Laser Intensity as a Basis for the Design of Passive Laser Safety Barriers – A Dangerous Approach

AbstractModern laser beam sources provide radiation with high output power and brilliance. Additionally, innovative laser system technology enables the deflection of the laser into every direction. These developments depict new aspects in laser safety. On the one hand, there is no standard design approach for laser safety barriers and, on the other hand, no practical database of resulting protection times is available. A prototype test rig was built up, which allows the determinationof the protection time of different passive safety barriers. By experimental investigations, a process model for single steel sheets was established, which provides a relation between the applied process parameters and the protection time of the safety barrier. Within the conducted investigations, the laser power and the spot diameter were varied, whereas former investigations only considered the total laser intensity. The presented results show the influence of the varied parameters on the protection time and provide a first database, which will be extended within further investigations

A performance measurement system for integrated production and maintenance planning

CITATION: Schreiber, M. et al. 2020. A performance measurement system for integrated production and maintenance planning. Procedia CIRP, 93:1037-1042. doi:10.1016/j.procir.2020.03.041The original publication is available at https://www.sciencedirect.com/journal/procedia-cirpThe increasing amount of production resources to be maintained and efficiency requirements are forcing manufacturing companies to improve production and maintenance effectiveness by a mutual consideration of both functions in an integrated planning process. However, less attention is paid to monitor the performance of manufacturing systems based on key performance indicators to identify the need for measures and adapting plans. Performance Measurement Systems (PMS) are applied to accomplish this task. Therefore, this paper presents an evaluation of existing PMS’s for production and maintenance planning. A new PMS that considers strategic and operational views is presented and validated by an industrial case study.https://www.sciencedirect.com/science/article/pii/S2212827120306375?via%3DihubPublishers versio

Modeling Approach for Situational Event-handling within Production Planning and Control Based on Complex Event Processing

Nowadays industrial production environments are complex, volatile, and driven by uncertainties. Manufacturing companies are striving for flexibility and adaptability to cope with these challenges and remain competitive. Market requirements such as shortened product life cycles, increasing number of variants, and customized products lead to complexity in manufacturing systems. Possible approaches to cope with such challenges can be found in the field of ‘Industrie 4.0’. In particular, decision-making and real-time reaction systems are one way to handle the complexity. To cope with this complexity, digitalization like the vision of ‘Industrie 4.0’ can offer different solutions. However, digitalization leads to an increase of the amount of data describing the status of products and resources within an industrial production environment. In order to achieve a near real time monitoring and control of production and logistics processes, intelligent processing and analyzing of the acquired data is necessary. As a result of this development, so called “complex event processing” (CEP) is essential for analyzing extensive data streams in real-time. In order to derive the rules for a CEP engine, an event model has to be described to visualize the relations, constraints and abstraction levels of production processes. The main focus within this paper is a modeling approach for the situational handling of events within production planning and control. The requirements of the modeling method are focused on the use case of a mass production for carbon-fiber-reinforced plastic CFRP components

Situational handling of events for industrial production environments

Industrial production environments are complex, volatile and driven by uncertainties nowadays. Enterprises are striving for flexibility and adaptability to handle these challenges and remain competitive. Market requirements such as shortened product life cycles, increasing number of variants, and customized products lead to complexity in production systems. To be able to handle this complexity, digitalization like the vision of “Industrie 4.0” can offer different solutions. In such complex production settings, decision-making and real-time reactions to events occurring during production processes are one way to handle the challenges. The approach presented here includes a situational handling of events for a manufacturing environment. The exemplary implementation of the method will be carried out by means of a complex Cyber-Physical Production System (CPPS) at the Fraunhofer Research Institution for Casting, Composite and Processing Technology IGCV in Augsburg and demonstrated using an example of a mass production for CFRP components

Digital Lean Production - An Approach to Identify Potentials for the Migration to a Digitalized Production System in SMEs from a Lean Perspective

In times of rising product variants, individualization and demand for flexibility, manufacturing companies are forced to meet economic target values to stay competitive within volatile markets. In order to accomplish these targets, companies implement the principles of lean production to ensure efficient and productive manufacturing processes. As the lean production philosophy is based on synchronization and standardization, currently it is stretched to its limits due to its lack of required flexibility in manufacturing processes and ability to cope with the complexity caused by variants and individualization. Digitization offers the possibility to extend the ability of lean production systems in order to adapt manufacturing processes with respect to the requirements resulting from challenging markets. This paper demonstrates the state of the art regarding lean production as wells as digitization and presents an approach based on the consistent opinion of reviewed literature, which formulates digitization as the next step of lean management in production systems. This study reveals the demand for a methodological approach in a SME environment that quantifies the profitability of the implementation of digital technologies in lean production systems. Based on this study a model for the identification of relevant technologies is suggested and the scope of further research is derived

Approach for Defining Rules in the Context of Complex Event Processing

The vision of Industrie 4.0 and the Internet of Things (IoT) is based on the connection of smart products and smart machines equipped with sensors and actuators. The digitalization of industrial processes leads to the production of data streams. In this context, real-time analytics is becoming more and more important for business applications as a result of the need to deal with the growth of data and to react instantly to changes in the data streams. Complex event processing (CEP) is an efficient methodology to enable processing and real-time analysis of streams of data. The main focus of CEP is the detection of patterns in data streams. Therefore, a set of rules has to be predefined. These rules are characterized by various parameters. Defining the optimal values for these parameters is challenging. In current CEP systems, experts have to define the rule patterns. In this paper we suggest three ways to define rules: manual by domain experts, semi-automated by rule mining, or optimization. However, not all of these three ways can be applied to a production scenario or use case. Thus, we compare these approaches and match them with the appropriate production scenario

Approach for Risk Identification and Assessment in A Manufacturing System

The growing number of sensors in production systems increases the availability of data for a manufacturing system. This data can be employed to recognize process related, operative risks during the production process more precisely and estimate the risk level of a factory. This renders the possibility to reduce possible risks, like machine breakdown or tool failure, even before their occurrence. We therefore present an approach of risk identification of a production system based on sensor induced events from the shop floor and a possible evaluation scheme of such risks. The described case study demonstrates the feasibility of the approach

Method of usability for mobile robotics in a manufacturing environment

The future demand of customers requires an increasing variability and complexity of the fabrication process. The requirements for flexibility in the fields of logistics and handling can be met by the abilities of mobile robotics. Currently, the presented use of mobile robots is rather confined, although these machines are highly flexible. The main objective of this research paper is the generation of a general schedule of robot skills based on a flow chart. This model serves to evaluate parameters of environment and product characteristics together with robot abilities, the whole being subsequently analysed by a specific algorithm

Approach for an event-driven production control for cyber-physical production systems

When the changing market environment in terms of logistical requirements is considered, production planning and control (PPS) has recently contributed significantly to fulfilling these demands. Cyber-physical production systems (CPPS) with their characteristics of decentralised organisation, real-time capability and smart data processing offer new possibilities for PPS. Consequently, this paper proposes a new event-based approach. Control loops close to the production shop floor enable a rapid identification of events. Based on an activity list, production control is able to react adequately to different events, such as machine disturbances. Finally, the developed approach for event-driven production control is implemented in a simulation