A Model-based Approach for Designing Cyber-Physical Production Systems

The most recent development trend related to manufacturing is called "Industry 4.0". It proposes to transition from "blind" mechatronics systems to Cyber-Physical Production Systems (CPPSs). Such systems are capable of communicating with each other, acquiring and transmitting real-time production data. Their management and control require a structured software architecture, which is tipically referred to as the "Automation Pyramid". The design of both the software architecture and the components (i.e., the CPPSs) is a complex task, where the complexity is induced by the heterogeneity of the required functionalities. In such a context, the target of this thesis is to propose a model-based framework for the analysis and the design of production lines, compliant with the Industry 4.0 paradigm. In particular, this framework exploits the Systems Modeling Language (SysML) as a unified representation for the different viewpoints of a manufacturing system. At the components level, the structural and behavioral diagrams provided by SysML are used to produce a set of logical propositions about the system and components under design. Such an approach is specifically tailored towards constructing Assume-Guarantee contracts. By exploiting reactive synthesis techniques, contracts are used to prototype portions of components' behaviors and to verify whether implementations are consistent with the requirements. At the software level, the framework proposes a particular architecture based on the concept of "service". Such an architecture facilitates the reconfiguration of components and integrates an advanced scheduling technique, taking advantage of the production recipe SysML model. The proposed framework has been built coupled with the construction of the ICE Laboratory, a research facility consisting of a full-fledged production line. Such an approach has been adopted to construct models of the laboratory, to virtual prototype parts of the system and to manage the physical system through the proposed software architecture

Enabling Component Reuse in Model-based System Engineering of Cyber-Physical Production Systems

Manufacturing lines are evolving into complex cyber-physical production systems. However, their growth in complexity is not matched by the development of structured modeling and design methodologies. In particular, approaches exploiting both models typical of the manufacturing domain and models used by computer engineers are still missing. In this work, we outline a design flow contemplating the reuse of already existing manufacturing lines' models, while designing novel advanced production systems. To enable such a flow, we propose a methodology extracting System Modeling Language (SysML) structural diagrams from AutomationML descriptions. Then, we propose to design the system functionalities on top of the produced diagrams. The paper shows the application of the methodology to a concrete manufacturing line, the structure of which was originally modeled using AutomationML. To exemplify the advantages of the methodology, we exploit the models being generated to automatically extract a digital twin for the production system transportation line. The resulting digital twin is compliant with a well-known plant simulation tool

Modeling in Industry 5.0: What Is There and What Is Missing: Special Session 1: Languages for Industry 5.0

The Industry 4.0 trend speeds up the adoption of a variety of technologies. In modern manufacturing, system data are collected both from the field through sensors and by exploiting complex simulations. Data analysis techniques became crucial to build and maintain any efficient production line, while autonomous systems and robots are the main focus of researchers and practitioners. This pervasive use of artificial intelligence derived technologies pushed humans to the border of production systems. Industry 5.0 aims at bringing the attention back to humans in production lines while magnifying their interactions with intelligent systems. This new trend will impact the design of future manufacturing infrastructures, increasing their complexity. Engineers will need modeling and developing tools able to capture this complexity. In this paper, we analyze the modeling languages and tools being used, identifying their strengths and weaknesses. Then, we propose some possible directions to provide engineers with the expressive power needed to tackle the challenges posed by Industry 5.0

Languages and Formalisms to Enable EDA Techniques in the Context of Industry 4.0

This paper analyzes a set of languages and standard used when designing industrial plants. It focuses on AutomationML and B2MML to specify respectively the architecture and the intended production of the system being designed. It also relies on the DIN 8580 standard to describe the actions performed by each machine composing the production line.Then, it outlines a methodology starting by mapping the information expressed by the analyzed languages and standards into the Assume-Guarantee Contracts formalism. It exploits contract-based design concepts to tackle the increase automation of the industrial plant design process and to enable the generation of digital twins. The approach is outlined by showing its applicability to a concrete manufacturing scenario

