Felix W. Baumann

Influence of slicing tools on quality of 3D printed parts

ABSTRACTWith slicing tools it is possible to convert digital 3D models into printing instructions for 3D printers. The general approach is: The model is cut into horizontal slices which are then used to create extrusion paths similar to milling paths in the traditional CNC field, which are then being filled with material, mostly plastic material.The goal of this study is to compare available slicing tools for 3D printers under defined aspects using different configurations. The main contributions of this study are: (1) collecting methods and tools to judge print results, (2) analysis of the available slicing tools with appropriate tests and comparisons, and (3) evaluating the slicing tools using the analysis as solid foundation.

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.

Additive Manufacturing, Cloud-Based 3D Printing and Associated Services—Overview

Cloud Manufacturing (CM) is the concept of using manufacturing resources in a service-oriented way over the Internet. Recent developments in Additive Manufacturing (AM) are making it possible to utilise resources ad-hoc as replacements for traditional manufacturing resources in case of spontaneous problems in the established manufacturing processes. In order to be of use in these scenarios, the AM resources must adhere to a strict principle of transparency and service composition in adherence to the Cloud Computing (CC) paradigm. With this review, we provide an overview of CM, AM and relevant domains as well as presenting the historical development of scientific research in these fields, from 2002 to 2016. Part of this work is also a meta-review on the domain to further detail its development and structure.

Software-aided measurement of geometrical fidelity for 3D printed objects

ABSTRACTThis research evaluates the geometric fidelity of 3D printed objects by utilizing digital image analysis techniques on consumer grade 3D printers. ISO 527-2 Type B specimen are modeled using Houdini Software, then positioned on the virtual print-bed and sliced in 0.3 mm thickness layers utilizing MiracleGrue slicer. The specimens are printed on a Makerbot Replicator2X 3D printer with an X-Y resolution of 0.011 mm. The test set is divided into three where the first object set (Test set A) is printed with their longest side parallel to the X axis, the second object set (Test set B) with their longest side oriented at an angle to the X axis and the third object set (Test set C) as a control with a different material type and structure. The test includes objects with parallel patterns of 0, 5, 10, 30, 45, 60 and 90 degree resulting in toolpaths along the parallel lines. For the second test set the orientation angle is chosen to match the parallel pattern degree. The objects are analyzed with a softwar...

Industrial Data Sharing with Data Access Policy

In current industrial settings, data is dispersed on numerous devices, systems and locations without integration and sharing capabilities. With this work, we present a framework for the integration of various data sources within an industrial setting, based on a mediating data hub. Within the data hub, data sources and sinks for this industrial application are equipped with data usage policies to restrict and enable usage and consumption of data for shared analytics. We identify such policies, their requirements and rationale. This work addresses an industrial setting, with manufacturing data being the primary use-case. Requirements for these policies are identified from existing use-cases and expert domain knowledge. The requirements are identified as reasonable via examples and exemplary implementation.

Utilising the Tor Network for IoT Addressing and Connectivity

For Internet of Things (IoT) devices and cyber-physical systems (CPS), it is required to connect them securely and reliably to some form of cloud environment or computing entity for control, management and utilisation. The Internet is a suitable, standardized, and proven means for the connection of IoT devices in various scenarios. Connection over the Internet utilises existing protocols, standards, technologies and avoids investment in new, specialised concepts. Thereby, this connection requires a transparent addressing schema which is commonly TCP/IP, using domain names and IP addresses. However, in industrial, commercial and private networks, the addressability and connectability/connectivity is often limited by firewalls, proxies and router configurations utilising NAT. Thus, the present network configurations hinder the establishment of connections between IoT devices across different locations. Therefore, the method for connecting IoT devices in a client-server configuration proposed herein utilises the Tor (previously: The onion router/routing) network for addressing of and secured communication to IoT and CPS devices. It is an overlay protocol that was designed to allow for robust and anonymous communication. The benefit of this approach is to enable addressability and connectivity of IoT devices in firewalled and potentially unknown and changing network environments, thus allowing for IoT devices to be used reliably behind firewalls as long as outgoing communication is not

