A Software Architecture to Control Service-Oriented Manufacturing Systems

Gaiardelli, Sebastiano; Spellini, Stefano; Panato, Marco; Lora, Michele; Fummi, Franco

doi:10.23919/date54114.2022.9774522

Cited by 14 publications

(3 citation statements)

References 6 publications

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“…The information gathered from the MES and the machinery is used to continuously make decisions about production adapting it to the current status of the system. It is a refinement and an extension of the Automation Manager software module we originally presented in [26]. The top layer is the MES connector: a driver interfacing the AMC with the upper layers of the automation pyramid.…”

Section: B Adaptive Manufacturing Controller (Amc)mentioning

confidence: 99%

Enabling Service-Oriented Manufacturing Through Architectures, Models, and Protocols

Gaiardelli,

Spellini,

Panato

et al. 2024

IEEE Access

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Modern production lines are often composed of machinery from different vendors that must be connected with each other to increase the overall interoperability and flexibility. A common problem that arises in such systems is the complexity of the configuration task: they usually require each component to be manually configured. Thus, machinery requires different configuration strategies, negatively impacting scalability and increasing the chance of human errors. Furthermore, each time the manufacturing system has to be updated, the entire procedure must be repeated. This paper proposes a software architecture abstracting the complexity of existing production lines by enabling the service-oriented manufacturing paradigm. Then, it presents a strategy to model manufacturing systems, covering the topology of the production plant, machinery, and production recipes. The paper also proposes a model-based methodology to automatically configure the reference software architecture and hence the machines in the system. The application of the contributions to a fully-fledged production line shows the effectiveness of relying on model-based automatic configuration.

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Section: B Adaptive Manufacturing Controller (Amc)mentioning

confidence: 99%

Enabling Service-Oriented Manufacturing Through Architectures, Models, and Protocols

Gaiardelli,

Spellini,

Panato

et al. 2024

IEEE Access

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“…We consider the set of instances described in Table 2, and we run them on an actual production line built in our lab (described in Section 2). The production line is governed by service-oriented manufacturing (SOM) software architecture similar to the one presented in [33,34], which automatically manages the production line, interacting with both the manufacturing execution system (MES) and the machines. On top, we have developed a module that implements our proposed scheduling heuristic, and the execution is forwarded to the SOM architecture.…”

Section: Real-world Experimentsmentioning

confidence: 99%

Dynamic Job and Conveyor-Based Transport Joint Scheduling in Flexible Manufacturing Systems

Gaiardelli,

Carra,

Spellini

et al. 2024

Applied Sciences

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Efficiently managing resource utilization is critical in manufacturing systems to optimize production efficiency, especially in dynamic environments where jobs continually enter the system and machine breakdowns are potential occurrences. In fully automated environments, co-ordinating the transport system with other resources is paramount for smooth operations. Despite extensive research exploring the impact of job characteristics, such as fixed or variable task-processing times and job arrival rates, the role of the transport system has been relatively underexplored. This paper specifically addresses the utilization of a conveyor belt as the primary mode of transportation among a set of production machines. In this configuration, no input or output buffers exist at the machines, and the transport times are contingent on machine availability. In order to tackle this challenge, we introduce a randomized heuristic approach designed to swiftly identify a near-optimal joint schedule for job processing and transfer. Our solution has undergone testing on both state-of-the-art benchmarks and real-world instances, showcasing its ability to accurately predict the overall processing time of a production line. With respect to our previous work, we specifically consider the case of the arrival of a dynamic job, which requires a different design approach since there is a need to keep track of partially processed jobs, jobs that are waiting, and newly arrived jobs. We adopt a total rescheduling strategy and, in order to show its performance, we consider a clairvoyant scheduling approach, in which job arrivals are known in advance. We show that the total rescheduling strategy yields a scheduling solution that is close to optimal.

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“…A manufacturing service is a minimal manufacturing operation executed from a specific piece of equipment (e.g., the pick and place operation carried out by a robotic arm). Each service is exposed to other pieces of software (e.g., supervisory control) through a communication infrastructure and a compatible software architecture [3]. To specify the production process, a recipe can be represented as an ordered composition of manufacturing services [4].…”

mentioning

confidence: 99%

RRPDG: A Graph Model to Enable AI-Based Production Reconfiguration and Optimization

Gaiardelli,

Lora,

Spellini

et al. 2024

IEEE Trans. Ind. Inf.

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This article introduces the regionalized resource process dependence graphs (RRPDGs): a manufacturing processes representation inspired by the regionalized value state dependence graphs traditionally used in software compilers. An RRPDG is an ordered sequence of nodes, each characterized by stereotyped input and output parameters, encapsulating a transformation of the process state (e.g., a manufacturing operation). RRPDG allow defining complex transformations by composing a set of nodes (i.e., regions), hiding the internal details. Then, RRPDGs are used to automatically reasoning over dynamic reconfiguration and process optimization: an instance of the Astar search algorithm is used to search for possible transformations while pursuing an optimization function. The rules defined in this article over RRPDG models enforce the transformations' correctness. We use RRPDGs to model a real production system while the transformation rules are applied to optimize the system's processes. The proposed representation reduced the search complexity in each experiment, allowing to reach an optimal solution also in the case for which classical approaches were unable to complete before reaching the timeout. In all the experiments, the cost of the solution produced by using the regionalized representation is minor than the the solution produced by using the classical representation.

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A Software Architecture to Control Service-Oriented Manufacturing Systems

Cited by 14 publications

References 6 publications

Enabling Service-Oriented Manufacturing Through Architectures, Models, and Protocols

Enabling Service-Oriented Manufacturing Through Architectures, Models, and Protocols

Dynamic Job and Conveyor-Based Transport Joint Scheduling in Flexible Manufacturing Systems

RRPDG: A Graph Model to Enable AI-Based Production Reconfiguration and Optimization

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