Identification of aftermarket and legacy parts suitable for additive manufacturing : A knowledge management-based approach

A research stream identifying aftermarket and legacy parts suitable for additive manufacturing (AM) has emerged in recent years. However, existing research reveals no golden standard for identifying suitable part candidates for AM and mainly combines preexisting methods that lack conceptual underpinnings. As a result, the identification approaches are not adjusted to organizations and are not completely operationalizable. Our first contribution is to investigate and map the existing literature from the perspective of knowledge management (KM). The second contribution is to develop and empirically investigate a combined part-identification approach in a defense sector case study. The part identification entailed an analytical hierarchy process (AHP), semi-structured interviews, and workshops. In the first run, we screened 35,000 existing aftermarket and legacy parts. Similar to previous research, the approach was not in sync with the organization. However, in contrast to previous research, we infuse part identification with KM theory by developing and testing a “Phase 0” assessment that ensures an operational fit between the approach and the organization. We tested Phase 0 and the knowledge management-based approach in a second run, which is the main contribution of this study. This paper contributes empirical research that moves beyond previous research by demonstrating how to overcome the present challenges of part identification and outlines how knowledge management-based part identification integrates with current operations and supply chains. The paper suggests avenues for future research related to AM; however, it also concerns Industry 4.0, lean improvement, and beyond, particularly from the perspective of KM.© 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).fi=vertaisarvioitu|en=peerReviewed

Techno-economic prospects and desirability of 3D food printing:perspectives of industrial experts, researchers and consumers

3D food printing is an emerging food technology innovation that enables the personalization and on-demand production of edible products. While its academic and industrial relevance has increased over the past decade, the functional value of the technology remains largely unrealized on a commercial scale. This study aimed at updating the business outlook of 3D food printing so as to help entrepreneurs and researchers in the field to channel their research and development (R&D) activities. A three-phase mixed methods approach was utilized to gain perspectives of industrial experts, researchers, and potential consumers. Data were collected from two sets of interviews with experts, a survey with experts, and consumer focus group discussions. The results gave insights into key attributes and use cases for a 3D food printer system, including the techno-economic feasibility and consumer desirability of identified use cases. A business modelling workshop was then organized to translate these results into three refined value propositions for 3D food printing. Both the experts and consumers found personalized nutrition and convenience to be the most desirable aspects of 3D food printing. Accordingly, business models related to 3D printed snacks/meals in semi-public spaces such as fitness centers and hospitals were found to offer the highest business potential. While the technology might be mature enough at component level, the successful realization of such high-reward models however would require risk-taking during the developmental phase

Smart textile waste collection system – Dynamic route optimization with IoT

Increasing textile production is associated with an environmental burden which can be decreased with an improved recycling system by digitalization. The collection of textiles is done with so-called curbside bins. Sensor technologies support dynamic-informed decisions during route planning, helping predict waste accumulation in bins, which is often irregular and difficult to predict. Therefore, dynamic route-optimization decreases the costs of textile collection and its environmental load. The existing research on the optimization of waste collection is not based on real-world data and is not carried out in the context of textile waste. The lack of real-world data can be attributed to the limited availability of tools for long-term data collection. Consequently, a system for data collection with flexible, low-cost, and open-source tools is developed. The viability and reliability of such tools are tested in practice to collect real-world data. This research demonstrates how smart bins solution for textile waste collection can be linked to a dynamic route-optimization system to improve overall system performance. The developed Arduino-based low-cost sensors collected actual data in Finnish outdoor conditions for over twelve months. The viability of the smart waste collection system was complemented with a case study evaluating the collection cost of the conventional and dynamic scheme of discarded textiles. The results of this study show how a sensor-enhanced dynamic collection system reduced the cost 7.4% compared with the conventional one. We demonstrate a time efficiency of −7.3% and that a reduction of 10.2% in CO2 emissions is achievable only considering the presented case study.publishedVersionPeer reviewe

