DO FORA À PELE: NOTAS DE UMA PRESENÇA ESTRANHA EM UM ESTÚDIO DE TATUAGEM

Possui como tema de fundo a relação entre corpo-sujeito-sociedade. Trata de uma narrativa que aborda, majoritariamente, os usos físicos do corpo, por meio das modificações corporais pela tatuagem, associando-as às suas respectivas atribuições simbólicas. São seus objetivos específicos: 1) mapear os tipos de usos físicos do corpo concernentes às intervenção de tatuagem; 2) identificar as razões sociais ou subjetivas desse tipo de investimento no corpo; 3) identificar os signos e símbolos que são impressos na pele; e 4) mapear as regiões onde tais investimentos acontecem. Baseia-se nas proposições teóricas de David Le Breton acerca do significante corpo e das práticas de modificação corporal na contemporaneidade. Os resultados construídos apontam para um corpo polissêmico e cambiante, que se apresenta sob diferentes arranjos, para atuar em seus tempos e espaços

Intelligent Sensors for Real-Time Decision-Making

The simultaneous integration of information from sensors with business data and how to acquire valuable information can be challenging. This paper proposes the simultaneous integration of information from sensors and business data. The proposal is supported by an industrial imple mentation, which integrates intelligent sensors and real-time decision-making, using a combination of PLC and PC Platforms in a three-level architecture: cloud-fog-edge. Automatic identification intelligent sensors are used to improve the decision-making of a dynamic scheduling tool. The proposed platform is applied to an industrial use-case in analytical Quality Control (QC) laborato ries. The regulatory complexity, the personalized production, and traceability requirements make QC laboratories an interesting use case. We use intelligent sensors for automatic identification to improve the decision-making of a dynamic scheduling tool. Results show how the integration of intelligent sensors can improve the online scheduling of tasks. Estimations from system processing times decreased by over 30%. The proposed solution can be extended to other applications such as predictive maintenance, chemical industry, and other industries where scheduling and rescheduling are critical factors for the production.This work was supported by FCT, through IDMEC, under LAETA, project UIDB/50022/2020

Reinforcement Learning for Dual-Resource Constrained Scheduling

This paper proposes using reinforcement learning to solve scheduling problems where two types of resources of limited availability must be allocated. The goal is to minimize the makespan of a dual-resource constrained flexible job shop scheduling problem. Efficient practical implementation is very valuable to industry, yet it is often only solved combining heuristics and expert knowledge. A framework for training a reinforcement learning agent to schedule diverse dual-resource constrained job shops is presented. Comparison with other state-of-theart approaches is done on both simpler and more complex instances that the ones used for training. Results show the agent produces competitive solutions for small instances that can outperform the implemented heuristic if given enough time. Other extensions are needed before real-world deployment, such as deadlines and constraining resources to work shifts

The impact of intelligent automation in internal supply chains

Nowadays, industry is being forced to produce smaller and more diverse batches, increasing the complexity of internal supply chains. Data has become a valuable asset, supporting the development of intelligent automation solutions. Decision support systems, which leverage data, require the automation pyramid to be more flexible, as information needs to be exchanged simultaneously and in real-time with all automation layers. This paper proposes a framework for intelligent automation to deal with current challenges in acquisition and management of data in industrial settings, towards feeding decision support systems. It frames the topic within the scope of internal supply chains, addressing the framework impact on work practices within the organisation. Two real industrial implementation cases are examined, in the wood and chemical industries. Results help practitioners address the most impactful challenges affecting the performance of internal supply chains, by developing systems which are faster, more flexible, efficient and with improved quality.This work was supported by FCT, through IDMEC, under LAETA, project UIDB/50022/2020

Multi-agent system for dynamic scheduling

This paper proposes a flexible manufacturing system based on intelligent computational agents. A Multi-Agent System composed of 4 types of reactive agents was designed to control the operation of a real implementation in the Intelligent Automation Lab at Instituto Superior Tecnico. This implementation was ´ based and constructed analogously to a known benchmark, AIPPRIMECA. The agents were modelled using Petri nets and agent communications were defined through the combination of FIPA Interaction Protocols. The system was tested under the conditions of static and dynamic scenarios, having its performance validated whenever possible by comparison with results from a Potential Fields Approach in the same benchmark. Overall, the performance exhibited by the proposed MAS was slightly better and it is worth highlighting the simple behaviour of each agent and ability to respond in real-time to all the dynamic scenarios tested

