Date of Award


Degree Type


Degree Name

Master of Science


Industrial Engineering

Major Professor

Mingzhou Jin

Committee Members

Oleg Shylo, Vincent C. Paquit


As marketplace competition drives industrial innovation to increase product value and decrease production costs, emerging technologies foster a new era through Industry 4.0. One aspect of the movement, additive manufacturing, or 3D [three-dimensional] printing, contains potential to revolutionize traditional manufacturing techniques and approach to design. However, uncertainties within the processes and high investment costs deter corporations from implementing and developing the technology. While several industries are benefitting from additive manufacturing’s current state, as the technology continues to progress, more companies will need to evaluate it for industrial viability and adoption. As such, there exists a need for a framework to evaluate the business case for investment review. While many papers in the literature provide cost estimation models for additively manufactured parts, there does not exist a thorough guide for decision making. This master’s thesis report introduces a process to evaluate machine investment and part production between additive manufacturing and traditional manufacturing technologies using operational and financial key performance indicators. A case study application of the process yielded suspect part unit costs 3.71% higher than its literature basis, indicating a viable methodology. The present value total investment cost for an EOSINT M 270 machine tool, with a five-year lifespan, was determined to be $3,241,710 in the case context; breakeven point occurs beyond investment life at 2.28 years. Results were dependent on product valuation and assumptions made. Key output metrics indicated the suspect machine could generate 5,238 units annually at a 1.4 part per hour throughput rate. As part production was deemed feasible under the provided constraints, sensitivity analysis indicated material and equipment costs as cost drivers. Similarly, production drivers were found to be scan rate and machine utilization. Results were consistent with common belief that additive manufacturing is currently viable for small-to-mid series production, or parts of high complexity value. These findings indicate areas of improvement for the additive manufacturing industry for commercialization purposes, and demonstrate a useful methodology for assessing the business case of additive manufacturing.

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