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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Selective laser sintering has become one of the most popular additive manufacturing technologies owing to its great capability of fabricating complex structures with reduced or even eliminated need for the support structure. Meanwhile, an average of 50% to 70% of the consumed powder materials is not directly used for part fabrication. To reduce material waste and enhance material usage efficiency, research studies have been conducted to facilitate the recycling and/or reusing of the waste powder in selective laser sintering. In this research, polyamide 12 powders are studied including virgin powder, waste powder, recycled powder, and mixed powder (with a 30% refresh rate) in terms of their microscopic morphology and material properties. In addition, the location of the powder sampled from the build chamber is also studied for its impact on the powder size and shape. Experimental results show that the average particle size does not change much in different samples, but the standard deviation increases in waste powder. Furthermore, the averaged ultimate tensile strength of test specimens fabricated with virgin powder is around 25% higher than specimens made with mixed powder (30% virgin powder and 70% recycled powder), showing a clear mechanical degradation.

Additive manufacturing (AM), owing to its unique layer-wise production method, can offer evident advantages comparing to traditional manufacturing (TM) technologies such as faster production, lower cost, and less waste. The uses of AM in rapid tooling, prototyping, and manufacturing have been innovating the current manufacturing industry from the process level to the entire supply chain. Most existing research on AM is focused on process improvement and new materials, largely neglecting the potential economic and environmental benefits enabled by AM supply chains. This research investigates an innovative supply chain structure, i.e., the integrated production-inventory-transportation (PIT) structure that is uniquely enabled by AM because of its capability of fabricating the entire product with less or even no need for assembly and labor involvement. This paper quantifies and compares the greenhouse gas (GHG) emissions of TM and AM-enabled PIT supply chains. The manufacturing industry is a major source of GHG emissions in the U.S., which therefore needs to be studied in order to explore opportunities for reducing GHG emissions for environmental protection. Case study results suggest that a potential reduction of 26.43% of GHG emissions can be achieved by adopting the AM-enabled PIT supply chain structure. Sensitivity analysis results show that a 20% variation in GHG emission intensity (the amount of CO2eq emissions caused by generating a unit of electricity) can lead to a 6.26% change in the total GHG emissions in the AM-enabled PIT supply chain.