"35 16","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135813478","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}

引用次数: 0

Deep Learning for Precise Robot Position Prediction in Logistics 物流中机器人精确位置预测的深度学习

Journal of Theory and Practice of Engineering Science Pub Date : 2023-10-31 DOI: 10.53469/jtpes.2023.03(10).05

Chang Che, Bo Liu, Shulin Li, Jiaxin Huang, Hao Hu

{"title":"Deep Learning for Precise Robot Position Prediction in Logistics","authors":"Chang Che, Bo Liu, Shulin Li, Jiaxin Huang, Hao Hu","doi":"10.53469/jtpes.2023.03(10).05","DOIUrl":"https://doi.org/10.53469/jtpes.2023.03(10).05","url":null,"abstract":"This study presents an interdisciplinary investigation at the nexus of mechanical engineering and computer science, aimed at advancing the field of logistics automation. In response to the escalating demands of global cargo transportation, the integration of these disciplines assumes paramount importance. Conducted within the domain of Dortmund University of Technology’s Material Flow and Warehousing Chair, this research focuses on the precise control of robots, a task contingent on accurate positional information. Leveraging a controlled internal logistics precinct, the study delves into the transformation of raw sensor data, comprising accelerometers, gyroscopes, and magnetometers, into precise position predictions. This process entails meticulous data preprocessing, encompassing synchronization and calibration procedures, yielding crucial parameters such as absolute velocity and accelerations along both parallel and perpendicular axes. The study employs deep learning, specifically a 2D Convolutional Neural Network (2D-CNN), for predictive modeling. This architecture excels in extracting intricate spatial features from sensor data. Training is conducted under the guidance of an Asymmetric Gaussian loss function, custom-tailored to accommodate the idiosyn- crasies of real-world sensor data. The results evince the efficacy of this approach, evidenced by remarkably low mean squared errors in predicting robot positions. Beyond its immediate applications in logistics automation, this research underscores the potential of interdisciplinary collaboration in addressing complex sensor data challenges.","PeriodicalId":489516,"journal":{"name":"Journal of Theory and Practice of Engineering Science","volume":" 16","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135813714","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}

引用次数: 4

首页上一页