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A prerequisite for the successful adoption of additive manufacturing (AM) technologies in industry is the identification of areas, where such technologies could offer a clear competitive advantage. The purpose of this paper is to investigate the unique value-adding characteristics of AM, define areas of viable application in a firm value chain and discuss common implications of AM adoption for companies and their processes.

The research leverages a multi-case-study approach and considers interviews with AM adopting companies from the Swiss and central European region in the medical and industrial manufacturing industries. The authors rely on a value chain model comprising a new product development process and an order fulfillment process (OFP) to analyze the benefits of AM technologies.

The research identifies and defines seven clusters within a firm value chain, where the application of AM could create benefits for the adopting company and its customers. The authors suggest that understanding the AM process chain and the design experience are key to explaining the heterogeneous industrial maturity of the presented clusters. The authors further examine the suitability of AM technologies with agile development techniques to pursue incremental product launches in hardware. It is clearly a field requiring the attention of scholars.

This paper presents a value-driven approach for use-case identification and reveals implications of the industrial implementation of AM technologies. The resultant clustering model provides guidance to new AM adopters.

Corporates have recently invested in company-owned makerspaces with the goal to skim the potential of makerspaces as innovation driver. The purpose of this paper is to introduce the first framework describing elements and the innovation-related impact on users of corporate makerspaces (CMSs).

The CMS framework is based on a critical review of 116 scientific articles on makerspaces and the embedding of the review findings into the corporate context.

A prototyping infrastructure, a community infrastructure and facilitators are proposed to be key elements of CMSs. Further, CMSs are suggested to have an impact on ideation, concept iteration during the innovation process and collaboration of its users.

The framework on CMSs is based on a critical review of makerspace literature and not on empirical research data.

This paper sheds light on key elements and the expected innovation-related impact of a CMS on the users and thus contains useful information for corporate innovation management on how to plan, build and implement a CMS.

To the best of the authors’ knowledge, this paper is the first review of makerspace literature with focus on their elements and innovation-related impact. Additionally, the review provides the first academic definition of the growing phenomenon of CMSs and describes elements and the innovation-related impact of CMSs on its users in companies, which paves the way for further research on CMSs.

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.

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.

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.

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.

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.

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.

Internal channels produced by selective laser melting (SLM) have rough surfaces that require post-processing. The purpose of this paper is to develop an empirical model for predicting the material removal and surface roughness (SR) of SLM-manufactured channels owing to abrasive flow machining (AFM).

A rheological model was developed to simulate the viscosity and power-law index of an AFM medium. To simulate the pressure distribution and velocity in the SLM channels, the fluid behavior and SR in the channels were simulated by using computational fluid dynamics. The results of this simulation were then applied to create an empirical model that can be used to predict the SR and material removal thickness. To verify this empirical model, it was applied to an actual part fabricated by SLM. The results were compared with the measurements of the SR and channel diameter subsequent to AFM.

The proposed model exhibits maximum deviation between the model and the measurement of −1.1% for the down-skin SR, −0.2% for the up-skin SR and −0.1% for material removal thickness.

The results of this study show that the proposed model can avoid expensive iterative tests to determine whether a given channel design leads to the desired SR after smoothing by AFM. Therefore, this model helps to design an AFM-ready channel geometry.

In this paper, a quantitatively validated AFM model was proposed for complex SLM channels with varying orientation angles.