Robot adoption and FDI driven transformation in the automotive industry

Anzolin, Guendalina; Andreoni, Antonio; Zanfei, Antonello

doi:10.1504/ijatm.2020.10030508

Cited by 7 publications

(10 citation statements)

References 32 publications

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“…Mainly analyze the motion of the robot from the perspective of motion, and solve the faults in the motion of the robot in time, so that the development of the robot is in line with the development of international electrical engineering [10]. In recent years, in the academic and robotic application technology research at home and abroad, the technology research of robots has become the focus of discussion, as a way to improve the overall level of industrial manufacturing and production, through the characteristics of robot automation technology, for improve the negative effects of traditional industrial production and construction, and to find and solve the problems in robot motion control at the first time, is the significance of promoting the widespread use of robots [11].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Motion Control System of IoT Intelligent Robot Based on Improved ResNet Model

Farisi

2023

Journal of Sensors

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In order to further optimize the precision and efficiency of intelligent robot navigation system control, an IoT intelligent robot motion control system based on the improved ResNet model is proposed. Based on the deep learning method, using the Faster R-CNN target detection architecture and the ResNet50 convolutional neural network, the network is trained according to the characteristics of the operation target of the distribution line maintenance robot system. On this basis, combined with the binocular vision ranging principle, the coordinates of the job target in the camera coordinate system are measured, and the coordinates are converted into the robot base coordinate system through hand-eye calibration, so as to complete the spatial positioning of the job target. The results showed that the errors of the binocular measurement methods adopted by the system are all within 1%. Conclusion. The method can well adapt to the complex background of the operation scene, the change of illumination, and the partial occlusion of the target and can meet the requirements of the distribution line maintenance robot for the measurement and positioning of the target space.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Motion Control System of IoT Intelligent Robot Based on Improved ResNet Model

Farisi

2023

Journal of Sensors

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“…Regarding determinants of automation technologies there are few contributions that explore this relation. On industrial robots specifically, Anzolin et al. (2020) look at what drives the adoption of industrial robots in the automotive sector, and they find that ecosystem variables such as the competitiveness and the innovativeness of the country matter more than foreign direct investments for industrial robots use.…”

Section: Literature Reviewmentioning

confidence: 99%

Robotising, but how? Evidence from the automotive sector in South Africa

Anzolin

Andreoni

2023

JMTM

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PurposeThis paper focuses on understanding firm-level determinants of industrial robots' adoption and how these determinants result in heterogenous processes of robotisation across firms within the same sector. The paper presents results from in-depth case studies of final assemblers in the South African automotive sector.Design/methodology/approachThe research has been conducted through multiple case studies with a focus on final assemblers. During the case studies, as well as before and after it, data coming from in-depth semi-structured interviews were triangulated with secondary data available from the international database on industrial robots' adoption and documents provided by firms and institutions.FindingsThis paper identifies three firm-level determinants of robotisation – i.e. modularity of the production process, flexibility in the use of technology and stability in product design. The results also showed that firms' robotisation depend on each of these determinants as well as their interdependence. The authors introduce a framework to study interdependence between these technology–organisational choices, which reveals heterogenous patterns of technology deployment and related managerial implications.Originality/valueThis research introduces a new framework on factors driving industrial robotisation – a key digital production technology – and offers empirical evidence of the heterogenous deployment of this technology. The authors identify two main manufacturing approaches to robotisation in the automotive sector: one in which the firm designs a robotised process around a certain product design – i.e. the German/American way and one in which the firm designs its product based on certain robotised processes – i.e. the Japanese way. These findings are valuable for both industry, operational research and the scientific community as they reveal heterogeneity on the “how” of robotisation and implications for manufacturing technology management.

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“…The development scale and technological level of automobile manufacturing industry is an important symbol of a comprehensive national strength for a country. This industry involves cross amalgamation of several fields like machinery, materials, logistics, management etc., as one of the first industries that introduce robotics into the manufacturing process [3][4][5][6][7]. In the field of automobile manufacturing, traditional labor-intensive industries are being replaced by technology-intensive industries gradually.…”

Section: Introductionmentioning

confidence: 99%

“…In the field of automobile manufacturing, traditional labor-intensive industries are being replaced by technology-intensive industries gradually. Thus, the construction of intelligent manufacturing factories relying on automated production will become the mainstream, and the robots will become more and more important in relevant production activities [2][3][4]7], meaning that optimization of robot's performance is the key to improvement of production efficiency.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Reconfigurability in Automobiles—Structure, Manufacturing and Algorithm for Automobiles

Zhuang¹,

Guan²,

Xu³

et al. 2022

IJAMM

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Review Reconfigurability in Automobiles—Structure, Manufacturing and Algorithm for Automobiles Zheming Zhuang 1, Yuntao Guan 1, Shuangjia Xu 2,4, and Jian S. Dai 3,4, * 1 Key Laboratory of Mechanism Theory and Equipment Design of the Ministry of Education, Centre for Advanced Mechanisms and Robotics, Tianjin University, Tianjin, 300072, China zhuangzheming@tju.edu.cn (Z.Z.); guanyuntao_018@tju.edu.cn (Y.G.) 2 DH Robotics Ltd, Shenzhen, China; Shuangjia.xu@dh-robotics.com 3 Institute for Robotics, Southern University of Science and Technology, Shenzhen, China 4 Centre for Robotics Research, King’s College London, London, UK * Correspondence: jian.dai@kcl.ac.uk Received: 9 November 2022 Accepted: 10 November 2022 Published: 18 December 2022 Abstract: For the automobile design and manufacturing, as a typical representative of the industry, the development and upgrading represent the application of the state-of-the-art technology in the industry. With a period of development, the related technology of traditional manufacturing factories for automobiles are found with some common issues while improving. As such, the reconfigurable intelligent manufacturing factory of the automobiles is fast developed with focus on reconfigurability in structures, manufactures and algorithms, thus advancing the level of the reconfigurable intelligent manufacturing continuously. With a sufficient reconfigurable manufacturing technology as the basis, reconfigurability can be better introduced to the structure design and the driving algorithms of the automobiles. Reconfigurability provides a new bridge for transforming the traditional automobile to the reconfigurable automobile, and a new driving force for the upgrading of the reconfigurable driving algorithms.

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Robot adoption and FDI driven transformation in the automotive industry

Cited by 7 publications

References 32 publications

[Retracted] Motion Control System of IoT Intelligent Robot Based on Improved ResNet Model

[Retracted] Motion Control System of IoT Intelligent Robot Based on Improved ResNet Model

Robotising, but how? Evidence from the automotive sector in South Africa

Reconfigurability in Automobiles—Structure, Manufacturing and Algorithm for Automobiles

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