Pienten kiertojen kehittämistä digitalisaatiolla

Kiertodigi-hankkeen tavoitteena on ollut kehittää uusia konsepteja pienten jäte- ja kierrätysmateriaalivirtojen kiertojen hallintaan digitalisaation avulla. Pienillä virroilla tarkoitetaan sellaisia kierrätysmateriaalilähteitä, joiden kerääminen on haastavaa tarvittavan keräyskapasiteetin ja niistä syntyvien kustannusten vuoksi. Tarkastelun kohteina olivat materiaalivirrat, jotka liittyvät tuotteiden tai pakkausten loppukuluttajiin. Tarkasteltuja pieniä materiaalikiertoja olivat esimerkiksi bio-, muovi-, tekstiili-, rakennusmateriaalit. Osana hanketta pieni ryhmä tarkasteltujen avainalojen pk-yrityksiä haastateltiin käyttämällä hyväksi kiertotalouden arviointityökalua, jonka tavoitteena on auttaa pk-yrityksiä tunnistamaan paremmin kiertotalouden tarjoamat liiketoimintamahdollisuudet. Haastattelut on tehty pääosin Etelä-Pohjanmaan maakunnan alueella. Raportti esittelee alueella näihin kiertovirtoihin kehitettyjä ratkaisumalleja, joista osaa on pilotoitu. Pilotoituja hankkeita kuvataan esimerkkitapauksina ja näihin esimerkkitapauksiin on liitetty hankkeen aikana tehdyissä haastatteluissa saatuja taustatietoja kunkin alan yrityksistä. Kuhunkin esimerkkitapaukseen on liitetty myös pohdintaa käsiteltävän materiaalin kierrätysasteen parantamis- ja kehittämismahdollisuuksista. Osin avataan yleisellä tasolla myös kunkin materiaalin kierrättämiseen hankkeen aikana esiin nousseita uusia liiketoimintamahdollisuuksia ja niiden rajaehtoja. Esimerkkitapauksina käsitellään: älykäs vaatekeruu, maatalousmuovi, biojätteet, mobiilisovellus kuluttajille, keruukuljetukset, rakentaminen, mikrokiertojen syntypaikkalajittelu ja muovilaadut. Huomiota kiinnitettiin erityisesti logistiikka-analyyseihin, analyysityökaluihin ja kiertotalouden alustoihin kierrätyksen vauhdittajina, sekä digitaalisten teknologioiden hyödyntämiseen pienten kiertojen lisäämisessä.fi=vertaisarvioimaton|en=nonPeerReviewed

Optimisation-driven design to explore and exploit the process–structure–property–performance linkages in digital manufacturing

An intelligent manufacturing paradigm requires material systems, manufacturing systems, and design engineering to be better connected. Surrogate models are used to couple product-design choices with manufacturing process variables and material systems, hence, to connect and capture knowledge and embed intelligence in the system. Later, optimisation-driven design provides the ability to enhance the human cognitive abilities in decision-making in complex systems. This research proposes a multidisciplinary design optimisation problem to explore and exploit the interactions between different engineering disciplines using a socket prosthetic device as a case study. The originality of this research is in the conceptualisation of a computer-aided expert system capable of exploring process–structure–property–performance linkages in digital manufacturing. Thus, trade-off exploration and optimisation are enabled of competing objectives, including prosthetic socket mass, manufacturing time, and performance-tailored socket stiffness for patient comfort. The material system is modelled by experimental characterisation—the manufacturing time by computer simulations, and the product-design subsystem is simulated using a finite element analysis (FEA) surrogate model. We used polynomial surface response-based surrogate models and a Bayesian Network for design space exploration at the embodiment design stage. Next, at detail design, a gradient descent algorithm-based optimisation exploits the results using desirability functions to isolate Pareto non-dominated solutions. This work demonstrates how advanced engineering design synthesis methods can enhance designers’ cognitive ability to explore and exploit multiple disciplines concurrently and improve overall system performance, thus paving the way for the next generation of computer systems with highly intertwined material, digital design and manufacturing workflows. Graphical abstract: [Figure not available: see fulltext.].publishedVersionPeer reviewe

Influence of process parameters on the particle–matrix interaction of WC-Co metal matrix composites produced by laser-directed energy deposition

