Designing and implementation of an intelligent manufacturing system

Abstract

Abstract:Purpose: The goal of XPRESS is to establish a breakthrough for the factory of the future with a new flexible production concept based on the generic idea of "specialized intelligent process units" ("Manufactrons") integrated in cross-sectoral learning networks for a customized production. XPRESS meets the challenge to integrate intelligence and flexibility at the "highest" level of the production control system as well as at the "lowest" level of the singular machine.Design/methodology/approach: Architecture of a manufactronic networked factory is presented, making it possible to generate particular manufactrons for the specific tasks, based on the automatic analysis of its required features. Findings:The manufactronic factory concept meets the challenge to integrate intelligence and flexibility at the "highest" level of the production control system as well as at the "lowest" level of the singular machine. The quality assurance system provided a 100% inline quality monitoring, destructive costs reduced 30%-49%, Practical implications:This concept is demonstrated in the automotive and aeronautics industries, but can be easily transferred to nearly all production processes. Using the manufactronic approach, industrial players will be able to anticipate and to respond to rapidly changing consumer needs, producing highquality products in adequate quantities while reducing costs.Originality/value: Assembly units composed of manufactrons can flexibly perform varying types of complex tasks, whereas today this is limited to a few predefined tasks. Additionally, radical innovations of the manufactronic networked factory include the knowledge and responsibility segregation and trans-sectoral process learning in specialist knowledge networks.

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.…”

Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning

“…However, the focus is increasingly shifting towards a combination of reconfigurability, flexibility and even adaptability (ElMaraghy 2006;Almeida 2011). Reconfigurability, flexibility and adaptability reflect the customer demand driven production of today's business environment.…”

“…IMS, in detail explained in Sect. 2.2.2, try expanding that view by expanding the focus on further characteristics like e.g., autonomy, learning and efficiency Oztemel 2010;Almeida 2011 …”