Economic aspects of additive manufacturing: benefits, costs and energy consumption

Additive Manufacturing (AM) refers to the use of a group of technologies capable of combining material layer-by-layer to manufacture geometrically complex products in a single digitally controlled process step, entirely without moulds, dies or other tooling. AM is a parallel manufacturing approach, allowing the contemporaneous production of multiple, potentially unrelated, components or products. This thesis contributes to the understanding of the economic aspects of additive technology usage through an analysis of the effect of AM s parallel nature on economic and environmental performance measurement. Further, this work assesses AM s ability to efficiently create complex components or products. To do so, this thesis applies a methodology for the quantitative analysis of the shape complexity of AM output. Moreover, this thesis develops and applies a methodology for the combined estimation of build time, process energy flows and financial costs. A key challenge met by this estimation technique is that results are derived on the basis of technically efficient AM operation. Results indicate that, at least for the technology variant Electron Beam Melting, shape complexity may be realised at zero marginal energy consumption and cost. Further, the combined estimator of build time, energy consumption and cost suggests that AM process efficiency is independent of production volume. Rather, this thesis argues that the key to efficient AM operation lies in the user s ability to exhaust the available build space

Informing additive manufacturing technology adoption: total cost and the impact of capacity utilisation

Informing Additive Manufacturing (AM) technology adoption decisions, this paper investigates the relationship between build volume capacity utilisation and efficient technology operation in an inter-process comparison of the costs of manufacturing a complex component used in the packaging industry. Confronting the reported costs of a conventional machining and welding pathway with an estimator of the costs incurred through an AM route utilising Direct Metal Laser Sintering (DMLS), we weave together four aspects: optimised capacity utilisation, ancillary process steps, the effect of build failure and design adaptation. Recognising that AM users can fill unused machine capacity with other, potentially unrelated, geometries, we posit a characteristic of ‘fungible’ build capacity. This aspect is integrated in the cost estimation framework through computational build volume packing, drawing on a basket of sample geometries. We show that the unit cost in mixed builds at full capacity is lower than in builds limited to a single type of geometry; in our study, this results in a mean unit cost overstatement of 157%. The estimated manufacturing cost savings from AM adoption range from 36 to 46%. Additionally, we indicate that operating cost savings resulting from design adaptation are likely to far outweigh the manufacturing cost advantage

Analysis of irregular three-dimensional packing problems in additive manufacturing: a new taxonomy and dataset

© 2018 Informa UK Limited, trading as Taylor & Francis Group. With most Additive Manufacturing (AM) technology variants, build processes take place inside an internal enclosed build container, referred to as a ‘build volume’. It has been demonstrated that the effectiveness with which this volume is filled with product geometries forms an important determinant of overall process efficiency in AM. For effective operations management, it is important to understand not only the problem faced, but also which methods have proved effective (or ineffective) for problems with these characteristics in the past. This research aims to facilitate this increased understanding. The build volume packing task can be formulated as a three-dimensional irregular packing (3DIP) problem, which is a combinatorial optimisation problem requiring the configuration of a set of arbitrary volumetric items. This paper reviews existing general cutting and packing taxonomies and provides a new specification which is more appropriate for classifying the problems encountered in AM. This comprises a clear-cut problem definition, a set of precise categorisation criteria for objectives and problem instances, and a simple notation. Furthermore, the paper establishes an improved terminology with terms that are familiar to, but not limited to, researchers and practitioners in the field of AM. Finally, this paper describes a new dataset to be used in the evaluation of existing and proposed computational solution methods for 3DIP problems encountered in AM and discusses the importance of this research for further underpinning work

Interactions of fast‐moving consumer goods in cooking: Insights from a quantitative ethnographic study

Fast‐moving consumer goods (FMCGs) are increasingly equipped with enhanced packaging that incorporates novel functionalities. Providing FMCGs with this technology is challenging due to their unique characteristics, such as their low cost and short lifespan. Knowledge derived from a comprehension of their interactions in practice can help develop FMCGs that better cater to consumer needs and are well‐integrated into real‐world contexts. To help develop a method for the formation of such practical insights, 20 households were visited where participants were then observed as they cooked a meal. The sessions were captured on video, and a detailed record of the interactions between individuals, FMCGs and other items was made. A quantitative ethnographic approach was applied to analyse and build an understanding of different aspects of these interactions including their frequential, sequential and correlational features. The findings are discussed through the lens of how an appreciation of the interactions of FMCGs can serve as a valuable guidance for the design and development of their enhanced counterparts. The discovery that FMCGs are linked to the use of other items, for instance, is proposed as an opportunity to make use of the unique properties of the other items that a given FMCG commonly interacts with as a resource to create functionalities. As an exploratory reflection of how FMCGs are utilised in practice, the methods and knowledge presented in this study can be valuable in creating enhanced FMCGs by advocating for a product development process in which decisions are firmly grounded in empirical insights

Shape complexity and process energy consumption in electron beam melting: a case of something for nothing in additive manufacturing?

