The fourth industrial revolution introduces changes in traditional manufacturing systems and creates basis for a lot-size-one production. The complexity of production processes is significantly increased, alongside the need to enable efficient process simulation, execution, monitoring, real-time decision making and control. The main goal of our research is to define a methodological approach and a software solution in which the Model-Driven (MD) principles and Domain-Specific Modeling Languages (DSMLs) are used to create a framework for the formal description and automatic execution of production processes. In that way production process models are used as central artifacts to manage the production. In this paper, we analyze production process modeling domain and present a DSML which can be used to create production process models suitable for automatic generation of executable code. The generated code is used for automatic execution of production processes within a simulation or a shop floor. The language can be used to specify errors that may occur during the process execution and to specify error handling and corrective steps, too. The DSML is evaluated by different groups of users and the evaluation results are presented. Both the DSML and the accompanying modeling tool are still in the prototype phase, as they are created and evaluated in use cases covering just the assembly of goods. To enable wider application of the language and the tool, it is required to have additional use cases from different manufacturing domains.

Multi-level production process modeling language

Owing to the different consumption habits of people, the market has rapidly changed, and customized demands have increased in recent years. This issue has led to the need for manufacturing industry to improve their production-process automation system control to become faster and more flexible. An intelligent automated production-line control system (IAPLCS) is proposed in this study, in which a graphic control system (GCS) is designed to integrate equipment communications in automated production. This system runs complex packaged programs for function blocks, which enable users to easily build function blocks in GCS. Users can plan the control flow of the system to control the machine operation. Not only is a web interface set in the IAPLCS, which makes it easier for users to operate and monitor, but also Internet-of-Things (IoT) sensors can be added for data collection and analysis based on the characteristics of the production line and equipment. Finally, the proposed control system was implemented in an actual production line to verify its feasibility.

Design and Implementation of Intelligent Automated Production-Line Control System

One of the goals of Industry 4.0 is to enable mass customization of products and to satisfy specific needs of customers. This goal is often hard to achieve in traditional manufacturing systems. To enable fast production changes, an automatic and flexible production is needed. In this context we propose a Model-Driven Software Development (MDSD) approach and a Domain-Specific Modeling Language (DSML) to model production processes. The language supports two levels of abstraction. A Master-Level (ML) model is used by a process designer to model process steps. A Detail-Level (DL) model is used by Orchestrator, a cluster of industrial computers that manages production, to fill existing ML models with a specification of production logistic and smart resources. A code generator is used to generate machine-readable or human-readable instructions from DL models. Generated code is used for automatic execution of production processes within a simulation or a shop floor. In this paper we provide an application of a DSML, which is capable of modeling production processes that are ready for automatic code generation.

An Application of a DSML in Industry 4.0 Production Processes.

Traditionally, enterprise models have been used for representing knowledge on all aspects of an organization. This aided not only in composing a holistic picture of the different layers of an enterprise in terms of its business model, products and services, business processes and IT architecture, but also for describing the inter-dependencies between the layers. Depending on the degree of formalization, algorithms may be applied to the models, e.g. for simulations. With the upcoming of low-code approaches in software engineering, we regard in this position paper how similar concepts may be integrated in enterprise engineering. In particular we regard augmented enterprise models as interfaces to digital systems and illustrate this view with approaches for semantic technologies, data analytics and blockchain platforms. It is envisaged that such approaches will aid domain experts in integrating digital technologies in their daily work practices.

Towards Augmented Enterprise Models as Low-Code Interfaces to Digital Systems

One of the main challenges of Industry 4.0 is the adaptation of existing production lines. Robots are substituting human workers in modern smart factories, as they are much more suitable for repetitive tasks. In contrast to that, Industry 4.0 predicts a high rise in product customization. The total disruption of the current factories, although the easiest solution, is not welcomed by the traditional industry stakeholders. To offer adaptation rather than disruption, and to promote man-machine collaboration rather than complete substitution of the human workforce, we present a Digital Factory solution capable of orchestrating different types of resources —humans, machines, robots— according to their capabilities. The core component of the solution is a real-time Orchestrator that orchestrates factory resources in order to produce the desired product. Orchestrator is a complex, modular, highly scalable, and pluggable software, responsible for dynamical matching, scheduling, and executing of production steps, allowing high customization and lot-size-one production.

Towards a Non-disruptive System for Dynamic Orchestration of the Shop Floor

Industry 4.0 enforces digital revolution in the manufacturing industry by introducing smart devices and knowledge bases to form intelligent manufacturing information systems. One of the goals of the digital revolution is to support an automatic production from product specification to allow faster reaction on the product changes and a lot size one production. In order to make this possible, a formal language to describe production processes is needed, together with a code generator for its models and an engine to execute the code on smart devices. In this paper we propose a research on Industry 4.0 manufacturing using a Model-Driven Software Development (MDSD) approach to model production processes, use the models to generate instructions to smart devices and human workers, and gather a feedback from them during the process execution.

Towards a formal description and automatic execution of production processes

/pdf/towards-a-formal-specification-of-production-processes-3otr0ur6ew.pdf

Milan Pisarić

Papers

Multi-level production process modeling language

An Application of a DSML in Industry 4.0 Production Processes.

Towards a formal description and automatic execution of production processes

Towards a Non-disruptive System for Dynamic Orchestration of the Shop Floor

Towards a Formal Specification of Production Processes Suitable for Automatic Execution