The prediction of the in-service behaviour of metal-matrix composites produced by laser-directed energy deposition is a fundamental challenge in additive manufacturing. The interaction between the reinforcement phase and the matrix has a major impact on the micro and macroscopic properties of these materials. This interaction is fostered by the exposition of the materials to high temperatures. Hence, it is highly influenced by the thermal cycle of the manufacturing process. In this work, an experimental approach is adopted to determine the influence of the main process parameters on the properties of metal-matrix composites. Statistical regression models are employed to consider the role of the most relevant parameters, from exploration to exploitation. The obtained trends are further corroborated by the corresponding microstructural, SEM, and EDS analyses. In terms of surface hardness, the DOE reveals different trends of the response depending on the composition of the feedstock employed. It is concluded that the strengthening behaviour of the material varies throughout the experimental domain studied. When high WC% feedstocks are employed, the main strengthening mechanism responsible for the increase of hardness is the solid-solution of tungsten and carbide precipitation. On the contrary, when low WC%s are employed, grain refinement becomes the main strengthening mechanism.publishedVersionPeer reviewe

A decision support system for the validation of metal powder bed-based additive manufacturing applications

The purpose of this research is to develop a computer-driven decision support system (DSS) to select optimal additive manufacturing (AM) machines for metal powder bed fusion (PBF) applications. The tool permits to evaluate productivity factors (i.e., cost and production time) for any given geometry. At the same time, the trade-off between feature resolution and productivity analysis is visualized and a sensitivity analysis is performed to evaluate future cost developments. This research encompasses a decision support system that includes a data structure and an algorithm which is coded in “MathWorks Matlab,” considering cost structures for metal-based AM (i.e., machine cost, material cost, and labor cost). Results of this research demonstrate that feature resolution has a crucial effect on the total cost per part, but displays decreasing impacts for higher build volume rates. Based on assumptions of business consultancies, productivity can be increased, resulting in a potential decline of cost per part of up to 55% until 2025. Using this DSS tool, it is possible to evaluate the most optimal AM production systems by selecting between several input parameters. The algorithm allows industry practitioners to retrieve information and assist in decision-making processes, including cost per part, total cost comparison, and build time evaluations for typical commercial metal PBF systems.Peer reviewe

Additive Manufacturing of Polypropylene : A Screening Design of Experiment Using Laser-Based Powder Bed Fusion

The use of commodity polymers such as polypropylene (PP) is key to open new market segments and applications for the additive manufacturing industry. Technologies such as powder-bed fusion (PBF) can process PP powder; however, much is still to learn concerning process parameters for reliable manufacturing. This study focusses in the process–property relationships of PP using laser-based PBF. The research presents an overview of the intrinsic and the extrinsic characteristic of a commercial PP powder as well as fabrication of tensile specimens with varying process parameters to characterize tensile, elongation at break, and porosity properties. The impact of key process parameters, such as power and scanning speed, are systematically modified in a controlled design of experiment. The results were compared to the existing body of knowledge; the outcome is to present a process window and optimal process parameters for industrial use of PP. The computer tomography data revealed a highly porous structure inside specimens ranging between 8.46% and 10.08%, with porosity concentrated in the interlayer planes in the build direction. The results of the design of experiment for this commercial material show a narrow window of 0.122 &gt; Ev &gt; 0.138 J/mm3 led to increased mechanical properties while maintaining geometrical stability.Funding Agency:Aalto University Department of Mechanical Engineering </p

Towards the Interoperability of IoT Platforms : A Case Study for Data Collection and Data Storage

Interoperability is one of the biggest challenges in IoT. There is a need to connect tools and services from different platforms to improve performance and flexibility as well as to get rid of vendor-locks. Moreover, fully established practices to classify IoT systems to support the development of multi-platform systems have not been grounded. This paper addresses these issues on a two-fold approach. Firstly, by introducing a classification that is based on the tasks performed in IoT system. Secondly by proposing a conceptual middleware to connect different IoT platforms as for a possible solution for interoperability issues in IoT.publishedVersionPeer reviewe