Additive manufacturing (AM) technology is capable of building up component geometry in a layer-by-layer process, entirely without tools, molds, or dies. One advantage of the approach is that it is capable of efficiently creating complex product geometry. Using experimental data collected during the manufacture of a titanium test part on a variant of AM technology, electron beam melting (EBM), this research studies the effect of a variation in product shape complexity on process energy consumption. This is done by applying a computationally quantifiable convexity-based characteristic associated with shape complexity to the test part and correlating this quantity with per-layer process energy consumption on the EBM system. Only a weak correlation is found between the complexity metric and energy consumption (ρ = .35), suggesting that process energy consumption is indeed not driven by shape complexity. This result is discussed in the context of the energy consumption of computer-controlled machining technology, which forms an important substitute to EBM. This article further discusses the impact of available additional shape complexity at the manufacturing process level on the incentives toward minimization of energy inputs, additional benefits arising later within the product’s life cycle, and its implications for value creation possibilities

Toward better build volume packing in additive manufacturing: classification of existing problems and benchmarks

In many cases, the efficient operation of Additive Manufacturing (AM) technology relies on build volumes being packed effectively. Packing algorithms have been developed in response to this requirement. The configuration of AM build volumes is particularly challenging due to the multitude of irregular geometries encountered and the potential benefits of nesting parts. Currently proposed approaches to address this packing problem are routinely evaluated on data sets featuring shapes that are not representative of targeted manufacturing products. This study provides a useful classification of AM build volume packing problems and an overview of existing benchmarks for the analysis of such problems. Additionally, this paper discusses characteristics of future, more realistic, benchmarks with the intention of promoting research toward effective and efficient AM build volume packing being integrated into AM production planning methodologies

Data as a Resource for Designing Digitally Enhanced Consumer Packaged Goods

The incorporation of digital functionalities into consumer packaged goods (CPG) has the potential to improve our lives by supporting us in our daily practises. However, despite the increasing availability of data about their use, research is needed to explore how these data can be harnessed to create such digital enhancements. This paper explores how consumers can utilise data about interactions with CPGs to conceptualise their enhanced versions. We devised a data-inspired ideation approach, using data visualisations and design cards to facilitate the conceptualisation of enhanced CPGs. Analysing the role of data as expressed through participants’ comments and designs, we found that data served as a basis for the creation of unique concepts imbued with greater consideration for the experiences of others and attention to their own interests. Our study shows the value of empowering consumers through data to broaden and inform their contributions towards the creation of smart products

Toward better build volume packing in additive manufacturing: classification of existing problems and benchmarks

In many cases, the efficient operation of Additive Manufacturing (AM) technology relies on build volumes being packed effectively. Packing algorithms have been developed in response to this requirement. The configuration of AM build volumes is particularly challenging due to the multitude of irregular geometries encountered and the potential benefits of nesting parts. Currently proposed approaches to address this packing problem are routinely evaluated on data sets featuring shapes that are not representative of targeted manufacturing products. This study provides a useful classification of AM build volume packing problems and an overview of existing benchmarks for the analysis of such problems. Additionally, this paper discusses characteristics of future, more realistic, benchmarks with the intention of promoting research toward effective and efficient AM build volume packing being integrated into AM production planning methodologies

A Structured Approach for Synchronising the Applications of Failure Mode and Effects Analysis

Failure Mode and Effects Analysis (FMEA) is a systematic approach for evaluating the potential failure modes in a system, and is mainly employed in three distinct tasks labelled: (1) Functional FMEA – evaluating those failures associated with product functional definition, (2) Design FMEA – analysing those failures associated with design definition and (3) Process FMEA – assessing potential failures in manufacturing and assembly processes. The lit-erature review has shown limited works on the field of synchronising these different tasks into a working model. To address this gap, this research developed a framework for integrating these tasks of FMEAs, and then qualita-tively validating the proposed framework. This research adopted a semi-structured questionnaire to collect ex-perts’ feedback and validate the proposed framework. The t-test was then employed to evaluate the collected feedback. The findings highlight that the proposed framework is applicable and could facilitate the synchronisa-tion of the different tasks of FMEA. This research presents a methodological approach for executing and synchro-nising FMEAs. Therefore, the proposed framework is practically relevant as an aid for the practitioners in catching the cascading failures and reducing the relevant impact

Modelling the influence of UV curing strategies for optimisation of inkjet based 3D printing

A predictive model is developed to assist in the design and manufacture of structures by inkjet based 3D printing (IJ3DP)/additive manufacturing. IJ3DP often exploits photopolymerisation to rapidly convert a photoreactive liquid ink into a solid product. Unfortunately, deviations from the intended design and product performance are often observed and a lack of understanding of the underlying processes and their interactions prevents users from resolving these issues. We develop and validate a predictive model that incorporates the critical processing parameters, including UV source pathway, UV intensity, printing strategy, and interlayer attenuation, such that we are able to predict the degree of ink conversion throughout the product. We show how this model can then be used to guide users by demonstrating the coupling of this description with a cost model and illustrating how printing strategy affects descriptors of both the quality and cost of production