Assessing the impact of automation in pharmaceutical quality control labs using a digital twin

Nowadays, pharmaceutical Quality Control (QC) laboratories have complex workflows where analysts test different samples simultaneously. Tests ensure the physical properties of drugs are expected and within guidelines. Each test follows an analytical procedure containing tasks. Cyber-Physical Production System (CPPS) improves tasks/operations; however, accurate cost analysis with reasoned data is challenging. Theoretical estimation of impacts requires a high level of abstraction and fails to capture the proper behavior of the workflow. This paper proposes a method for evaluating the introduction of automation in a pharmaceutical QC laboratory. In the proposed methodology, this paper developed a simulation model of the analytical workflow of the tests. The impact assessment compares the current As-Is and future To-Be workflows, reworking the affected tasks. The model of the new resource is a hybrid parallel process with an initial buffer. The paper analyses several scenarios on parameters such as throughput, resource occupation, and annual man-hours gained. The simulation model was validated against actual historical data and compared to theoretical projections on the impact of automation. From our results, we found the available equipment has a high impact. Using production data, we project an increase in the analyst availability of 4,7% and equipment availability of 1,2%

A Middleware Platform for Intelligent Automation: An Industrial Prototype Implementation

The development of dynamic data-based Decision Support Systems (DSSs) along with the increasing availability of data in the industry, makes real-time data acquisition and management a challenge. Intelligent automation appears as a holistic combination of automation with analytics and decisions made by artificial intelligence, delivering smart manufacturing and mass customization while improving resource efficiency. However, challenges towards the development of intelligent automation architectures include the lack of interoperability between systems, complex data preparation steps, and the inability to deal with both high-frequency and high-volume data in a timely fashion. This paper contributes to industrial frameworks focused on the development of standardized system architectures for Industry 4.0, closing the gap between generic architectures and physical realizations. It proposes a platform for intelligent automation relying on a gateway or middleware between field devices, enterprise databases, and DSSs in real-time scenarios. This is achieved by providing the middleware interoperability, determinism, and automatic data structuring over an industrial communication infrastructure such as the OPC UA Standard over Time Sensitive Networks (TSN). Cloud services and database warehousing used to address some of the challenges are handled using fog computing and a multi-workload database. This paper presents an implementation of the platform in the pharmaceutical industry, providing interoperability and real-time reaction capability to changes to an industrial prototype using dynamic scheduling algorithms

Digital Twin of a Flexible Manufacturing System for Solutions Preparation

In the last few decades, there has been a growing necessity for systems that handle market changes and personalized customer needs with near mass production efficiency, defined as the new mass customization paradigm. The Industry 5.0 vision further enhances the human-centricity aspect, in the necessity for manufacturing systems to cooperate with workers, taking advantage of their problem-solving capabilities, creativity, and expertise of the manufacturing process. A solution is to develop a flexible manufacturing system capable of handling different customer requests and real-time decisions from operators. This paper tackles these aspects by proposing a digital twin of a robotic system for solution preparation capable of making real-time scheduling decisions and forecasts using a simulation model while allowing human interventions. A discrete event simulation model was used to forecast possible system improvements. The simulation handles real-time scheduling considering the possibility of adding identical parallel machines. Results show that processing multiple jobs simultaneously with more than one machine on critical processes, increasing the robot speed, and using heuristics that emphasize the shortest transportation time can reduce the overall completion time by 82%. The simulation model has an animated visualization window for a deeper understanding of the system

Solving Dynamic Delivery Services Using Ant Colony Optimization

This article presents a model for courier services designed to guide a fleet of vehicles over a dynamic set of requests. Motivation for this problem comes from a real-world scenario in an ever-changing environment, where the time to solve such optimization problem is constrained instead of endlessly searching for the optimal solution. First, a hybrid method combining Ant Colony Optimization with Local Search is proposed, which is used to solve a given static instance. Then, a framework to handle and adapt to dynamic changes over time is defined. A new method pairing nearest neighbourhood search with subtractive clustering is proposed to improve initial solutions and accelerate the convergence of the optimization algorithm. Overall, the proposed strategy presents good results for the dynamic environment and is suitable to be applied on real-world scenarios