Implementation of Additive Manufacturing Cost Estimation Tool (AMCET) Using Break-down Approach

Mahadik, Aditya; Masel, Dale T.

doi:10.1016/j.promfg.2018.10.014

Cited by 25 publications

(13 citation statements)

References 6 publications

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“…The production cost, as one of the critical aspects of sustainability (along with environmental and social sustainability), plays a guiding role in the evaluation of a new manufacturing process [14]. In the current literature, cost assessments have been conducted for different AM processes [15][16][17][18][19][20][21][22][23][24][25][26][27] including fused filament fabrication (FFF) [18], mask image projection-based stereolithography [23], fused deposition modeling [19], light-directed electrophoretic deposition [28], inkjet printing [29], multijet printing [30], laminated object manufacturing [31], and electron beam manufacturing [32], etc.…”

Section: Introductionmentioning

confidence: 99%

Cost Modeling and Evaluation of Direct Metal Laser Sintering with Integrated Dynamic Process Planning

Yang

2020

Sustainability

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Additive manufacturing technologies have been adopted in a wide range of industries such as automotive, aerospace, and consumer products. Currently, additive manufacturing is mainly used for small-scale, low volume productions due to its limitations such as high unit cost. To enhance the scalability of additive manufacturing, it is critical to evaluate and preferably reduce the cost of adopting additive manufacturing for production. The current literature on additive manufacturing cost mainly adopts empirical approaches and does not sufficiently explore the cost-saving potentials enabled by leveraging different process planning algorithms. In this article, a mathematical cost model is established to quantify the different cost components in the direct metal laser sintering process, and it is applicable for evaluating the cost performance when adopting dynamic process planning with different layer-wise process parameters. The case study results indicate that 12.73% of the total production cost could be potentially reduced when applying the proposed dynamic process planning algorithm based on the complexity level of geometries. In addition, the sensitivity analysis results suggest that the raw material price and the overhead cost are the two key cost drivers in the current additive manufacturing market.

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Section: Introductionmentioning

confidence: 99%

Cost Modeling and Evaluation of Direct Metal Laser Sintering with Integrated Dynamic Process Planning

Yang

2020

Sustainability

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“…For example, the Quality factor is influenced by the design and the cost as per research in the past studies on this factor [23][24][25][26]. On the other hand, the Cost factor is influenced by the resources used, such as the materials, labor, machines, and tooling [27,28]. Lead time is being controlled by the product's input, process, and transportation time [17,29].…”

Section: Sms Evaluation Factorsmentioning

confidence: 99%

“…Lead time is being controlled by the product's input, process, and transportation time [17,29]. Productivity is being influenced by the indicators such as Six Sigma, human factors, and engineering methods [26,27]. Lastly, Environmental Impact is being ruled out by various indicators such as compressed air, electricity for machining, metalworking factory, electricity for cooling, heating, and transportations [28,29].…”

Section: Sms Evaluation Factorsmentioning

confidence: 99%

An Evaluation Model of Smart Manufacturing System Configurations Prior to Implementation Using Fuzzy Logic

et al. 2022

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Several research works have addressed the different aspects and technologies associated with Smart Manufacturing Systems (SMS); however, the evaluation challenges while establishing a new SMS that requires pre-implementation planning and assessment have given little attention. To overcome this limitation, this paper formulates an evaluation framework by identifying apparent evaluation factors to measure the effectiveness of a particular SMS configuration before implementation. Three factors from the literature studies have been used as inputs to control the final output of the configuration modal. Compositions were manipulated based on how factors affected the manufacturing cost justification in multiple setups. Different configurations were analyzed based on the trained Fuzzy Logic model by configurations and based on the trained Fuzzy Logic model using MATLAB’s Fuzzy Logic Designer tool. Results obtained from the evaluation performed by various configuration experiments were later presented to actual field engineers from the manufacturing industry to evaluate the satisfaction level of the evaluation framework. The result showed that this proposed configuration model has a satisfactory rate of 83.7%, as this was achieved by significant feedback from field engineers. This study has significantly facilitated the identification of influential factors and the measured relationship of the factors in the formulated configurations, enabling the best configuration approach to be identified. Therefore, it can be concluded that a visualized and measured configuration system can influence decision-making in the manufacturing industry, thus allowing manufacturers to stay competitive by making well-versed decisions proactively. Exclusively, this research has staged a framework for the industry to follow suit and adapt for future research work related to the SMS field.

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“…Most early cost models include material, machine and labor costs (Hopkinson and Dickens, 2003;Ruffo et al, 2006a;Atzeni and Salmi, 2012;Mahadik and Masel, 2018;Baumers et al, 2016Baumers et al, , 2017Sharma and Dixit, 2019;Atzeni et al, 2010) for a variety of AM processes. Over time, researchers have improved the models to also consider the following:…”

Section: Jdalmentioning

confidence: 99%

Performance tradeoffs for spare parts supply chains with additive manufacturing capability servicing intermittent demand

McDermott

Winz

Hodgson

et al. 2021

JDAL

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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.

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Implementation of Additive Manufacturing Cost Estimation Tool (AMCET) Using Break-down Approach

Cited by 25 publications

References 6 publications

Cost Modeling and Evaluation of Direct Metal Laser Sintering with Integrated Dynamic Process Planning

Cost Modeling and Evaluation of Direct Metal Laser Sintering with Integrated Dynamic Process Planning

An Evaluation Model of Smart Manufacturing System Configurations Prior to Implementation Using Fuzzy Logic

Performance tradeoffs for spare parts supply chains with additive manufacturing capability servicing intermittent demand

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