Sebastian C. Brandt

A Process Data Warehouse for Tracing and Reuse of Engineering Design Processes

The design and development processes of complex technical systems are of crucial importance to the competitiveness of an enterprise. These processes are characterized by high creativity and strong non-deterministic dynamics. Traditional information science methods, however, are intended for more deterministic work processes. They cannot be effectively applied to support creative activities like conceptual synthesis, analysis, and decision making. Therefore methods of experience management need to be exploited here. This paper presents a new integrated approach to such design process guidance based on capturing the process traces in a Process Data Warehouse (PDW). Both the products to be designed and the process steps corresponding are structured and stored as extended method traces. This trace capture facilitates the processing and subsequent reuse of the information through a process-integrated development environment. The concept of the PDW has been evaluated in an engineering design case study which focuses on the phases of conceptual design and basic engineering in designing a chemical production plant.

Ontology-Based information management in design processes

Abstract Engineering design processes are highly creative and knowledge-intensive tasks that involve extensive information exchange and communication among diverse developers. In such dynamic settings, traditional information management systems fail to provide adequate support due to their inflexible data structures and hard-wired usage procedures, as well as their restricted ability to integrate processes and product information. In this paper, we advocate the idea of Process Data Warehousing as a means to provide an information management and integration platform for such design processes. The key idea behind our approach is a flexible ontology-based schema with formally defined semantics that enables the capture and reuse of design knowledge, supported by advanced computer science methods.

IDENTIFICATION AND REUSE OF EXPERIENCE KNOWLEDGE IN CONTINUOUS PRODUCTION PROCESSES

Abstract Extrusion of rubber profiles is a time- and resource-intensive process. The experience of individuals supervising the production is difficult to gather and to convey to others. The aim of the MErKoFer project is to gather accumulated knowledge on the production process and established procedures, which are employed in handling occurring malfunctions and ensuring specified quality standards, and to give advisory feedback based on this knowledge. This approach is based on case-based reasoning, an ontological representation of domain-specific information and dependencies, associations and rules identified by data mining techniques, and neural networks. Solutions of similar situations are provided to the machine operators as hints from the knowledge base to support their decisions and improve their autonomy and the quality of their actions.

Media-assisted product and process traceability in supply chain engineering

Many engineering domains involve an intricate interplay of conceptual synthesis of alternative requirements and design configurations, preliminary impact analysis of these alternatives using complex simulations and multimedia visualizations, and human decision-making. Design traceability in such settings must be both product-oriented and process-oriented: it must enable an efficient media-based comparison of product alternatives from the current project or related experiences, and it must facilitate reuse of modeling experiences to avoid unnecessary repetition of negative experiences. We have studied these problems in a large interdisciplinary project whose aim it is to optimize supply chains linking chemical engineering, plastics engineering, and related application demands e.g. in the automotive industry. A number of novel experience reuse tools have been designed and implemented as part of a process-integrated modeling environment.

PROCESS DATA WAREHOUSE MODELS FOR COOPERATIVE ENGINEERING PROCESSES

Abstract The design and development processes of complex technical systems are important to the competitiveness of enterprises. These processes are characterised by high creativity and strong non-deterministic dynamics spanning different disciplines, relying on cooperation of different organisations. To successfully support these cooperative activities, methods of experience management are needed. This paper describes the capturing of process and product traces in an ontology-based Process Data Warehouse (PDW). These traces can be processed and reused in further development and cooperation cycles. The concept of the PDW has been evaluated in an engineering case study in the domains of chemical and plastics engineering.

Goal-Oriented Information Flow Management in Development Processes

The research of the IMPROVE subproject C1 Goal-Oriented Information Flow Management in Development Processes aims at the development and evaluation of database-driven methods and tools to support and optimize the distributed storage and routing of information flows in cooperative design processes. The overall concept of a Process Data Warehouse (PDW) has been followed which collects, and selectively transforms and enriches required information from the engineering process. The PDW has been conceptually based on interrelated partial domain and integration models which are represented and applied inside a metadata repository. This allows to query and apply experience information based on semantic relationships and dependencies. Special attention has been paid to aspects of cross-organizational cooperation.

Process integration and media-assisted traceability in cross-organisational engineering

Many engineering domains involve an intricate interplay of conceptual synthesis of alternative requirements and design configurations, preliminary impact analysis of these alternatives using complex simulations and multimedia visualisations and human decision-making. Design traceability in such settings must be both product-oriented and process-oriented: it must enable an efficient media-based comparison of product alternatives from the current project or related experiences, and it must facilitate the reuse of modelling experiences to avoid unnecessary repetition of costly experiences. We have studied these problems in a large interdisciplinary project. Its aim is to optimise cross-organisational engineering tasks linking chemical engineering, plastics engineering and related application demands, for example, in the automotive industry. The solution presented in this paper integrates ideas from scenario-based modelling, process integration and ontology-based management of multimedia artefacts.

Management of Experience Knowledge in Continuous Production Processes

Abstract The extrusion of rubber profiles, as used for automotive production, is a highly complex continuous production process which is strongly influenced by variability in input materials and other external conditions. The personal experience of the production line operators continues to play an important role here, as analytical models exist only for small parts of such processes, and systems for automatic control can only provide basic functionality. In the MErKoFer project, knowledge about extrusion processes is captured by ontology-based traceability mechanisms for both direct process support of extrusion operators, and for process analysis and improvement based on the integrated application of data mining techniques. The knowledge accumulated this way assists in ensuring defined quality standards and in handling production faults efficiently and effectively. The approach was experimentally implemented and evaluated at the industrial partners site, and some generalizable parts of the environment were taken up by the software house partner in their aiXPerience software environment for process automation and process information systems.

Management and Reuse of Experience Knowledge in Extrusion Processes

Extrusion of rubber profiles, e.g., for the automotive industry, is a highly complex continuous production process which is nevertheless influenced strongly by variability in input materials and other external conditions. As analytical models exist only for small parts of such processes, experience continues to play an important role here, very similar to the situation in the early phases of process engineering studied in CRC IMPROVE. This section therefore describes a transfer research project called MErKoFer conducted jointly with an industrial application partner and a software house founded by former CRC members. n nIn MErKoFer, results from the CRC projects on direct process support (B1, see Sect. 3.1), process data warehousing (C1, Sect. 4.1), and plastics engineering (A3, see Sect. 5.4) were applied and extended. Specifically, knowledge about extrusion processes is captured by ontology-based traceability mechanisms for both direct process support of extrusion operators, and for process analysis and improvement based on an integration of data mining techniques. The accumulated knowledge assists in ensuring defined quality standards and in handling production faults efficiently and effectively. The approach was experimentally implemented and evaluated in the industrial partners site, and some generalizable parts of the environment were taken up by the software house partner in their aiXPerience software environment for process automation and process information systems.