Robotics and Industry 4.0

Goel, Ruchi; Gupta, Pooja

doi:10.1007/978-3-030-14544-6_9

Cited by 90 publications

(55 citation statements)

References 12 publications

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“…The potential advantages for integrating three-dimensional (3D) printing (3DP) into house construction are significant in Construction Industry 4.0 (Ndlovu et al, 2019;Baz et al, 2020;Goel and Gupta, 2020); these include the capacity for mass customisation of designs and parameters for functional and aesthetic purposes, reduction in construction waste from highly precise material placement and the use of recycled waste products in layer deposition materials (Shakor et al, 2020). Industry 4.0 refers to the Fourth Industrial Revolution, which is enabled by the use of the advanced technologies including 3DP into the production (Tjahjono et al, 2017).…”

Section: Background Reviewmentioning

confidence: 99%

Criteria development for sustainable construction manufacturing in Construction Industry 4.0

Tahmasebinia

Sepasgozar

Shirowzhan

et al. 2020

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Purpose This paper aims to present the sustainable performance criteria for 3D printing practices, while reporting the primarily computations and lab experimentations. The potential advantages for integrating three-dimensional (3D) printing into house construction are significant in Construction Industry 4.0; these include the capacity for mass customisation of designs and parameters for functional and aesthetic purposes, reduction in construction waste from highly precise material placement and the use of recycled waste products in layer deposition materials. With the ultimate goal of improving construction efficiency and decreasing building costs, applying Strand7 Finite Element Analysis software, a numerical model was designed specifically for 3D printing in a cement mix incorporated with recycled waste product high-density polyethylene (HDPE) and found that construction of an arched truss-like roof was structurally feasible without the need for steel reinforcements. Design/methodology/approach The research method consists of three key steps: design a prototype of possible structural layouts for the 3DSBP, create 24 laboratory samples using a brittle material to identify operation challenges and analyse the correlation between time and scale size and synthesising the numerical analysis and laboratory observations to develop the evaluation criteria for 3DSBP products. The selected house consists of layouts that resemble existing house such as living room, bed rooms and garages. Findings Some criteria for sustainable construction using 3DP were developed. The Strand7 model results suggested that under the different load combinations as stated in AS1700, the maximum tensile stress experienced is 1.70 MPa and maximum compressive stress experienced is 3.06 MPa. The cement mix of the house is incorporated with rHDPE, which result in a tensile strength of 3 MPa and compressive strength of 26 MPa. That means the house is structurally feasible without the help of any reinforcements. Investigations had also been performed on comparing a flat and arch and found the maximum tensile stress within a flat roof would cause the concrete to fail. Whereas an arch roof had reduced the maximum tensile stress to an acceptable range for concrete to withstand loadings. Currently, there are a few 3D printing techniques that can be adopted for this purpose, and more advanced technology in the future could eliminate the current limitation on 3D printing and bring forth this idea as a common practice in house construction. Originality/value This study provides some novel criteria for evaluating a 3D printing performance and discusses challenges of 3D utilisation from design and managerial perspectives. The criteria are relied on maximum utility and minimum impact pillars which can be used by scholars and practitioners to measure their performance. The criteria and the results of the computation and experimentation can be considered as critical benchmarks for future practices.

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Section: Background Reviewmentioning

confidence: 99%

Criteria development for sustainable construction manufacturing in Construction Industry 4.0

Tahmasebinia

Sepasgozar

Shirowzhan

et al. 2020

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“…These factories will become a highly productive manufacturing environment, with computers being used at various production stages and transportation networks that share knowledge continuously without much human involvement (Bartodziej, 2017). Furthermore, adaptable automation solutions enable custom-made output, which, in turn, helps meet the increasingly complex needs of customers and consumers (Goel and Gupta, 2020).…”

Section: Ejmsmentioning

confidence: 99%

The impact of Industry 4.0 on organizational performance: the case of Pakistan's retail industry

Ali

Xie

2021

EJMS

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PurposeThe purpose of this paper was to assess and determine the impact of the five core technologies of Industry 4.0 (3D Printing, Big Data Analytics, Cloud Computing, Internet of Things (IoT) and Robotics) on the organizational performance of the retail industry in the context of Pakistan.Design/methodology/approachPakistan's retail industry was chosen as the target sector, and the target population was composed of senior-level employees, including managers from first-level positions to top-level positions, as well as subordinate employees working under the supervision of first-level managers, possessing the technological know-how of Industry 4.0. The data were collected through a matrix-based survey questionnaire that was based on a five-point Likert scale, ranging from “strongly agree” to “strongly disagree.” The process of data analysis was conducted using IBM SPSS Statistics.FindingsThe findings obtained by this research work showed a significant relationship among the five core pillars of Industry 4.0 and the organizational performance of Pakistan's retail industry. Besides, the obtained findings provided preliminary evidence that Industry 4.0's disruptive technologies, particularly, 3D printing, big data analytics, cloud computing, IoT and robotics, could help Pakistan's retail industry solve various problems and challenges, such as meager revenues, increased expenses and unorganized systems.Originality/valueThe present study extended the theoretical body of knowledge through studying and examining Industry 4.0's five crucial factors that significantly contribute to the service sector, particularly, the retail industry, of the big emerging markets (BEM) economies, including Pakistan.

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“…In this manner, the rangers and drones can patrol these target areas to prevent poaching. Finally, the inspection of power lines, infrastructure, wind turbines, pipelines, chimneys and gas leaks [35][36][37] using drones facilitates the work of companies in the industrial sector, since drones provide thermography, photogrammetry, and scanning information to minimise expenses and risks.…”

Section: Related Workmentioning

confidence: 99%

Towards Autonomous Drone Racing without GPU Using an OAK-D Smart Camera

Rojas-Perez

Martínez-Carranza

2021

Sensors

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Recent advances have shown for the first time that it is possible to beat a human with an autonomous drone in a drone race. However, this solution relies heavily on external sensors, specifically on the use of a motion capture system. Thus, a truly autonomous solution demands performing computationally intensive tasks such as gate detection, drone localisation, and state estimation. To this end, other solutions rely on specialised hardware such as graphics processing units (GPUs) whose onboard hardware versions are not as powerful as those available for desktop and server computers. An alternative is to combine specialised hardware with smart sensors capable of processing specific tasks on the chip, alleviating the need for the onboard processor to perform these computations. Motivated by this, we present the initial results of adapting a novel smart camera, known as the OpenCV AI Kit or OAK-D, as part of a solution for the ADR running entirely on board. This smart camera performs neural inference on the chip that does not use a GPU. It can also perform depth estimation with a stereo rig and run neural network models using images from a 4K colour camera as the input. Additionally, seeking to limit the payload to 200 g, we present a new 3D-printed design of the camera’s back case, reducing the original weight 40%, thus enabling the drone to carry it in tandem with a host onboard computer, the Intel Stick compute, where we run a controller based on gate detection. The latter is performed with a neural model running on an OAK-D at an operation frequency of 40 Hz, enabling the drone to fly at a speed of 2 m/s. We deem these initial results promising toward the development of a truly autonomous solution that will run intensive computational tasks fully on board.

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Robotics and Industry 4.0

Cited by 90 publications

References 12 publications

Criteria development for sustainable construction manufacturing in Construction Industry 4.0

Criteria development for sustainable construction manufacturing in Construction Industry 4.0

The impact of Industry 4.0 on organizational performance: the case of Pakistan's retail industry

Towards Autonomous Drone Racing without GPU Using an OAK-D Smart Camera

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