Compositional Design of Multi-Robot Systems Control Software on ROS

This paper presents a methodology that relies on Assume-Guarantee Contracts to decompose the problem of synthesizing control software for a multi-robot system. Initially, each contract describes either a component (e.g., a robot) or an aspect of the system. Then, the design problem is decomposed into different synthesis and verification sub-problems, allowing to tackle the complexity involved in the design process. The design problem is then recomposed by exploiting the rigorousness provided by contracts. This allows us to achieve system-level simulation capable to be used for validating the entire design. Once validated, the software synthesized during the process can be integrated into Robot Operating System (ROS) nodes and executed using state-of-the-practice packages and tools for modern robotic systems.We apply the methodology to generate a control strategy for an autonomous goods transportation system. Our results show a massive reduction of the time required to obtain automatically the control software implementing a multi-robot mission

On the Impact of Transport Times in Flexible Job Shop Scheduling Problems

Manufacturing systems require a careful scheduling of the resource usage to maximize the production efficiency. In a completely automated environment, the transport system should be orchestrated to work smoothly with the other resources. While the impact of job characteristics, such as fixed or variable processing times of the tasks composing the jobs, or task dependencies, has been extensively studied, the role of the transport system has received less attention.In this paper we consider a conveyor belt as a mean of transportation among a set of production machines. In this scenario, there is no input or output buffer at the machines, and the transport times depend on the availability of the machines. We propose a heuristic based on randomization, called SCHED-T, which is able to find a near optimal joint schedule for job processing and transfer in few seconds. We test our solution on known benchmarks, along with real-world instances, showing that our scheduler is able to predict accurately the overall processing time of a production line

Work-in-Progress: Introducing Assume-Guarantee Contracts for Verifying Robotic Applications

This paper summarizes the first steps toward an automatic framework, relying on Assume-Guarantee Contracts, for the verification of robotics applications. Classic HW and SW design and verification techniques are inadequate for robots due to the involved complexity. In this paper we advocate that contract-based methodologies allow safe problem decomposition easing system-level validation

A Hierarchical Modeling Approach to Improve Scheduling of Manufacturing Processes

Timely response to sudden production events and requirements shifts is a key feature of Industry 4.0. It requires techniques to manipulate and optimize the production processes, and components providing an high degree of reconfigurability. To acknowledge to such demands, this paper presents a multi-level and hierarchical approach to manufacturing processes modeling. Models are structured to represent the production hierarchically: partitioning recipes in a set of tasks, allocated to machines' manufacturing services and expressed as a sequence of elementary actions. Then, we propose a run-time scheduling algorithm able to exploit the novel structure given to knowledge by the proposed modeling approach. The algorithm aims at minimizing the makes pan while maximizing machines utilization. We validate the contributions of this paper on a full-fledged production line. The modeling strategy has been implemented in SysML: a well-known systems modeling language. The experiments show the presented model and the proposed scheduling approach enabling a more precise and more performing control over the manufacturing process

A Software Architecture to Control Service-Oriented Manufacturing Systems

This paper presents a software architecture extending the classical automation pyramid to control and reconfigure flexible, service-oriented manufacturing systems. At the Planning level, the architecture requires a Manufacturing Execution System (MES) consistent with the International Society of Automation (ISA) standard. Then, the Supervisory level is automated by introducing a novel component, called Automation Manager. The new component interacts upward with the MES, and downward with a set of servers providing access to the manufacturing machines. The communication with machines relies on the OPC Unified Architecture (OPC UA) standard protocol, which allows exposing production tasks as “services”. The proposed software architecture has been prototyped to control a real production line, originally controlled by a commercial MES, unable to fully exploit the flexibility provided by the case study manufacturing system. Meanwhile, the proposed architecture is fully exploiting the production line's flexibility

Integrating Smart Contracts in Manufacturing for Automated Assessment of Production Quality

Products and materials traceability is essential in modern manufacturing, where the production must meet certain standards that range from Quality Control (QC) to the quality of the used materials. In this environment, blockchain applications allow certifying data provenience and subsequent modification, offering trust and security along the entire supply chain. Nonetheless, the design and the development of such applications are usually performed manually and, thus, subject to errors.In this paper, we propose a methodology allowing to automatically generate smart contracts starting from a SysML model. This approach allows easing the integration of blockchain applications in a production system: by abstracting the implementations with models, it is possible to generate smart contracts for different blockchains, connecting to multiple production environments.We applied the proposed methodology on a real manufacturing system, assessing the quality of a case-study production