1,721,389 research outputs found
Human-Centered Design in Industry 5.0: Leveraging Technology for Maximum Efficiency
No full textThe shift from Industry 4.0 to Industry 5.0 represents a significant change in how technologies are approached in workplace design. Industry 4.0 was characterized by the automation of the production process, with a focus on maximizing output and efficiency. However, as Industry 5.0 becomes more relevant, the focus is moving toward the importance of putting people at the center of the production process. This means designing workspaces that prioritize human comfort and productivity and finding ways to integrate technology that supports and enhances human abilities. One of the key technologies that is helping to facilitate this transition is collaborative robots or cobots. By working alongside humans, cobots can help improve production efficiency while allowing for greater human involvement in the production process. However, to fully leverage the potential of cobots, it is essential to design workspaces that are optimized for human comfort and productivity. This requires taking into account the needs and preferences of both human and robotic resources and finding ways to allocate tasks in a way that maximizes efficiency while also taking into account human well-being. One promising approach to achieving this goal is the implementation of a dynamic multi-objective task allocation system, as presented in this work. This method uses physiological and performance data to evaluate the well-being of human operators and dynamically re-allocate tasks to ensure that operators are not overworked or fatigued. This is a significant step towards creating truly human-centered production environments that prioritize the well-being and productivity of human workers
A Multi-Criteria Decision-Making Model Based on Fuzzy Logic and AHP for the Selection of Digital Technologies
Full text linkThe presence of Industry 4.0 national plans and the ever-increasing international competition are forcing companies to embark on digitalization projects of their industrial plants. Time and money, however, are a constraint and, in addition to that, there is a considerable lack of works in the academic literature with regards to specific models for the selection of digital technologies. Starting from our methodological framework, we developed a multi-criteria decision-making model for the digitalization of industrial plants. The model is based on both Fuzzy Logic and AHP and is combined with an existing hierarchical classification of digital technologies in an attempt to highlight the advantage of adopting similar and easily interconnectable technologies. Finally, the model is applied to a simple case study to test its validity. © 2022 Elsevier B.V.. All rights reserved
Modelling and Managing “Station-Sequence” Parts Feeding in the I4.0 Era: A Simulation Approach for In-Plant Logistics
Full text linkParts feeding is a complex logistic problem that is further complicated by the market demand for more product variety, which forces companies and manufacturers to adopt the mixed model approach in their assembly systems. Among the parts feeding policies for mixed-model assembly systems, there is the so-called "station-sequence" policy, where stationary kits are prepared using sequences of parts that follow the sequence of the production models. This policy can reduce stocks at the assembly stations but can also lead to potential production stops due to its low robustness. The aim of this paper is to study the station-sequence parts feeding policy, focusing on its dynamic time dependence and analyzing the effects of time and model mix perturbations on the performance of the assembly system. The study was conducted through a simulation model and a statistical analysis. The final discussion also provides a set of Industry 4.0 (I4.0) enabled solutions that are able to address the negative effect of variability on the performance of the system
A new approach for performance assessment of parallel and non-bottleneck machines in a dynamic job shop environment
No full texturpose: The current study aims to propose a new analytical approach by considering energy consumption (EC), maximum tardiness and completion time as the primary objective functions to assess the performance of parallel, non-bottleneck and multitasking machines operating in dynamic job shops. Design/methodology/approach: An analytical and iterative method is presented to optimize a novel dynamic job shop under technical constraints. The machine’s performance is analyzed by considering the setup energy. An optimization model from initial processing until scheduling and planning is proposed, and data sets consisting of design parameters are fed into the model. Findings: Significant variations of EC and tardiness are observed. The minimum EC was calculated to be 141.5 hp.s when the defined decision variables were constantly increasing. Analysis of the optimum completion time has shown that among all studied methods, first come first served (FCFS), earliest due date (EDD) and shortest processing time (SPT) have resulted in the least completion time with a value of 20 s. Originality/value: Considerable amount of energy can be dissipated when parallel, non-bottleneck and multitasking machines operate in lower-power modes. Additionally, in a dynamic job shop, adjusting the trend and arrangement of decision variables plays a crucial role in enhancing the system’s reliability. Such issues have never caught the attention of scientists for addressing the aforementioned problems. Therefore, with these underlying goals, this paper presents a new approach for evaluating and optimizing the system’s performance, considering different objective functions and technical constraints
Strategic view on cobot Deployment in Assembly 4.0 systems
No full textCollaborative robots (cobots) are intended to physically interact with humans in a shared workspace. While cobots research proliferated in the recent decade, only scant attention was given to the strategic consideration of deploying them. The obvious strategic consideration is related to economic cost-benefits trade-off. However, the economic decision is tightly tied to the technology improvement rate, as cobots lifetime expectancy strongly depend on the technological developments. In this regard, the difference between different types of cobots may be dramatic. Another strategic issue is the sociological effects including the reaction of the operators and unions to cobot deployment. This paper reviews the related literature and proposes a model to analyze the underlying factors and facilitate the decision making process of: where and when to deploy which cobot
A framework for the integration of traditional and collaborative robotics
No full textIn recent years, a new type of robotic manipulator, i.e., collaborative robots (cobots), was introduced in the market. Their ability to share the workspace with the operator without any safety fences allows automating tasks that were too difficult or too expensive to automate. Moreover, collaborative workcells merge the flexibility of the human operator and the accuracy of automated systems. However, they are usually separated from the main industrial plant, reducing their influence on the process. Hence, a framework to connect traditional and collaborative robotics is presented in this work. The framework is developed in three layers with a top-down approach, where a first offline layer will solve the task scheduling problem of a human-robot collaborative workcell. Due to the unpredictability of the human operator, it is important to develop a second layer to monitor the operator and dynamically adapt the cobot. A possible implementation with depth cameras is presented along with a control scheme. Lastly, a third layer is responsible for the connection between the collaborative workcell and the other devices connected to the process line. A case study presents a possible application of the proposed approach
The impact of augmented reality on learning curves and mental workload: A preliminary experimental study
Full text linkThe learning process has always been fundamental in the industrial environment to correctly learn the right process and to perform it faster, increasing efficiency by also minimizing errors, consequently. Nowadays, new technologies that are emerging in this field are based on augmented reality, and, through a motion capture architecture, it is possible to real-Time follow the operators' activities and guide them in the next ones, improving the learning process. Therefore, this paper presents an architecture setup and the first preliminary tests, realized in the Industrial Plants and Logistics Laboratory of the University of Padua, in order to study the benefits that this type of technology can provide. The main findings are the decrease in the time required to learn the job along with a smaller operator's cognitive workload during the training
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