Automatische Generierung von Fertigungs-Managementsystemen

Flad, Stefan; Weißenberger, Benedikt; Chen, Xinyu; Rösch, Susanne; Voigt, Tobias

doi:10.1007/978-3-662-53248-5_65

Cited by 4 publications

(4 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, the domain-specific language should be defined and related to the specific domain. Flad et al [35] introduced a model-driven concept for MES engineering for the food and beverage industry. It includes three steps: modeling the components in an MES solution, specifying the model information into a software-readable structure, and automatic generation of the MES solution with a generator.…”

Section: Model-driven Engineering Of the Mesmentioning

confidence: 99%

“…In the second use case, the approach is applied to the packaging area on the bottling plant of a brewery to analyze the technical efficiency of a beer bottling line for returnable glass. According to the literature [28,35,36], the prototype of an editor for modeling and transformation and a generator for automatic MES generation has been developed and used in two use cases.…”

Section: Mes Improvementmentioning

confidence: 99%

See 1 more Smart Citation

A model-driven approach for engineering customizable MES with the application to the food and beverage industry

Chen

Nophut

Voigt

2021

Int J Adv Manuf Technol

Self Cite

View full text Add to dashboard Cite

The Manufacturing Execution System (MES) is a process-oriented IT solution collecting and managing information from the shop floor manufacturing processes. Because of the programming and customization effort required for specific production processes, the MES implementation is not widespread in the food and beverage industry, as most food and beverage manufacturers are small- and medium-sized enterprises with limited resources to invest in MES. For engineering the MES that should be customized according to the target production processes, a model-driven approach has been presented in this paper with six phases covering the entire lifecycle of the MES engineering process. By using this approach, MES can be automatically generated and sustainably improved, which has the potential to reduce the complexity of implementation as well as the resources required for the engineering of customizable MES. Based on two use cases in the processing and packaging areas in the food and beverage industry, the feasibility and practicality of the presented approach have been proven.

show abstract

Section: Model-driven Engineering Of the Mesmentioning

confidence: 99%

Section: Mes Improvementmentioning

confidence: 99%

A model-driven approach for engineering customizable MES with the application to the food and beverage industry

Chen

Nophut

Voigt

2021

Int J Adv Manuf Technol

Self Cite

View full text Add to dashboard Cite

show abstract

“…[ 1,12 ] Precise knowledge of its processes is essential to meet the required product quality and optimize the process efficiency. [ 13 ] Furthermore, the German brewing industry, for example, is characterized by SMEs (yearly production <50.000 hl), as they represent approx. 90% of the companies.…”

Section: Introduction and Objectivementioning

confidence: 99%

“…A metamodel is a suitable approach. [ 13 ] It should set the framework to represent production plants holistically in a generic and hybrid way. Consequently, it could be used for all different companies in the beverage industry.…”

Section: Introduction and Objectivementioning

confidence: 99%

A metamodeling approach for the simulation of energy and media demand for the brewing industry

Bär

Voigt

2021

J Adv Manuf & Process

Self Cite

View full text Add to dashboard Cite

Due to economic, social and technical trends, as well as rising energy costs, the food and beverage industry is challenged to work energy‐efficiently. Small and medium‐sized enterprises in particular lack of both time and knowledge to identify and implement suitable energy efficiency measures. With the help of simulation, decision‐makers can pursue numerous approaches and make well‐founded decisions. A metamodel based on four modeling pillars (physical‐, process‐, article/recipe‐, and production plan model) for an entire production system concerning the forecast of energy and media consumption is presented. The metamodel is implemented in a user‐friendly modeling tool, which enables a simple hybrid modeling without prequalification of the user. Subsequently, a standardized data exchange file, as basis for a simulation model, is generated. For validation, various use cases were modeled and the tool was validated using a systematic test. In addition, two simulation studies were performed to show that the presented approach provides the opportunity to create a holistic model in terms of forecasting energy and media consumption.

show abstract

Data Science on Industrial Data—Today’s Challenges in Brown Field Applications

2021

View full text Add to dashboard Cite

Much research is done on data analytics and machine learning for data coming from industrial processes. In practical approaches, one finds many pitfalls restraining the application of these modern technologies especially in brownfield applications. With this paper, we want to show state of the art and what to expect when working with stock machines in the field. The paper is a review of literature found to cover challenges for cyber-physical production systems (CPPS) in brownfield applications. This review is combined with our own personal experience and findings gained while setting up such systems in processing and packaging machines as well as in other areas. A major focus in this paper is on data collection, which tends be more cumbersome than most people might expect. In addition, data quality for machine learning applications is a challenge once leaving the laboratory and its academic data sets. Topics here include missing ground truth or the lack of semantic description of the data. A last challenge covered is IT security and passing data through firewalls to allow for the cyber part in CPPS. However, all of these findings show that potentials of data driven production systems are strongly depending on data collection to build proclaimed new automation systems with more flexibility, improved human–machine interaction and better process-stability and thus less waste during manufacturing.

show abstract

Automatische Generierung von Fertigungs-Managementsystemen

Cited by 4 publications

References 4 publications

A model-driven approach for engineering customizable MES with the application to the food and beverage industry

A model-driven approach for engineering customizable MES with the application to the food and beverage industry

A metamodeling approach for the simulation of energy and media demand for the brewing industry

Data Science on Industrial Data—Today’s Challenges in Brown Field Applications

Contact Info

Product

Resources

About