Autonomic smart manufacturing

Abstract

Smart manufacturing (SM) systems have to optimise manufacturing activities at the machine, unit or entire manufacturing facility level as well as adapting the manufacturing process on the fly as required by a variety of conditions (e.g. machine breakdowns and/or slowdowns) and unexpected variations in demands. This paper provides a framework for autonomic smart manufacturing (ASM) that relies on a variety of models for its support: (1) a process model to represent machines, parst inventories and the flow of parts through machines in a discrete manufacturing floor;(2) a predictive queueing network model to support the analysis and planning phases of ASM; and (3) optimisation models to support the planning phase of ASM. In essence, ASM is an integrated decision support system for smart manufacturing that combines models of different nature in a seamless manner. As shown here, these models can be used to predict manufacturing time and the energy consumed by the manufacturing process, as well as for finding the machine settings that minimise the energy consumed or the manufacturing time subject to a variety of constraints using non-linear optimisation models. The diversity of models used affords different levels of integration and granularity in the decision-making process. An example of a car manufacturing process is used throughout the paper to explain the concepts and models introduced here.

Process modeling for smart factories: using science mapping to understand the strategic themes, main challenges and future trends

“…The analyses of the field of study also show a possible integration between lean production and business processes through the application of value-stream mapping (VSM) to reduce costs and manufacturing times (Smelov et al , 2014). Flexible production also seeks integration into learning networks to customize workflow development (Almeida, 2011) and frameworks to map processes and predict variables such as time, energy consumption and machine usage (Menascé et al , 2015). Seiger et al (2015) propose an object-oriented workflow language to treat heterogeneous processes in smart environments, making it possible to compose processes with several variants.…”