Frank Kübler

RFID Integrated Adaption of Manufacturing Execution Systems for Energy Efficient Production

An emerging challenge for manufacturing companies is to increase the energy efficiency of their manufacturing systems in order to reduce both energy costs and overall environmental impact. Modern manufacturing execution systems must cope with the new requirements of sustainable manufacturing in addition to the conventional focus on production management. This approach shows how the digital network of manufacturing execution systems can be used to eliminate non-value adding energy consumption in the manufacturing system. Therefore, an integrated use of workstation-related information from production schedules as well as the availability of product, operator and manufacturing equipment was developed. In particular, the advantages of the radio frequency identification technology is considered, as a decentralized information source for the manufacturing execution system for product and operator induced idle and stand-by times of production machinery.

Development of a Procedure for Energy Efficiency Evaluation and Improvement of Production Machinery

A major and growing challenge for manufacturing companies is to increase the energy efficiency of their production machinery in order to reduce both their energy costs and their overall environmental impact. Established approaches either focus on optimizing the base line energy consumption of auxiliary production infrastructure, or directly target the optimization of value creating processes like special machine tools. In practice, heterogeneous machinery in operation requires a procedure to prioritize which machine should be improved first. Hence a decision support procedure based on multiple criteria was developed and implemented during a use case. This system combines machine data, production process information and the evaluation of expert knowledge and energy consumption measurements.

Sustainability Assessment in Remanufacturing Companies—Qualitative Approach

In the last three decades the concept of sustainability has gained a growing attention. There are different initiatives around the world which aim to provide methods and tools for sustainability assessment at a company level. The majority of existing quantitative methods require extended scope of data to be collected and analyzed. Small and medium size remanufacturing enterprises lack the know-how and technical resources to apply them. For this reason there is still research gap regarding the qualitative, easy applicable methods for sustainability assessment, which might be applied, based on expert’s knowledge. The aim of this chapter is to present a new qualitative method, which provides cross company valid sustainability assessment criteria. The method allows assessing the level of maturity of resources utilization in a company with regards to the three dimensions of sustainability. The chapter presents the research methodology and the results of the application of the method in the companies.

A Comparison of Neural Network and DOE Regression Analysis for Predicting Resource Consumption of Manufacturing Processes

Artificial neural networks (ANN) as well as Design of Experiments (DOE) based regression analysis (RA) are used for modeling of complex systems. Both methodologies are commonly applied in process and quality control of manufacturing processes. Due to the fact that resource efficiency has become a critical concern for manufacturing companies these models need to be extended to predict resource consumption of manufacturing processes. This chapter describes an approach to use neural networks as well as DOE based regression analysis for predicting resource consumption of manufacturing processes and gives a comparison of the achievable results based on an industrial case study of a turning process.

Energy Efficiency and Productivity Optimization of Industrial Cleaning Equipment

Advanced cleanliness requirements in production are forcing industrial companies to include new cleaning processes into their manufacturing process. Complex cleaning operation procedures can lower process productivity and at the same time are responsible for substantial parts of the overall energy consumption. An optimization of cleaning processes with respect to cleaning duration, energy consumption and efficiency can therefore contribute to cost reduction significantly. This article presents a procedure for real data based assessment of industrial cleaning equipment. Based upon the resulting information of the procedure, productivity ratios and energy consumptions can be determined up to individual cleaning components. This creates the required transparency to derive customized production and energy efficiency optimization measures.