Neuro-symbolic Empowered Denoising Diffusion Probabilistic Models for Real-time Anomaly Detection in Industry 4.0

Industry 4.0 involves the integration of digital technologies, such as IoT, Big Data, and AI, into manufacturing and industrial processes to increase efficiency and productivity. As these technologies become more interconnected and interdependent, Industry 4.0 systems become more complex, which brings the difficulty of identifying and stopping anomalies that may cause disturbances in the manufacturing process. This paper aims to propose a diffusion-based model for real-time anomaly prediction in Industry 4.0 processes. Using a neuro-symbolic approach, we integrate industrial ontologies in the model, thereby adding formal knowledge on smart manufacturing. Finally, we propose a simple yet effective way of distilling diffusion models through Random Fourier Features for deployment on an embedded system for direct integration into the manufacturing process. To the best of our knowledge, this approach has never been explored before.Comment: Accepted at the 26th Forum on specification and Design Languages (FDL 2023

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

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

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

Robotic Arm Dataset (RoAD): a Dataset to Support the Design and Test of Machine Learning-driven Anomaly Detection in a Production Line

The early detection of anomalous behaviors from a production line is a fundamental aspect of Industry 4.0, facilitated by the collection of massive amounts of data enabled by the Industrial Internet of Things. Nonetheless, the design and validation of anomaly detection algorithms, mostly based on sophisticated Machine Learning models, heavily rely on the availability of annotated datasets of realistic anomalies, which is very difficult to obtain in a real production line. To address this problem, we introduce the Robotic Arm Dataset (RoAD), specifically designed to support the development and validation of Multivariate Time Series Anomaly Detection (MTSAD) algorithms. We collect and annotate a large number of data and metadata to characterize the motion and energy consumption of a collaborative robotic arm in a full-fledged production line and annotate a comprehensive set of healthy as well as realistic anomalies scenarios. To prove the significance of RoAD and encourage future developments, we benchmark several state-of-the-art anomaly detection algorithms on our newly introduced dataset, and we freely release it to the scientific community

Robotic Arm Dataset (RoAD): A Dataset to Support the Design and Test of Machine Learning-Driven Anomaly Detection in a Production Line

The early detection of anomalous behaviors from a production line is a fundamental aspect of Industry 4.0, facilitated by the collection of massive amounts of data enabled by the Industrial Internet of Things. Nonetheless, the design and validation of anomaly detection algorithms, mostly based on sophisticated Machine Learning models, heavily rely on the availability of annotated datasets of realistic anomalies, which is very difficult to obtain in a real production line. To address this problem, we introduce the Robotic Arm Dataset (RoAD), specifically designed to support the development and validation of Multivariate Time Series Anomaly Detection (MTSAD) algorithms. We collect and annotate a large number of data and metadata to characterize the motion and energy consumption of a collaborative robotic arm in a full-fledged production line and annotate a comprehensive set of healthy as well as realistic anomalies scenarios. To prove the significance of RoAD and encourage future developments, we benchmark several state-of-the-art anomaly detection algorithms on our newly introduced dataset, and we freely release it to the scientific community

SMARTIC: Smart Monitoring and Production Optimization for Zero-waste Semiconductor Manufacturing

The Industry 4.0 revolution introduced decentralized, self-organizing, and self-learning systems for production control. New machine learning algorithms are getting increasingly powerful to solve real-world problems, like predictive maintenance and anomaly detection. However, many data-driven applications are still far from being optimized to cover many aspects and the complexity of modern industries; correlations between smart monitoring, production scheduling, and anomaly detection/predictive maintenance have only been partially exploited. This paper proposes to develop new data-driven approaches for smart monitoring and production optimization, targeting semiconductor manufacturing, one of the most technologically advanced and data-intensive industrial sectors, where process quality, control, and simulation tools are critical for decreasing costs and increasing yield. The goal is to reduce defect generation at the electronic component level and its propagation to the system- and system-of-systems- level by working on (1) enhanced anomaly detection, based on the human-in-the-loop concept and on advanced treatment of multiple time-series and of domain adaptation, (2) smart and predictive maintenance based on both objective data traces and simulated ones, to mitigate the risk of degrading product quality, and (3) the construction of an extended manufacturing software stack that allows anomaly- and maintenance-aware policies to enhance production line scheduling and optimization