scholarly journals Production Capacity Pooling in Additive Manufacturing, Possibilities and Challenges

Author(s):  
Siavash H. Khajavi ◽  
Jan Holmström
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kyle C. McDermott ◽  
Ryan D. Winz ◽  
Thom J. Hodgson ◽  
Michael G. Kay ◽  
Russell E. King ◽  
...  

PurposeThe study aims to investigate the impact of additive manufacturing (AM) on the performance of a spare parts supply chain with a particular focus on underlying spare part demand patterns.Design/methodology/approachThis work evaluates various AM-enabled supply chain configurations through Monte Carlo simulation. Historical demand simulation and intermittent demand forecasting are used in conjunction with a mixed integer linear program to determine optimal network nodal inventory policies. By varying demand characteristics and AM capacity this work assesses how to best employ AM capability within the network.FindingsThis research assesses the preferred AM-enabled supply chain configuration for varying levels of intermittent demand patterns and AM production capacity. The research shows that variation in demand patterns alone directly affects the preferred network configuration. The relationship between the demand volume and relative AM production capacity affects the regions of superior network configuration performance.Research limitations/implicationsThis research makes several simplifying assumptions regarding AM technical capabilities. AM production time is assumed to be deterministic and does not consider build failure probability, build chamber capacity, part size, part complexity and post-processing requirements.Originality/valueThis research is the first study to link realistic spare part demand characterization to AM supply chain design using quantitative modeling.


Materials ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4161 ◽  
Author(s):  
Vincenzo Tagliaferri ◽  
Federica Trovalusci ◽  
Stefano Guarino ◽  
Simone Venettacci

In this study, the authors present a comparative analysis of different additive manufacturing (AM) technologies for high-performance components. Four 3D printers, currently available on the Italian national manufacturing market and belonging to three different AM technologies, were considered. The analysis focused on technical aspects to highlight the characteristics and performance limits of each technology, economic aspects to allow for an assessment of the costs associated with the different processes, and environmental aspects to focus on the impact of the production cycles associated with these technologies on the ecosystem, resources and human health. This study highlighted the current limits of additive manufacturing technologies in terms of production capacity in the case of large-scale production of plastic components, especially large ones. At the same time, this study highlights how the geometry of the object to be developed greatly influences the optimal choice between the various AM technologies, in both technological and economic terms. Fused deposition modeling (FDM) is the technology that exhibits the greatest limitations hindering mass production due to production times and costs, but also due to the associated environmental impact.


Author(s):  
S. Castillo-Rivera ◽  
J. De Antón ◽  
R. Del Olmo ◽  
J. Pajares ◽  
A. López-Paredes

<p>Genetic Algorithms (GAs) are introduced to tackle the packing problem. The scheduling in Additive Manufacturing (AM) is also dealt with to set up a managed market, called “Lonja3D”. This will enable to determine an alternative tool through the combinatorial auctions, wherein the customers will be able to purchase the products at the best prices from the manufacturers. Moreover, the manufacturers will be able to optimize the production capacity and to decrease the operating costs in each case.</p>


Author(s):  
Yosep Oh ◽  
Sara Behdad

Abstract The purpose of this study is to optimize production planning decisions in additive manufacturing for mass customization (AMMC) systems in which customer demands are highly variable. The main research question is to find the optimal quantity of products for scheduling, the economic scheduling quantity (ESQ). If the scheduling quantity is too large, the time to collect customer orders increases and a penalty cost occurs due to the delay in responding to consumer demands. On the other hand, if the scheduling quantity is too small, the number of parts per jobs decreases and parts are not efficiently packed within a workspace and consequently the build process cost increases. An experiment is provided for the case of stereolithography (SLA) and 2D packing to demonstrate how the build time per part increases as the scheduling quantity decreases. In addition, a mathematical framework based on ESQ is provided to evaluate the production capacity in satisfying the market demand.


2021 ◽  
Author(s):  
Kyle C McDermott ◽  
Ryan D Winz ◽  
Thom J Hodgson ◽  
Michael G Kay ◽  
Russell E King ◽  
...  

Purpose - Investigate the impact of additive manufacturing (AM) on the performance of a spare parts supply chain with a particular focus on underlying spare part demand patterns. Design/Methodology/Approach - This work evaluates various AM-enabled supply chain configurations through Monte Carlo simulation. Historical demand simulation and intermittent demand forecasting are used in conjunction with a mixed integer linear program to determine optimal network nodal inventory policies. By varying demand characteristics and AM capacity this work assesses how to best employ AM capability within the network. Findings - This research assesses the preferred AM-enabled supply chain configuration for varying levels of intermittent demand patterns and AM production capacity. The research shows that variation in demand patterns alone directly affects the preferred network configuration. The relationship between the demand volume and relative AM production capacity affects the regions of superior network configuration performance. Research limitations/implications - This research makes several simplifying assumptions regarding AM technical capabilities. AM production time is assumed to be deterministic and does not consider build failure probability, build chamber capacity, part size, part complexity, and post-processing requirements. Originality/value - This research is the first study to link realistic spare part demand characterization to AM supply chain design using quantitative modeling.


2016 ◽  
Vol 704 ◽  
pp. 271-281 ◽  
Author(s):  
Ian Mellor ◽  
Greg Doughty

As the adoption of components fabricated via titanium powder metallurgy (PM) techniques becomes more prevalent, and projected to increase at a substantial rate over the next decade, especially in the field of additive manufacturing (AM), there is a necessity to increase titanium powder production capacity from the current annual level of ca. 6000 tonnes per annum. At present a well-documented barrier restricting this widespread implementation, is the inherently high cost of the feedstock, an issue which to date has been neglected to some degree, at the expense of developing the individual powder metallurgy routes. The scope of this overview therefore is to provide an insight of both established and novel methods of titanium powder production, as potential opportunities to satisfy this growing demand. Particular emphasis will focus on Metalysis, a company founded to commercialize an innovative electrochemical approach for the synthesis of metals and alloys from their respective oxides, where the ability to generate titanium eloquently demonstrates the extent of its capabilities.The patented Metalysis technology, exploiting the FFC® Cambridge process, lends itself to producing alloys and intermetallics, where Ti-6Al-4V provides a prime example of this. Furthermore, as electrolysis occurs solely in the solid state, issues pertaining to segregation due to dissimilar densities and melting points are avoided. It is possible to tailor both the average particle diameter and size distribution of the product targeted powder metallurgy (PM) applications, based upon appropriate selection of the feed. The attraction of this strategy is that the steps associated with conventional metal powder synthesis are circumvented, resulting in a significant cost reduction. Moreover it has recently been revealed that titanium can be produced directly from naturally occurring ore (beach sand) and synthetic rutile, with the ensuing product presenting itself as an inexpensive and abundant feedstock for additive manufacturing (AM). This represents a paradigm shift in the availability of consumables for the 3D printing market.


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