ArticlePloS one2024
ABC model for cost estimation of custom implants by Additive Manufacturing.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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3 authors.
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Abstract
Computer-aided design (CAD) models can now be directly converted into products and structures. One technique to realize such approach is through Additive Manufacturing (AM). AM is relatively new manufacturing technology in which products are manufactured by layering various materials like rubber, metal, ceramic, composites, and polymers. However, the use of this technology requires consideration of its associated cost to ensure its competitiveness. In this paper, a simplified mathematical cost model is suggested. The model considers the main components of costs. The model formula utilizes expenses related to the pre-processing, main processing, and the post-processing operations. To validate the model, it is tested to estimate the cost of medical implants manufacturing using AM technique. In many cases, medical implants require unique or dedicated design for each patient. Hence cost estimation will help to assess and estimate the required financial resources for such operations. A case study is provided in this paper to estimate the manufacturing cost of a finger's phalanges bone, with metal implant using AM technique. The developed model may be described as Activity Based Costing (ABC). The model is introduced to estimate the cost of parts produced using AM technique. Although the model is developed to suit custom implant manufacturing using AM technique, its use may also be adapted to suit the manufacturing of many other parts and products. The developed model is aiming to achieve several tasks namely assigning cost drivers to each activity, estimating the cost of individual actions, allocating overhead expenses, calculating the overall production cost, and establishing an acceptable selling price. It assists companies in computing the cost of custom implants for customers, enhancing the accuracy of production cost estimates, and ultimately boosting profitability.
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