Reprint of: Gentelligent processes in biologically inspired manufacturing

Denkena, Berend; Dittrich, Marc-André; Stamm, Siebo; Wichmann, Marcel; Wilmsmeier, Sören

doi:10.1016/j.cirpj.2021.06.006

Cited by 3 publications

(1 citation statement)

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“…The answer is that DNA-type information is more important than BN-type information because superior biological functions, such as flexibility, autonomy, self-formation, and self-recovery, are expressed by DNA-type information rather than BN-type information (see [17,18,22] for more details). The remarkable thing is that an integrated approach called "gentelligent" manufacturing systems has been developed to gain benefit from DNA-type information and BN-type information [26,27].…”

Section: Introductionmentioning

confidence: 99%

Biologicalization of Smart Manufacturing Using DNA-Based Computing

Ura,

Zaman

2023

Biomimetics

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Smart manufacturing needs cognitive computing methods to make the relevant systems more intelligent and autonomous. In this respect, bio-inspired cognitive computing methods (i.e., biologicalization) can play a vital role. This article is written from this perspective. In particular, this article provides a general overview of the bio-inspired computing method called DNA-Based Computing (DBC), including its theory and applications. The main theme of DBC is the central dogma of molecular biology (once information of DNA/RNA has got into a protein, it cannot get out again), i.e., DNA to RNA (sequences of four types of nucleotides) and DNA/RNA to protein (sequence of twenty types of amino acids) are allowed, but not the reverse ones. Thus, DBC transfers few-element information (DNA/RAN-like) to many-element information (protein-like). This characteristic of DBC can help to solve cognitive problems (e.g., pattern recognition). DBC can take many forms; this article elucidates two main forms, denoted as DBC-1 and DBC-2. Using arbitrary numerical examples, we demonstrate that DBC-1 can solve various cognitive problems, e.g., “similarity indexing between seemingly different but inherently identical objects” and “recognizing regions of an image separated by a complex boundary.” In addition, using an arbitrary numerical example, we demonstrate that DBC-2 can solve the following cognitive problem: “pattern recognition when the relevant information is insufficient.” The remarkable thing is that smart manufacturing-based systems (e.g., digital twins and big data analytics) must solve the abovementioned problems to make the manufacturing enablers (e.g., machine tools and monitoring systems) more self-reliant and autonomous. Consequently, DBC can improve the cognitive problem-solving ability of smart manufacturing-relevant systems and enrich their biologicalization.

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