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

Design and Additive Manufacture of Functionally Graded Structures based on Digital Materials

Voxel-based multimaterial jetting additive manufacturing allows fabrication of digital materials (DMs) at the meso-scale (∼1 mm) by controlling the deposition patterns of soft elastomeric and rigid glassy polymers at the voxel-scale (∼90 μm). The digital materials can then be used to create heterogeneous functionally graded material (FGM) structures at the macro-scale (∼10 mm) programmed to behave in a predefined manner. This offers huge potential for design and fabrication of novel and complex bespoke mechanical structures. This paper presents a complete design and manufacturing workflow that simultaneously integrates material design, structural design, and product fabrication of FGM structures based on digital materials. This is enabled by a regression analysis of the experimental data on mechanical performance of the DMs i.e., Young’s modulus, tensile strength and elongation at break. This allows us to express the material behavior simply as a function of the microstructural descriptors (in this case, just volume fraction) without having to understand the underlying microstructural mechanics while simultaneously connecting it to the process parameters. Our proposed design and manufacturing approach is then demonstrated and validated in two series of design exercises to devise complex FGM structures. First, we design, computationally predict and experimentally validate the behavior of prescribed designs of FGM tensile structures with different material gradients. Second, we present a design automation approach for optimal FGM structures. The comparison between the simulations and the experiments with the FGM structures shows that the presented design and fabrication workflow based on our modeling approach for DMs at meso-scale can be effectively used to design and predict the performance of FGMs at macro-scale

Megaproyectos y estrategias alternativas para el ciclo urbano del agua: el caso del sistema de abastecimiento de Sevilla (España)

El concepto de 'megaproyecto' ha adquirido en los últimos años una notable operatividad para categorizar un tipo de intervenciones físicas, generalmente infraestructurales, que reúnen una serie de características comu-nes. El objetivo de esta comunicación es mostrar cómo el proceso de proyección y construcción del embalse de Los Melonares y sus obras auxiliares, a lo largo de un periodo de más de 25 años (1989-2015), responde a este modelo. A través del análisis empírico de este caso se pretende comprobar la solidez del planteamiento teóri-co, a la vez que se presta atención a cómo las circunstancias singulares, dinámicas y rodeadas de incertidumbre de cada contexto socio-ecológico (marco físico natural, aspectos económicos, sociales, culturales, instituciona-les, políticos) interaccionan en cada caso específico para dar lugar a procesos territoriales concretos y fluidos

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

Understanding pore formation and the effect on mechanical properties of high speed sintered polyamide-12 parts: A focus on energy input

High Speed Sintering is a novel powder-bed fusion Additive Manufacturing technique that uses an infrared lamp to provide intensive thermal energy to sinter polymer powders. The amount of thermal energy is critical to particle coalescence related defects such as porosity. This study investigates the effect of energy input on porosity and the resulting mechanical properties of polyamide-12 parts. Samples were produced at different lamp speeds, generating varying amount of energy input from a low to a high level. They were then scanned using X-ray Computed Tomography technique, following which they were subject to tensile testing. A strong correlation between energy input, porosity and mechanical properties was found, whereby pore formation was fundamentally caused by insufficient energy input. A greater amount of energy input resulted in a reduced porosity level, which in turn led to improved mechanical properties. The porosity, ultimate tensile strength and elongation achieved were 0.58%, 42.4 MPa and 10.0%, respectively, by using the standard parameters. Further increasing the energy input resulted in the lowest porosity of 0.14% and the highest ultimate tensile strength and elongation of 44.4 MPa and 13.5%, respectively. Pore morphology, volume, number density and spatial distribution were investigated, which were found to be closely linked with energy input and mechanical properties