scholarly journals Context-Based Resilience in Cyber-Physical Production System

2021 ◽  
Author(s):  
Ada Bagozi ◽  
Devis Bianchini ◽  
Valeria De Antonellis
Keyword(s):  
Production Systems ◽  
Complex Structure ◽  
High Volume ◽  
Sensor Data ◽  
Shop Floor ◽  
Human In The Loop ◽  
Operator Interface ◽  
Physical Elements ◽  
Cyber Physical Production Systems ◽  
Smart Machine

AbstractCyber-physical systems are hybrid networked cyber and engineered physical elements that record data (e.g. using sensors), analyse them using connected services, influence physical processes and interact with human actors using multi-channel interfaces. Examples of CPS interacting with humans in industrial production environments are the so-called cyber-physical production systems (CPPS), where operators supervise the industrial machines, according to the human-in-the-loop paradigm. In this scenario, research challenges for implementing CPPS resilience, promptly reacting to faults, concern: (i) the complex structure of CPPS, which cannot be addressed as a monolithic system, but as a dynamic ecosystem of single CPS interacting and influencing each other; (ii) the volume, velocity and variety of data (Big Data) on which resilience is based, which call for novel methods and techniques to ensure recovery procedures; (iii) the involvement of human factors in these systems. In this paper, we address the design of resilient cyber-physical production systems (R-CPPS) in digital factories by facing these challenges. Specifically, each component of the R-CPPS is modelled as a smart machine, that is, a cyber-physical system equipped with a set of recovery services, a Sensor Data API used to collect sensor data acquired from the physical side for monitoring the component behaviour, and an operator interface for displaying detected anomalous conditions and notifying necessary recovery actions to on-field operators. A context-based mediator, at shop floor level, is in charge of ensuring resilience by gathering data from the CPPS, selecting the proper recovery actions and invoking corresponding recovery services on the target CPS. Finally, data summarisation and relevance evaluation techniques are used for supporting the identification of anomalous conditions in the presence of high volume and velocity of data collected through the Sensor Data API. The approach is validated in a food industry real case study.

One-of-a-kind Production in Cyber-Physical Production Systems Considering Machine Failures

2021 ◽  
pp. 1-20
Author(s):  
Guido Vinci Carlavan ◽  
Daniel Alejandro Rossit
Keyword(s):  
Industry 4.0 ◽  
Information Technologies ◽  
Job Shop ◽  
Production Systems ◽  
Dispatching Rules ◽  
Shop Floor ◽  
Due Dates ◽  
Efficient Manner ◽  
Large Variability ◽  
Cyber Physical Production Systems

Industry 4.0 proposes the incorporation of information technologies at all levels of the production process. By incorporating these technologies, Industry 4.0 provides new tools for production planning processes, allowing to address problems in an innovative and efficient manner. From these technologies and tools, it is that in this work a One-of-a-Kind Production (OKP) process is approached, where the products tend to be highly customized. OKP implies working with a very large variability within production, demanding very efficient planning systems. For this, a planning model based on CONWIP-type strategies was proposed, which seeks to level the production of a shop floor configured in the form of a job shop. Even more, for having a more realistic shop-floor representation, machine failures have been included in the model. In turn, different dispatching rules were proposed to study the performance and analyze the behaviour of the system. From the results obtained, it is observed that, when the production demand is very exigent in relation with the capacity of the system, the dispatching rules that analyze the workload generated by each job tend to perform better. However, when the demand on the capacity of the production system is less intense, the rules associated with due dates are the ones that obtain the best results.

Productivity Increase through Industrialization of Overhead in Cyber-Physical Production Systems

2013 ◽  
Vol 769 ◽  
pp. 359-366
Author(s):  
Till Potente ◽  
Thomas Jasinski ◽  
Bartholomaeus Wolff
Keyword(s):  
Business Processes ◽  
Industrial Revolution ◽  
Production Systems ◽  
Labour Productivity ◽  
Smart Devices ◽  
Shop Floor ◽  
Manufacturing Companies ◽  
Core Element ◽  
Cyber Physical Production Systems ◽  
Management Staff

The importance of knowledge workers and management staff in manufacturing companies is increasing due to a rising complexity within indirect business processes. As a result, current overhead costs account for most of the overall manufacturing costs. Despite this fact manufacturing companies disregard the productivity potentials of their indirect areas and focus predominantly on the optimisation of shop-floor processes.Cyber-physical systems constitute a technological paradigm of the current forth industrial revolution and promise a further push of labour productivity in the upcoming decades. We expand the vision of cyber-physical production systems on business processes and develop a mathematical approach to predict and analyse productivity potentials of management staff in the context of emerging cyber-physical production systems. The core element of the presented model states the collaborative productivity between people, people and smart devices and between these smart devices themselves.

scholarly journals A Deep Learning Model for Predictive Maintenance in Cyber-Physical Production Systems Using LSTM Autoencoders

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 972
Author(s):  
Xanthi Bampoula ◽  
Georgios Siaterlis ◽  
Nikolaos Nikolakis ◽  
Kosmas Alexopoulos
Keyword(s):  
Deep Learning ◽  
Real World ◽  
Short Term Memory ◽  
Production Systems ◽  
Learning Algorithm ◽  
Remaining Useful Life ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Maintenance Activities ◽  
Cyber Physical Production Systems

