Understanding the Limits of Generic LCI Data: Process-Induced Variability in Machining

Life Cycle Inventory (LCI) databases provide essential average data for manufacturing processes to support life cycle assessment (LCA) applications. However, highly parameter-dependent processes like machining can exhibit environmental impact variations that exceed the boundaries of these average values. This research investigates shoulder milling operations in Inconel 718 across material removal rates from 1.5 to 32 cm³/min with different tool types. Using a functional unit of 500 cm³ cutting volume, the study captures complete environmental profiles including process energy and tool-related impacts. Experimental results reveal 3.7-fold variations in environmental impacts across the parameter space. Neither maximum productivity nor minimum cutting loads consistently yielded optimal environmental performance, highlighting complex interdependencies between process parameters, tool life, and energy consumption. Generic LCI values cannot guide parameter-specific optimization decisions in real process design. We propose complementing existing databases with Discrete Process Elements (DPEs), a novel framework that maintains universal applicability while enabling refined, parameter-sensitive assessments for specific manufacturing scenarios without extensive primary data collection.