The need for more flexibility and energy efficiency in production systems motivated the development of balanced manufacturing (BaMa), a novel method for holistic optimization of operation strategies in the field of production engineering. It allows quick integration into existing production plants with hardly any requirements for additional hardware. We present a real-world application scenario from a semiconductor production plant. Black-box simulation models are created from monitoring data and technical specification documents and used to derive optimal operation strategies. Furthermore, machine learning approaches are applied for data preprocessing to improve model prediction quality and therefore increase optimization potential. We evaluate the method for the presented scenario, where potential savings due to increased energy efficiency are expected to amount to 15-40%.

Recently, the manufacturing industry became aware of distributed ledger technologies, a protocol that, amongst other things, allows trustless transactions between machines. In this paper, we investigate whether an M2M economy would be feasible within the IOTA network, a popular cryptocurrency for IoT. We build and present a simple industrial lot-size one production system involving three agents that cooperate to create an artistic painting. Payments between agents and users are autonomously executed via IOTA. Amongst other points of criticism, we found that an M2M economy would benefit from the support of smart-contracts and conclude that IOTA is not a fitting solution.

Motivated by two industrial use cases that involve detecting events of interest in (asynchronous) time series from sensors in manufacturing rigs and gas turbines, we design an expressive rule language DslD equipped with interval aggregate functions (such as weighted average over a time interval), Allen’s interval relations and various metric constructs. We demonstrate how to model events in the uses cases in terms of DslD programs. We show that answering DslD queries in our use cases can be reduced to evaluating SQL queries. Our experiments with the use cases, carried out on the Apache Spark system, show that such SQL queries scale well on large real-world datasets. 2012 ACM Subject Classification Computing methodologies → Ontology engineering; Computing methodologies → Temporal reasoning; Theory of computation → Modal and temporal logics

Time-series based simulations of industrial processes are instrumental to optimizing a variety of industrial settings. In this paper, we describe a use case, developed together with Infineon Technologies Austria AG. Monitoring data stored in relational databases was used to build process models of industrial chillers. Optimization algorithms were then applied to find optimal strategies for operating the chillers. Even though the results from this approach were convincing, the access to the necessary data was a labor-intensive and error-prone task. Therefore, in this paper, we investigate how Semantic Web technologies can help to improve data access for time-series data and under which circumstances they would be helpful for the domain experts performing the simulation.

Time series data from machining process monitoring promises to be a rich resource for optimization applications. Limited data access, however restricts the number of potential applications significantly. Semantic technologies such as ontology based data access could help overcoming those restrictions and therefore pave the way for a wider use of state of the art data analysis applications. Semantic web technologies are not yet widely applied in the manufacturing domain which partly has to do with the fact that in the past no relevant use cases where presented in this area. Therefore, in this paper, semantic technologies and their potential applications are illustrated using an existing research database.

Time-series based simulations of industrial processes are instrumental to optimizing a variety of industrial settings. In this paper, we describe a use case, developed together with Infineon Technologies Austria AG. Monitoring data stored in relational databases was used to build process models of industrial chillers. Optimization algorithms were then applied to find optimal strategies for operating the chillers. Even though the results from this approach were convincing, the access to the necessary data was a labor-intensive and error-prone task. Therefore, in this paper, we investigate how Semantic Web technologies can help to improve data access for time-series data and under which circumstances they would be helpful for the domain experts performing the simulation.

The identification of loose manufacturing utilities (e.g. tools, fixtures) and their current condition are essential for machining operations. In this paper a Service Oriented Architecture (SOA) is described, which is applied on manufacturing utilities equipped with sensors and actuators. The suggested SOA not only facilitates interoperability but also can expose semantic data models using OPC UA. Moreover, locally computed information about the utility can be subscribed by relevant communication partners e.g. the CNC. To demonstrate possible applications, a show case tombstone was developed which exposes its state and formal description as well as its current thermal deformation.

In the challenge of achieving environmental sustainability, industrial production plants, as large contributors to the overall energy demand of a country, are prime candidates for applying energy efficiency measures. A modelling approach using cubes is used to decompose a production facility into manageable modules. All aspects of the facility are considered, classified into the building, energy system, production and logistics. This approach leads to specific challenges for building performance simulations since all parts of the facility are highly interconnected. To meet this challenge, models for the building, thermal zones, energy converters and energy grids are presented and the interfaces to the production and logistics equipment are illustrated. The advantages and limitations of the chosen approach are discussed. In an example implementation, the feasibility of the approach and models is shown. Different scenarios are simulated to highlight the models and the results are compared.

Demand Response can be seen as one effective way to harmonize demand and supply in order to achieve high self-coverage of energy consumption by means of renewable energy sources. This paper presents two different simulation-based concepts to integrate demand-response strategies into energy management systems in the customer domain of the Smart Grid. The first approach is a Model Predictive Control of the heating and cooling system of a low-energy office building. The second concept aims at industrial Demand Side Management by integrating energy use optimization into industrial automation systems. Both approaches are targeted at day-ahead planning. Furthermore, insights gained into the implications of the concepts onto the design of the model, simulation and optimization will be discussed. While both approaches share a similar architecture, different modelling and simulation approaches were required by the use cases.

Versatile and economically competitive thermal energy storages are necessary to fulfill the widely differing requirements for storages applied in renewable energy systems, process heat, district heating, power generation and domestic heating. We present the concept of a hybrid sensible-latent heat storage based on an adapted commercial shell-and-tube heat exchanger. The phase change material (PCM) is encapsulated within the tubes and thermal oil serves as sensible heat storage as well as the heat transfer medium. We designed and built a prototype using high density polyethylene (HDPE) as PCM and characterized the storage on a dedicated test rig at ALT. Energy capacities and power profiles are presented for different mass flows and (dis)charging temperatures. Two physical models were developed and implemented using the Modelica language. Dymola was used to simulate the behavior of the prototype storage. Very good agreement was achieved between simulation and experiment. Using the models, we studied the heat transfer within the storage in detail, which enabled us to present how to adapt the storage geometry and PCM properties to cover a broad range of applications. We discuss storage costs and calculate material costs per stored kilowatt-hour for different PCM-thermal oil volume ratios as a function of the tube outer diameter. Finally, we highlight the main advantages and design freedoms of our concept and describe concrete application scenarios in district heating and process heat. (C) 2016 Elsevier Ltd. All rights reserved.