Julian R. Eichhoff

Collaborative Cloud Printing Service

We develop a small and lightweight cloud based service for the utilization of 3D printer resources enabling users to collaborate on models. Users can collaborate by sharing model files, discussions on aspects of the printing process or using 3D printers as shared resources. This service consists of user, artefact and printer management building on existing web technology. It enables scheduling of printing jobs for artefacts and high utilization of 3D printer resources. This cloud based manufacturing (CBM) system enables 3D printers that are non-native networked to be used remotely by providing easily installable low cost networked computers or installable services. It focuses on the interface between the physical resources and their representation in software to form a cyber physical system (CPS). This service requires smart 3D printers and representation of technical capabilities of physical resources. This work is a research platform for smart machinery or the enhancement of machinery for smart control under the paradigm of Industry 4.0. We discuss the design and concept of this work in progress service and the distinctions from similar systems. Furthermore, the sharing requirements and capabilities of such a service are discussed with a focus on the data integrity and safety for sharing data among users.

Designing the Same, but in Different Ways: Determinism in Graph-Rewriting Systems for Function-Based Design Synthesis

This paper compares methods for identifying determinism within graph-rewriting systems. From the viewpoint of functional decomposition, these methods can be implemented to search efficiently for distinct function structures. An additional requirement is imposed on this comparison that stems from a cooperative design application where different organizations contribute to a distributed graph-rewriting system: Inspecting the definitions of production rules is not allowed for identifying determinism because production rules are considered to be confidential corporate knowledge. Under this assumption, two approaches were selected and empirically compared with respect to random search and guided search scenarios. The results suggest that the herein proposed dynamic rule independence analysis outperforms traditional approaches in light of the above restriction.

A survey on automating configuration and parameterization in evolutionary design exploration

Abstract Configuration and parameterization of optimization frameworks for the computational support of design exploration can become an exclusive barrier for the adoption of such systems by engineers. This work addresses the problem of defining the elements that constitute a multiple-objective design optimization problem, that is, design variables, constants, objective functions, and constraint functions. In light of this, contributions are reviewed from the field of evolutionary design optimization with respect to their concrete implementation for design exploration. Machine learning and natural language processing are supposed to facilitate feasible approaches to the support of configuration and parameterization. Hence, the authors further review promising machine learning and natural language processing methods for automatic knowledge elicitation and formalization with respect to their implementation for evolutionary design optimization. These methods come from the fields of product attribute extraction, clustering of design solutions, relationship discovery, computation of objective functions, metamodeling, and design pattern extraction.

Inducing production rules to extend existing design grammars: The parse/derive method

ABSTRACTGraph-rewriting is a promising computation model for computer-aided design (CAD) applications that operate on graph-based design models. Graph-rewriting-based CAD systems rely on predefined production rules. These rules encode the set of possible actions that may be taken within the design process to make changes to the current design. This paper presents a method for automatically inducing new production rules from existing sample designs. The methods applicability is illustrated in context of conceptual spacecraft design. Results gained from experiments, where existing rules were deliberately left out and had to be rediscovered, show that the induced rules are often similar or identical to the original rules.

Facilitating Design Automation in Multi-organization Concurrent Engineering: Insights from Graph-Rewriting Theory

The aim of this paper is to introduce emerging technologies for the implementation of graph-rewriting-based design automation applications that can be used in collaborative environments. The paper motivates the use of graph-rewriting for design automation and highlights an important issue: preservation of confidentiality. Approaches to the efficient derivation (using design knowledge) and rule induction (learning design knowledge) are discussed. The crucial feature of these approaches for confidentiality-preserving graph-rewriting is that knowledge-bases of contributing organizations do not have to be disclosed.