The Open Platform Communications Unified Architecture (OPC UA) has received attention as a standard for data interoperability in industries. In particular, OPC UA extends its applicability across various industrial sectors by publishing OPC UA companion specifications created in collaboration with other industrial consortiums. However, OPC UA is limited to ensure the interoperability of data analytics models because the relevant companion specifications have not been developed yet. OPC UA should be extended to implement such model interoperability so that machines seamlessly use and share data analytics models across the layers of manufacturing systems to predict and optimize their performance autonomously and collaboratively in terms of interoperable manufacturing intelligence. The present work proposes an OPC UA-compliant interface for the exchange of Predictive Model Markup Language (PMML), a domain-independent standard for representing XML-based data analytics models. This paper includes the design of mapping rules and OPC UA information models for the exchange between PMML and OPC UA, as well as the implementation of an OPC UA server-client prototype to publish and subscribe to OPC UA-compliant regression and neural network models that have been transformed from PMML-based models.

The present work proposes a holonic-based mechanism for self-learning factories based on a hybrid learning approach. The self-learning factory is a manufacturing system that gains predictive capability by machine self-learning, and thus automatically anticipates the performance results during the process planning phase through learning from past experience. The system mechanism, including a modeling method, architecture, and operational procedure, is structured to agentize machines and manufacturing objects under the paradigm of Holonic Manufacturing Systems. This mechanism allows machines and manufacturing objects to acquire their data and model interconnection and to perform model-driven autonomous and collaborative behaviors. The hybrid learning approach is designed to obtain predictive modeling ability in both data-existent and even data-absent environments via accommodating machine learning (which extracts knowledge from data) and transfer learning (which extracts knowledge from existing knowledge). The present work also implements a prototype system to demonstrate automatic predictive modeling and autonomous process planning for energy reduction in milling processes. The prototype generates energy-predictive models via hybrid learning and seeks the minimum energy-using machine tool through the contract net protocol combined with energy prediction. As a result, the prototype could achieve a reduction of 9.70% with respect to energy consumption as compared with the maximum energy-using machine tool.

This article reviews the state of the art of prediction and optimization for sequence-driven scheduling in job shop flexible manufacturing systems (JS-FMSs). The objectives of the article are to (1) analyze the literature related to algorithms for sequencing and scheduling, considering domain, method, objective, sequence type, and uncertainty; and to (2) examine current challenges and future directions to promote the feasibility and usability of the relevant research. Current challenges are summarized as follows: less consideration of uncertainty factors causes a gap between the reality and the derived schedules; the use of stationary dispatching rules is limited to reflect the dynamics and flexibility; production-level scheduling is restricted to increase responsiveness owing to product-level uncertainty; and optimization is more focused, while prediction is used mostly for verification and validation, although prediction-then-optimization is the standard stream in data analytics. In future research, the degree of uncertainty should be quantified and modeled explicitly; both holistic and granular algorithms should be considered; product sequences should be incorporated; and sequence learning should be applied to implement the prediction-then-optimization stream. This would enable us to derive data-learned prediction and optimization models that output accurate and precise schedules; foresee individual product locations; and respond rapidly to dynamic and frequent changes in JS-FMSs.