Cyber - physical systems & industry 4.0
Real-Time Locating System in Production Management
Real-time monitoring and optimization of production and logistics processes significantlyimprove the efficiency of production systems. Advanced production management solutions requirereal-time information about the status of products, production, and resources. As real-time locatingsystems (also referred to as indoor positioning systems) can enrich the available information, thesesystems started to gain attention in industrial environments in recent years. This paper providesa review of the possible technologies and applications related to production control and logistics,quality management, safety, and efficiency monitoring. This work also provides a workflow to clarifythe steps of a typical real-time locating system project, including the cleaning, pre-processing, andanalysis of the data to provide a guideline and reference for research and development of indoorpositioning-based manufacturing solutions.
Integration of real-time locating systems into digital twins
Cyber-physical model-based solutions should rely on digital twins in which simulations are integrated with real-time sensory and manufacturing data. This paper highlights the benefits of information fusion with real-time locating systems (RTLS) and demonstrates how position and acceleration data can be utilised for the simulation-based analysis of product-specific activity times. The proposed digital twin is continuously capable to predict the production status and provide information for monitoring of production performance thanks to the real time connections of the RTLS and adaptive simulation models. The presented industrial case study demonstrates how the resulted Simulation 4.0 concept supports the analysis of human resource effectiveness (HRE) in an assembly process.
Development of manufacturing execution systems in accordance with Industry 4.0 requirements: A review of standard- and ontology-based methodologies and tools
This work presents how recent trends in Industry 4.0 (I4.0) solutions are influencing the development of manufacturing execution systems (MESs) and analyzes what kinds of trends will determine the development of the next generation of these technologies. This systematic and thematic review provides a detailed analysis of I4.0-related requirements in terms of MES functionalities and an overview of MES development methods and standards because these three aspects are essential in developing MESs. The analysis highlights that MESs should interconnect all components of cyber-physical systems in a seamless, secure, and trustworthy manner to enable high-level automated smart solutions and that semantic metadata can provide contextual information to support interoperability and modular development. The observed trends show that formal models and ontologies will play an even more essential role in I4.0 systems as interoperability becomes more of a focus and that the new generation of linkable data sources should be based on semantically enriched information. The presented overview can serve as a guide for engineers interested in the development of MESs as well as for researchers interested in finding worthwhile areas of research.
Fuzzy activity time-based model predictive control of open station assembly lines
The sequencing and line balancing of manual mixed-model assembly lines are challenging tasks due to the complexity and uncertainty of operator activities. The control of cycle time and the sequencing of production can mitigate the losses due to non-optimal line balancing in the case of open-station production where the operators can work ahead of schedule and try to reduce their backlog. The objective of this paper is to provide a cycle time control algorithm that can improve the e ciency of assembly lines in such situations based on a specially mixed sequencing strategy. To handle the uncertainty of activity times, a fuzzy model-based solution has been developed. As the production process is modular, the fuzzy sets represent the uncertainty of the elementary activity times related to the processing of the modules. The optimistic and pessimistic estimates of the completion of activity times extracted from the fuzzy model are incorporated into a model predictive control algorithm to ensure the constrained optimization of the cycle time. The applicability of the proposed method is demonstrated based on a wire-harness manufacturing process with a paced conveyor, but the proposed algorithm can handle continuous conveyors as well. The results confirm that the application of the proposed algorithm is widely applicable in cases where a production line of a supply chain is not well balanced and the activity times are uncertain.
A Review of Semantic Sensor Technologies in Internet of Things Architectures
Intelligent sensors should be seamlessly, securely, and trustworthy interconnected to enable automated high-level smart applications. Semantic metadata can provide contextual information to support the accessibility of these features, making it easier for machines and humans to process the sensory data and achieve interoperability. The unique overview of sensor ontologies according to the semantic needs of the layers of IoT solutions can serve a guideline of engineers and researchers interested in the development of intelligent sensor-based solutions. The explored trends show that ontologies will play an even more essential role in interlinked IoT systems as interoperability and the generation of controlled linkable data sources should be based on semantically enriched sensory data.
Enabling Technologies for Operator 4.0: A Survey
The fast development of smart sensors and wearable devices has provided the opportunity to develop intelligent operator workspaces. The resultant Human-Cyber-Physical Systems (H-CPS) integrate the operators into flexible and multi-purpose manufacturing processes. The primary enabling factor of the resultant Operator 4.0 paradigm is the integration of advanced sensor and actuator technologies and communications solutions. This work provides an extensive overview of these technologies and highlights that the design of future workplaces should be based on the concept of intelligent space.
Industrial Internet of Things based Cycle Time Control of Assembly Lines
Dynamic cycle time setting and line balancing are the most significant problems in modular manufacturing. Industry 4.0 and IIoT (Industrial Internet of Things) based production management systems connect decentralized production units and information sources to increase productivity and flexibility. We developed an IIoT based solution to ensure a real-time connection between products and assembly lines. The proposed dynamic cycle time setting algorithm takes into account the varying complexity of the product based on the real-time information provided by smart wireless sensors and an Indoor Positioning System (IPS). In this paper, we overview Industry 4.0 based assembly line management solutions, present the developed IIoT based infrastructure, and demonstrate the applicability of the proposed cycle time setting algorithm in a simulation example motivated by an industrial open station conveyor.
RFID-based Task Time Analysis for Shop Floor Optimization
According to the concept of Industry 4.0, shop floor control and optimization should be more and more autonomous and integrated. In the age of digital transformation, human operators are still applied in manufacturing processes, so the uncertainty of their tasks times cannot be ignored during scheduling and line balancing. To provide accurate and realtimeinformationabouttheoperators,weproposeanRFID-based task time analysis system. We demonstrate that the empirical density distribution functions of the tasks times can be convolved to generate stochastic model-based optimal solutions of simple assembly line balancing (SALPB) and bin-packing (BP) based scheduling problems.
Multilayer Network-Based Production Flow Analysis
A multilayer network model for the exploratory analysis of production technologies is proposed. To represent the relationship between products, parts, machines, resources, operators, and skills, standardized production and product-relevant data are transformed into a set of bi- and multipartite networks. This representation is beneficial in production flow analysis (PFA) that is used to identify improvement opportunities by grouping similar groups of products, components, and machines. It is demonstrated that the goal-oriented mapping and modularity-based clustering of multilayer networks can serve as a readily applicable and interpretable decision support tool for PFA, and the analysis of the degrees and correlations of a node can identify critically important skills and resources. The applicability of the proposed methodology is demonstrated by a well-documented benchmark problem of a wire-harness production process. The results confirm that the proposed multilayer network can support the standardized integration of production-relevant data and exploratory analysis of strongly interconnected production systems.
Scalable co-Clustering using a Crossing Minimization ‒ Application to Production Flow Analysis
Dynamic cycle time setting and line balancing are the most significant problems in modular manufacturing. Industry 4.0 and IIoT (Industrial Internet of Things) based production management systems connect decentralized production units and information sources to increase productivity and flexibility. We developed an IIoT based solution to ensure a real-time connection between products and assembly lines. The proposed dynamic cycle time setting algorithm takes into account the varying complexity of the product based on the real-time information provided by smart wireless sensors and an Indoor Positioning System (IPS). In this paper, we overview Industry 4.0 based assembly line management solutions, present the developed IIoT based infrastructure, and demonstrate the applicability of the proposed cycle time setting algorithm in a simulation example motivated by an industrial open station conveyor.
