Additive manufacturing - Test artifacts - Geometric capability assessment of additive manufacturing systems

2019 ◽  
2021 ◽  
Vol 1 ◽  
pp. 2127-2136
Author(s):  
Olivia Borgue ◽  
John Stavridis ◽  
Tomas Vannucci ◽  
Panagiotis Stavropoulos ◽  
Harry Bikas ◽  
...  

AbstractAdditive manufacturing (AM) is a versatile technology that could add flexibility in manufacturing processes, whether implemented alone or along other technologies. This technology enables on-demand production and decentralized production networks, as production facilities can be located around the world to manufacture products closer to the final consumer (decentralized manufacturing). However, the wide adoption of additive manufacturing technologies is hindered by the lack of experience on its implementation, the lack of repeatability among different manufacturers and a lack of integrated production systems. The later, hinders the traceability and quality assurance of printed components and limits the understanding and data generation of the AM processes and parameters. In this article, a design strategy is proposed to integrate the different phases of the development process into a model-based design platform for decentralized manufacturing. This platform is aimed at facilitating data traceability and product repeatability among different AM machines. The strategy is illustrated with a case study where a car steering knuckle is manufactured in three different facilities in Sweden and Italy.


2021 ◽  
Author(s):  
Alexander Matschinski ◽  
Tim Osswald ◽  
Klaus Drechsler

The market segment of additive manufacturing is showing an annual growth of more than ten percent, with extrusion-based processes being the larger segment of the market. The scope of use is limited to secondary structures. Equipment manufacturers try to guarantee constant material characteristics by closed systems. The characteristic values are up to 50% below the ones from injection molding. The processing of high-performance polymers with reinforcing fibers is an additional challenge. Further development requires an opening of the material and manufacturing systems. The guidelines and standardization for this are still missing. For this reason, a functional analysis (FA) according to TRIZ ("theory of the resolution of invention-related tasks") is performed within this study. This identifies the undesired functions and quantifies their coupling with process components and parameters. In the FA, the manufactured part is the target component in order to address its quality. This way the FA identifies five undesirable functions in the process. These are: deform, cool, weaken, swell and shape. For hightemperature thermoplastics, thermal shrinkage is the primary cause of geometric tolerance. Therefore, the deformation is largely dependent on the cooling mechanism. For a detailed analysis, the polymer melt is further disassembled. The results are six sub-components. The weakening is mainly due to the physical phase of the voids, which exists during the entire processing. The breakdown comprises physical fields such as stress, temperature and flow. These determine the output properties as well as the bonding between the layers. The associated functions are the swelling and shaping. In order to generate broadly applicable standardizations, research questions for further investigation are derived from this study.


Author(s):  
Farhad Imani ◽  
Bing Yao ◽  
Ruimin Chen ◽  
Prahalada Rao ◽  
Hui Yang

Nowadays manufacturing industry faces increasing demands to customize products according to personal needs. This trend leads to a proliferation of complex product designs. To cope with this complexity, manufacturing systems are equipped with advanced sensing capabilities. However, traditional statistical process control methods are not concerned with the stream of in-process imaging data. Also, very little has been done to investigate nonlinearity, irregularity, and inhomogeneity in image stream collected from manufacturing processes. This paper presents the multifractal spectrum and lacunarity measures to characterize irregular and inhomogeneous patterns of image profiles, as well as detect the hidden dynamics of the underlying manufacturing process. Experimental studies show that the proposed method not only effectively characterizes the surface finishes for quality control of ultra-precision machining but also provides an effective model to link process parameters with fractal characteristics of in-process images acquired from additive manufacturing. This, in turn, will allow a swift response to processes changes and consequently reduce the number of defective products. The proposed fractal method has strong potentials to be applied for process monitoring and control in a variety of domains such as ultra-precision machining, additive manufacturing, and biomanufacturing.


Author(s):  
G.K. Awari ◽  
C.S. Thorat ◽  
Vishwjeet Ambade ◽  
D.P. Kothari

2018 ◽  
Vol 38 (12) ◽  
pp. 2313-2343 ◽  
Author(s):  
Daniel R. Eyers ◽  
Andrew T. Potter ◽  
Jonathan Gosling ◽  
Mohamed M. Naim

Purpose Flexibility is a fundamental performance objective for manufacturing operations, allowing them to respond to changing requirements in uncertain and competitive global markets. Additive manufacturing machines are often described as “flexible,” but there is no detailed understanding of such flexibility in an operations management context. The purpose of this paper is to examine flexibility from a manufacturing systems perspective, demonstrating the different competencies that can be achieved and the factors that can inhibit these in commercial practice. Design/methodology/approach This study extends existing flexibility theory in the context of an industrial additive manufacturing system through an investigation of 12 case studies, covering a range of sectors, product volumes, and technologies. Drawing upon multiple sources, this research takes a manufacturing systems perspective that recognizes the multitude of different resources that, together with individual industrial additive manufacturing machines, contribute to the satisfaction of demand. Findings The results show that the manufacturing system can achieve seven distinct internal flexibility competencies. This ability was shown to enable six out of seven external flexibility capabilities identified in the literature. Through a categorical assessment the extent to which each competency can be achieved is identified, supported by a detailed explanation of the enablers and inhibitors of flexibility for industrial additive manufacturing systems. Originality/value Additive manufacturing is widely expected to make an important contribution to future manufacturing, yet relevant management research is scant and the flexibility term is often ambiguously used. This research contributes the first detailed examination of flexibility for industrial additive manufacturing systems.


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