Condition monitoring of industrial equipment, combined with machine learning algorithms, may significantly improve maintenance activities on modern cyber-physical production systems. However, data of proper quality and of adequate quantity, modeling both good operational conditions as well as abnormal situations throughout the operational lifecycle, are required. Nevertheless, this is difficult to acquire in a non-destructive approach. In this context, this study investigates an approach to enable a transition from preventive maintenance activities, that are scheduled at predetermined time intervals, into predictive ones. In order to enable such approaches in a cyber-physical production system, a deep learning algorithm is used, allowing for maintenance activities to be planned according to the actual operational status of the machine and not in advance. An autoencoder-based methodology is employed for classifying real-world machine and sensor data, into a set of condition-related labels. Real-world data collected from manufacturing operations are used for training and testing a prototype implementation of Long Short-Term Memory autoencoders for estimating the remaining useful life of the monitored equipment. Finally, the proposed approach is evaluated in a use case related to a steel industry production process.

scholarly journals Advances in Machine Learning Detecting Changeover Processes in Cyber Physical Production Systems

2020 ◽  
Vol 4 (4) ◽  
pp. 108
Author(s):  
Bastian Engelmann ◽  
Simon Schmitt ◽  
Eddi Miller ◽  
Volker Bräutigam ◽  
Jan Schmitt
Keyword(s):  
Machine Learning ◽  
Production Control ◽  
Production Systems ◽  
Heterogeneous Data ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Medium Size ◽  
Data Set ◽  
Set Up ◽  
Cyber Physical Production Systems

The performance indicator, Overall Equipment Effectiveness (OEE), is one of the most important ones for production control, as it merges information of equipment usage, process yield, and product quality. The determination of the OEE is oftentimes not transparent in companies, due to the heterogeneous data sources and manual interference. Furthermore, there is a difference in present guidelines to calculate the OEE. Due to a big amount of sensor data in Cyber Physical Production Systems, Machine Learning methods can be used in order to detect several elements of the OEE by a trained model. Changeover time is one crucial aspect influencing the OEE, as it adds no value to the product. Furthermore, changeover processes are fulfilled manually and vary from worker to worker. They always have their own procedure to conduct a changeover of a machine for a new product or production lot. Hence, the changeover time as well as the process itself vary. Thus, a new Machine Learning based concept for identification and characterization of machine set-up actions is presented. Here, the issue to be dealt with is the necessity of human and machine interaction to fulfill the entire machine set-up process. Because of this, the paper shows the use case in a real production scenario of a small to medium size company (SME), the derived data set, promising Machine Learning algorithms, as well as the results of the implemented Machine Learning model to classify machine set-up actions.

Intelligente Werkstattfertigung*/Intelligent job shop manufacturing - Control mechanisms for job shop manufacturing of series products using the German “Industrie 4.0” approach

2015 ◽  
Vol 105 (04) ◽  
pp. 184-189
Author(s):  
E. Uhlmann ◽  
B. Schallock ◽  
F. Otto
Keyword(s):  
Production Control ◽  
Job Shop ◽  
Production Systems ◽  
Industrie 4.0 ◽  
Innovative Technology ◽  
Shop Floor ◽  
Control Mechanisms ◽  
Utilisation Rate ◽  
Cyber Physical Production Systems ◽  
And Control

Die „intelligente selbstorganisierende Werkstattproduktion“ (iWePro) folgt dem Konzept einer dezentralisierten Produktionssteuerung. Erstmalig wird die Anwendung der Selbstorganisation auf die Serienproduktion von Automobilkomponenten untersucht, die momentan nach Lean-Prinzipien für große Stückzahlen verkettet aufgebaut ist. Zukünftig soll mit dem Werkstattprinzip schwankenden Auslastungen entgegengewirkt werden. Die Fertigungssteuerung für die dadurch wahlfrei zugreifbaren Produktionsmaschinen lässt sich konventionell kaum, wohl aber mit Zukunftskonzepten und Industrie 4.0-Technologien umsetzen.   “Intelligent self-organizing shop floor production” (iWePro) uses the concept of decentralized production control solutions. For the first time, a concept of self-organization is applied to the production of car components, which are currently a moving line according to traditional lean production large batch principles. In the future, the traditional shop floor structure of disconnected machines should guarantee a higher utilisation rate but needs innovative technology and control mechanisms for cyber-physical production systems (CPPS).

scholarly journals Research Fields and Challenges to implement Cyber-Physical Production Systems in SMEs: A Literature Review

2021 ◽  
Vol 20 (2) ◽  
Author(s):  
Rafael Rojas ◽  
Erwin Rauch ◽  
Dominik T. Matt
Keyword(s):  
Literature Review ◽  
Production Systems ◽  
Smart Manufacturing ◽  
Shop Floor ◽  
Future Research ◽  
System Architectures ◽  
Main Research ◽  
Research Fields ◽  
Research Activities ◽  
Cyber Physical Production Systems

Cyber-Physical Production Systems (CPPS) consists of the orchestration of single intelligent and connected cyber-physical systems (CPS) in order to perform what we call smart manufacturing. CPS collaborate in an intelligent way in order to obtain and maintain the optimum of the manufacturing process, handle disturbances and adapt to changing conditions. It might not be easy for small and medium-sized enterprises (SMEs) to implement such production system architectures in their shop floor. In this paper, we want to investigate existing scientific literature through a systematic literature review in order to identify the main research fields for implementing CPPS in smart SME factories. As a result, the identified research fields are critically discussed, highlighting those fields that can be identified as the most difficult challenges for SMEs in the near future and giving directions for future research activities.

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