scholarly journals A Methodology for Flexible Implementation of Collaborative Robots in Smart Manufacturing Systems

Robotics ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 9
Author(s):  
Hermes Giberti ◽  
Tommaso Abbattista ◽  
Marco Carnevale ◽  
Luca Giagu ◽  
Fabio Cristini

Small-scale production is relying more and more on personalization and flexibility as an innovation key for success in response to market needs such as diversification of consumer preferences and/or greater regulatory pressure. This can be possible thanks to assembly lines dynamically adaptable to new production requirements, easily reconfigurable and reprogrammable to any change in the production line. In such new automated production lines, where traditional automation is not applicable, human and robot collaboration can be established, giving birth to a kind of industrial craftsmanship. The idea at the base of this work is to take advantage of collaborative robotics by using the robots as other generic industrial tools. To overcome the need of complex programming, identified in the literature as one of the main issues preventing cobot diffusion into industrial environments, the paper proposes an approach for simplifying the programming process while still maintaining high flexibility through a pyramidal parametrized approach exploiting cobot collaborative features. An Interactive Refinement Programming procedure is described and validated through a real test case performed as a pilot in the Building Automation department of ABB in Vittuone (Milan, Italy). The key novel ingredients in this approach are a first translation phase, carried out by engineers of production processes who convert the sequence of assembly operations into a preliminary code built as a sequence of robot operations, followed by an on-line correction carried out by non-expert users who can interact with the machine to define the input parameters to make the robotic code runnable. The users in this second step do not need any competence in programming robotic code. Moreover, from an economic point of view, a standardized way of assessing the convenience of the robotic investment is proposed. Both economic and technical results highlight improvements in comparison to the traditional automation approach, demonstrating the possibility to open new further opportunities for collaborative robots when small/medium batch sizes are involved.

Blockchain is going to be the most fundamental technology, and will change the world — going forward. In fact, the revolution has already begun. The birth of Industry 4.0 aka the Fourth Industrial Relution (I4.0), has created a need for autonomous and integrated, secure manufacturing systems. The current smart systems lack the decentralized decision making and real-time communication infrastructure, which is a condition for adaptive, smart manufacturing systems. In this paper, an autonomous, secure and collaborative platform based on Blockchain technology, is presented to adapt to such results. In support with Internet of Things (IoT) and cloud services, a Blockchain Driven Cyber Physical Production System (BDCPS) architecture is designed to communicate with machines, users, devices, suppliers and other peers. Using the Smart Contracts feature and trust-less peer-to-peer decentralized ledger feature, BDCPS will validate the claim with a small-scale real-life Blockchain with IoT system. This implementation case study will be running a private Blockchain on a single board computer, and bridged to a microcontroller containing IoT sensors. The applications of this system in automotive manufacturing industry are presented, to proceed towards Industry 4.0.


2021 ◽  
Vol 11 (6) ◽  
pp. 2850
Author(s):  
Dalibor Dobrilovic ◽  
Vladimir Brtka ◽  
Zeljko Stojanov ◽  
Gordana Jotanovic ◽  
Dragan Perakovic ◽  
...  

The growing application of smart manufacturing systems and the expansion of the Industry 4.0 model have created a need for new teaching platforms for education, rapid application development, and testing. This research addresses this need with a proposal for a model of working environment monitoring in smart manufacturing, based on emerging wireless sensor technologies and the message queuing telemetry transport (MQTT) protocol. In accordance with the proposed model, a testing platform was developed. The testing platform was built on open-source hardware and software components. The testing platform was used for the validation of the model within the presented experimental environment. The results showed that the proposed model could be developed by mainly using open-source components, which can then be used to simulate different scenarios, applications, and target systems. Furthermore, the presented stable and functional platform proved to be applicable in the process of rapid prototyping, and software development for the targeted systems, as well as for student teaching as part of the engineering education process.


2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Soojeen Jang ◽  
Yanghon Chung ◽  
Hosung Son

PurposeThrough the resource-based view (RBV) and contingency theory, this study empirically investigates the impacts of smart manufacturing systems' maturity levels on the performance of small and medium-sized enterprises (SMEs). Moreover, it aims to examine how industry types (i.e. high- and low-tech industries) and human-resource factors (i.e. the proportion of production workers to total workers) as contingency factors influence the effects of smart manufacturing systems.Design/methodology/approachThe study conducted an empirical investigation of a sample of 163 Korean manufacturing SMEs. This study used an ordinary least squares regression to examine the impacts of the maturity levels of smart manufacturing systems on financial performance. Moreover, the impacts on operational efficiency were analysed using data envelopment analysis based on bootstrap methods and Tobit regression.FindingsThe RBV results indicate that the higher the maturity levels of smart manufacturing systems, the higher the financial performance and operational efficiency. Moreover, based on contingency theory, this study reveals that the effect of the maturity levels of smart manufacturing systems on financial performance and operational efficiency depends on firms' industry types and the proportion of production workers.Research limitations/implicationsThis study shows that the introduction of smart manufacturing systems can help SMEs achieve better financial performance and operational efficiency. However, their effectiveness is contingent on firms' industry types and the characteristics of their human resources.Practical implicationsSince the effects of the maturity levels of smart manufacturing systems on SME performance differ depending on their industries and the characteristics of human resources, managers need to consider them when introducing or investing in smart manufacturing systems.Originality/valueBased on the RBV and contingency theory, this is the first empirical study to examine the moderating effects of industry types and the proportion of production workers on the impacts of the maturity levels of smart manufacturing systems on the financial performance and operational efficiency of SMEs.


Sign in / Sign up

Export Citation Format

Share Document