Adapter for Self-Learning Production Systems

Cândido, Gonçalo; Orio, Giovanni Di; Barata, José; Scholze, Sebastian

doi:10.1007/978-3-642-28255-3_19

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…Suitable multiagent manufacturing shopfloor control systems exist, with CoBASA (Barata 2005) being the basis on which this work was developed. CoBASA has been augmented over the last few years, adding more autonomy at the module level and including features for a variety of self-* properties, including self-adaptivity, self-diagnosis and self-learning (Barata et al 2010;Candido et al 2012;Frei 2010;Ribeiro et al 2008).…”

Section: Discussionmentioning

confidence: 99%

Self-organising assembly systems formally specified in Maude

Frei

Şerbănuţă

Serugendo

2012

J Ambient Intell Human Comput

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This article recapitulates on the research done in Self-Organising Assembly Systems (SOAS) and presents the completed formal specifications and their simulation in Maude. SOAS are assembly systems that (1) participate in their own design by spontaneously organising themselves in the shop floor layout in response to the arrival of a product order and (2) manage themselves during production. The self-organising process for SOAS to design themselves follows the Chemical Abstract Machine (CHAM) paradigm: industrial robots self-select and self-arrange according to specific chemical rules in response to a product order with generic assembly instructions (GAP). This article presents an additional set of rules describing how the GAP is transformed into Layout-Specific Assembly Instructions (LSAI), which is a kind of recipe for how the self-organising robots assemble the product.

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Section: Discussionmentioning

confidence: 99%

Self-organising assembly systems formally specified in Maude

Frei

Şerbănuţă

Serugendo

2012

J Ambient Intell Human Comput

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“…These efforts resulted in a methodology and platform that is assessed and validated in all application scenarios at these manufacturing companies. However, in this paper only one application scenario is exposed, more details about the other application scenarios and the related business case can be found in [19].…”

Section: Application Scenariosmentioning

confidence: 99%

Self-Learning Production Systems (SLPS) — Energy management application for machine tools

Cândido

Orio

Barata

et al. 2013

2013 IEEE International Symposium on Industrial Electronics

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In an increasingly globalised environment, modern manufacturing companies struggle to improve their integrated monitoring and control solutions with respect to total cost of ownership by reducing down-times during production, improving system performances and throughput and enabling for a faster fault detection as well as reducing energy consumption.The research currently done under the scope of Self-Learning Production Systems (SLPS) tries to fill this gap by providing an innovative and integrated approach for developing more intelligent monitoring and control solutions. This paper introduces the research background and describes the generic SLPS architecture with the main focus on the Adapter component, which represents the entity responsible for adapting the system behaviour based on the available contextual information. The envisioned Adapter architecture as well as the generic Adaptation Process are introduced.The proposed solution was then employed to manage the energy consumed by machine tools in CNC machines, taking into account machine idle times patterns. Both the objectives and approach of this application scenario are presented, as well as the Adapter role whenever the system needs to be adapted to improve the manufacturing line sustainability.

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“…A detailed description about the other application scenarios and the related business case can be found in [14].…”

Section: Application Scenariosmentioning

confidence: 99%

Energy Efficiency in Machine Tools - A Self-Learning Approach

Orio¹,

Candido

Barata

et al. 2013

2013 IEEE International Conference on Systems, Man, and Cybernetics

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Due to the growing demand to reduce the environmental impact, the manufacturing companies of today are encouraged to adopt new green methodologies, strategies and technologies for increasing the energy efficiency of their manufacturing production lines. These solutions have a great impact on several productivity metrics including availability and costs. The continuous pursuit of productivity and particularly of machine availability has led to an increase of the total energy consumption in production plants. However, productivity gains can also be achieved by reducing the life-cycle costs of the manufacturing production systems. The research currently done under the scope of Self-Learning Production Systems (SLPS) tries to fill the gap between availability and efficiency by providing an innovative and integrated approach for ensuring the efficient utilization of the resources in machine tools.

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Adapter for Self-Learning Production Systems

Cited by 3 publications

References 4 publications

Self-organising assembly systems formally specified in Maude

Self-organising assembly systems formally specified in Maude

Self-Learning Production Systems (SLPS) — Energy management application for machine tools

Energy Efficiency in Machine Tools - A Self-Learning Approach

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Adapter for Self-Learning Production Systems

Cited by 3 publications

References 4 publications

Self-organising assembly systems formally specified in Maude

Self-organising assembly systems formally specified in Maude

Self-Learning Production Systems (SLPS) &#x2014; Energy management application for machine tools

Energy Efficiency in Machine Tools - A Self-Learning Approach

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Self-Learning Production Systems (SLPS) — Energy management application for machine tools