Mapping value clusters of additive manufacturing on design strategies to support part identification and selection

Abstract

Purpose Additive manufacturing (AM) allows companies to create additional value in the processes of new product development and order fulfillment. One of the challenges for engineers is to identify suitable parts and applications for additive manufacturing. The purpose of this paper is to investigate the relation between value creation and the design process. The implications of this relation provide an orientation on the methods for identifying parts and applications for additive manufacturing. Design/methodology/approach Mapping the value clusters of AM on design strategies allows determining the expected degree of change in design. A classification into major and minor design changes is introduced to describe the predictability of the impact of AM on past performance and business model. The ability to predict the future properties of an AM part determines the suitability of identification and selection methods from literature. The mapping is validated by an identification process that creates a shortlist of potential AM parts based on the strategic decision for a value cluster. Shortlisted parts are then evaluated based on the criteria technology readiness, required post-processing, customer benefit and manufacturer benefit. Findings The mapping of value clusters on expected design changes determines the type of selection process. For minor design changes, automated part identification serves as a powerful tool while major design changes require the judgment of skilled engineers. Research limitations/implications The mapping of value clusters to design strategies and degree of change in design is based on empirical observations and conclusions. The mapping has been validated in an industrial context in different identification and selection processes. Nevertheless the versatility of AM and industrial environments impede a universal validity of high-level concepts. Practical implications This value-driven process of identification and selection was applied in technology transfer projects and proved to be useful for AM novices and experts. The mapping supports the identification and selection process, as well as the general product development process by providing an indication of the design effort for implementing AM. Originality/value The novel mapping links the economic domain of value creation to the engineering domain of design strategies to provide guidance in the selection of economically and technically suitable parts for additive manufacturing.

Method for Function-Based Identification of Potential AM Components in Conventional Product Architectures

“…In this case, indicators are primarily geometrical characteristics that are found in parts which have already been successfully converted to AM. Klahn et al propose a method which combines AM potentials with design strategies (minor or major changes) to identify and select the right parts for specific AM applications (Klahn et al 2020). Parts with minor design changes are identified in an automatized process and parts with major design changes are identified manually based on the experience and judgement of AM experts.…”

“…A growing number of tools and identification methods are found in literature that deal with this issue. Many of these methods focus on a geometry-oriented part identification (Lindemann et al 2015;Klahn et al 2014;Knofius et al 2016;Klahn et al 2020;Leutenecker-Twelsiek 2019a), while only few studies have addressed the possibility to identify potential part candidates based on their functions (Richter 2020;Reichwein et al 2021). This study proposes a combined function and component-oriented heuristic approach to restructure a conventional product architecture towards AM and thereby identify potential AM candidates.…”

Preliminary study of knowledge transfer aspect in the creation of a framework for education and training of digital manufacturing technologies

“…new product development and order fulfillment processes. [1] An estimation of potential of AM can be gathered by considering the potential of laser cutting, which is one of the most widely used modern manufacturing technologies, as in [2] and [3] With a market value of around four billion euros and an annual growth rate of approximately 10%, laser cutting has been a significant technology for over four decades. However, the emergence of AM and 3D printing promises radical advancements in the production of three-dimensional parts, mirroring the transformative impact of laser cutting on flat sheet products.…”

A view similarity-based shape complexity metric to guide part selection for additive manufacturing

“…They can be divided into opportunistic and restrictive methods of DfAM. For example, Klahn et al (2020) mapped the expected end benefits to the changes in design, and an automatic part identification tool was proposed in their work for minor revisions in designs that are incomprehensible by a skilled practitioner in design. The method proposed by Lindemann et al (2015) uses 34 questions along with part properties like material and geometric dimensions for the part selection.…”

“…The measurement of geometry-based shape complexity is essential for improving the adoption of AM technology in industries to redesign opportunistic DfAM (Brennan et al , 2021; Klahn et al , 2020) and advancing one of the core premises of AM, that is, the decoupling cost of manufacturing a part from the complexity of its shape (Alsulami et al , 2020). The existing metric-based part selection method do not accurately capture the geometry of the part for assessing the shape complexity of the AM.…”