No Code AI: Automatic generation of Function Block Diagrams from documentation and associated heuristic for context-aware ML algorithm training

TL;DR

This paper introduces a method for automatically generating Function Block Diagrams from requirement documents, enabling no-code industrial process programming with a heuristic that incorporates machine learning recommendations.

Contribution

It presents a novel approach combining constrained selection algorithms and recommender systems to generate executable FBD programs from documentation.

Findings

Generated FBDs are viable for industrial process design

The method effectively translates documentation into executable code

Recommender systems enhance the accuracy of FBD generation

Abstract

Industrial process engineering and PLC program development have traditionally favored Function Block Diagram (FBD) programming over classical imperative style programming like the object oriented and functional programming paradigms. The increasing momentum in the adoption and trial of ideas now classified as 'No Code' or 'Low Code' alongside the mainstream success of statistical learning theory or the so-called machine learning is redefining the way in which we structure programs for the digital machine to execute. A principal focus of 'No Code' is deriving executable programs directly from a set of requirement documents or any other documentation that defines consumer or customer expectation. We present a method for generating Function Block Diagram (FBD) programs as either the intermediate or final artifact that can be executed by a target system from a set of requirement documents using a constrained selection algorithm that draws from the top line of an associated recommender system. The results presented demonstrate that this type of No-code generative model is a viable option for industrial process design.