Additive Manufacturing

Additive manufacturing technologies, such as 3D printing, have reached a stage of performance for industrial application such as small series and spare parts. The adoption of additive manufacturing has so far mostly been investigated from the perspective of individual manufacturing firms. This paper focuses on the identification of overarching influence factors. In a category system, influence factors are analyzed from the perspectives of the supply and demand side, the supply chain actors and flows as well as sustainability, thus contributing to the adoption from a supply chain perspective.

Additive Manufacturing (AM), also commonly called 3D-Printing, is very recent technology. Numerous innovations have improved their capabilities in the last few years. These improvements combined with ambitious promises made by 3D-Printing companies have led to some disregard of the physical and economical limitations of these technologies. As impressive as the opportunities especially in light weight construction may be, the technical, physical and economical restrictions have to be considered. This article focuses on the premises and restraints as well as the opportunities of AM-technology.

Current advances result in increasingly complex production programs. Through combination of additive manufacturing and Industry 4.0, new elements can be formed and - as a whole - enable to economically manufacture the above mentioned programs. The Bionic Smart Factory 4.0 provides a framework, structuring them in terms of relation and interaction. Their development and implementation is being promoted through their evaluation against the determinants of complex production programs.

Metal-cutting manufacturing companies constantly demand for highly efficient process layouts which can particularly be achieved by the utilization of application optimized special tools. Conventional methods for the manufacturing of cutting tools are subject to restrictions, particularly with respect to the inner and outer shape design. At this point, additive manufacturing offers a substantial innovation potential. Through the buildup by layers, design limits of conventional methods are repealed and the production of complex and individual structures is feasible. Against the background of these process-specific potentialities, the iWFT and associated research and industry partners are developing a process chain for additive manufacturing of tungsten carbide cutting tools in the framework of the joint research project PraeziGen.

Architectural design is traditionally limited by the availability of fabrication tools and increasing loan costs. Especially in concrete industries this has a great effect due to the intensive need of manual craftsmanship at the production of formwork. By introducing additive fabrication processes and methods of Industry 4.0 as advanced inspection procedures new design limits can be discovered. To produce large scale concrete elements with high surface qualities and high accuracy at joints in short time hybrid fabrication processes can be a solution. At TU Braunschweig a new generative method is investigated where concrete is sprayed on an adaptive formwork and graded surfaces can be generated.

The proliferation of technological innovations in additive manufacturing is accompanied by an increasing awareness of sustainability. In order to achieve an adequate investment methodical support is needed. A set of indicators for sustainable production represents the performance of a technology. Since an evaluation of technologies is subjective individual preferences of decision makers have to be taken into account. Hence, the multiple-criteria decision analysis methodology PROMETHEE is applicable in this context.

The LearningGripper from Festo looks like an abstract form of the human hand. The four fingers of the compliant gripper are driven by 12 pneumatic bellows actuators with low-level pressurisation. Thanks to the process of machine learning, it is able to teach itself to carry out complex actions such as, for example, gripping and positioning an object. By means of the LearningGripper we demonstrate how the development of such complex systems will be accelerated in the production of the future. Furthermore, the specific usage of machine learning algorithms will increase the efficiency of whole production plants.

The additive production processes are increasingly developed from Rapid Prototyping to Additive Manufacturing (AM), which provides an outstanding technological and economic potential for a variety of industries. Particularly in the area of varied small series production these technologies offer significant advantages in terms of reducing component weight, the integration of additional functions and the production of complex geometries or individual components. Due to the juvenileness of the technology there is a lack of knowledge concerning the technology itself, its possibilities and application potentials in many companies. In addition the costs are often regarded as a critical success factor for the widespread use of the technology. In particular the smart use of AM will affect the positive impact on the future economic use of a product throughout its life cycle.