Manufacturing Smart Services for automotive production lines

The intelligent exploitation of sensor data and order parameters from modern production systems is one of the biggest challenges in the context of Industry4.0. Currently, data from single machines are processed individually and not integrated with upstream or downstream processes. This case is prevalent in automotive assembly lines. Here, numerous machining tools from different vendors prohibit a smooth collaboration. However, only the aggregation of the entirety of available data sources permits a comprehensive and intelligent analysis and optimization of production lines. This approach leads to the regulation and behavior prediction of single components and finally of whole production systems. Such an intelligent assessment can be realized by Smart Services which are self-contained application containers allowing for efficient data analytics in modern production lines. The SePiA.Pro project develops and investigates a self-describing and secure packaging format for Smart Services facilitating their automatic provisioning. The project implements an open, standard- and cloud-based platform consisting of a modelling environment for Smart Services; a repository for the exchange of Smart Services; and a provisioning engine for automated deployment of Smart Services. Said platform opens up modern data analytics capabilities for anyone, as both customers and suppliers of analytics services. Use cases from automotive manufacturing demonstrate the value of the developed solution.

Long term availability of raw experimental data in experimental fracture mechanics

Abstract Experimental data availability is a cornerstone for reproducibility in experimental fracture mechanics, which is crucial to the scientific method. This short communication focuses on the accessibility and long term availability of raw experimental data. The corresponding authors of the eleven most cited papers, related to experimental fracture mechanics, for every year from 2000 up to 2016, were kindly asked about the availability of the raw experimental data associated with each publication. For the 187 e-mails sent: 22.46 % resulted in outdated contact information, 57.75 % of the authors did received our request and did not reply, and 19.79 replied to our request. The availability of data is generally low with only 11 available data sets ( 5.9 %). The authors identified two main issues for the lacking availability of raw experimental data. First, the ability to retrieve data is strongly attached to the possibility to contact the corresponding author. This study suggests that institutional e-mail addresses are insufficient means for obtaining experimental data sets. Second, lack of experimental data is also due that submission and publication does not require to make the raw experimental data available. The following solutions are proposed: (1) Requirement of unique identifiers, like ORCID or ResearcherID, to detach the author(s) from their institutional e-mail address, (2) Provide DOIs, like Zenodo or Dataverse, to make raw experimental data citable, and (3) grant providing organizations should ensure that experimental data by public funded projects is available to the public.

Automating the Provisioning and Integration of Analytics Tools with Data Resources in Industrial Environments Using OpenTOSCA

The fourth industrial revolution is driven by the integration and analysis of a vast amount of diverse data. Thereby, data about production steps, overall manufacturing processes, and also supporting processes is gathered to enable holistic analysis approaches. These approaches promise to provide new insights and knowledge by revealing cost saving possibilities and also automated adjustments of production processes. However, such scenarios typically require analytics services and data integration stacks since algorithms have to be developed, executed and therefore be wired with the data to be processed. This leads to complex setups of overall analytics environments that have to be installed, configured and managed according to the needs of different analysis scenarios and setups. The manual execution of such installations is time-consuming and error-prone. Therefore, we demonstrate how the different components of such combined integration and analytics scenarios can be modelled in order to be reused in different settings, while enabling the fully automated provisioning of overall analytics stacks and services.

From GCode to STL: Reconstruct Models from 3D Printing as a Service

The authors present a method to reverse engineer 3D printer specific machine instructions (GCode) to a point cloud representation and then a STL (Stereolithography) file format. GCode is a machine code that is used for 3D printing among other applications, such as CNC routers. Such code files contain instructions for the 3D printer to move and control its actuator, in case of Fused Deposition Modeling (FDM), the printhead that extrudes semi-molten plastics. The reverse engineering method presented here is based on the digital simulation of the extrusion process of FDM type 3D printing. The reconstructed models and pointclouds do not accommodate for hollow structures, such as holes or cavities. The implementation is performed in Python and relies on open source software and libraries, such as Matplotlib and OpenCV. The reconstruction is performed on the models extrusion boundary and considers mechanical imprecision. The complete reconstruction mechanism is available as a RESTful (Representational State Transfer) Web service.