Parametric environmental, economic and social assessment of WAAM, with a conventional CNC milling benchmark.
Environmental, economic and social impact drivers for the current scenario
Environmental contribution by inventory flow and midpoint categories.
Normalised separately within each impact category, following the two-process percentage bars in Sheet1!L89:U106. Midpoint units are not added together.
Run the uncertainty simulation below to see probability distributions.
Unit cost breakdown for wire deposition and CNC machining.
Run the uncertainty simulation below to see probability distributions.
18 equal-weight binary criteria, with country reference data.
Recreated from getGraphicsRightColumnData() using company, selected-country and historical EU/global benchmarks, depending on the indicator.
Reconstructs the normal-distribution methodology underlying Sheet6 and Sheet9. Each independent resource or cost component uses the selected relative standard deviation. This is a fresh browser simulation, not a copy of Google's RAND() samples.
Not yet simulated.
The two processes are compared for the same final geometry. WAAM uses wire deposition and finishing; conventional CNC removes material from an initially larger steel stock.
Uses the category-specific characterization factors stored in the original spreadsheet. No normalization of different midpoint units is performed.
The interface is recreated from the published graphical design, shared Google Apps Script modules and the actual Google Sheets equations. Calculations run locally in your browser without a server, making this version deployable as a static site on GitHub Pages.
Cradle-to-gate inventory of HSLA steel wire, electricity, 82% Ar / 18% CO₂ shielding gas and CNC finishing. Single scores in ReCiPe eco-points; detailed midpoint factors are included for benchmarking.
Machine depreciation, maintenance, tooling, steel feedstock, electricity, shielding gas, finish machining and labour. Cost assumptions are editable to reflect a particular facility.
Cylindrical or cuboidal feedstock; subtractive machining time estimated using feed rate × width × depth of cut. Steel bar material, machining electricity, machine and operator costs are compared.
BR = π × wireDiameter² × wireFeedSpeed / 4t_dep = (V + S × a / 10) / (BR × 60)
t_cool = ceil(10 × h / layerHeight) × t_layer / 3600m_wire = 7.8 × (V + S × a / 10) / 1000
gas_m³ = gasFlow_Lmin × t_dep × 60 / 1000SEI = (m_wire × CF_wire + gas_m³ × CF_gas + E_WAAM × CF_electricity + E_finishing × CF_electricity) / 1000(assetCost + depreciationYears × annualMaintenanceAndTooling) / (depreciationYears × workingDays × shiftHours × shifts × availability)C = C_machine + C_material + C_electricity + C_gas + C_finishing + C_labourThis version reconstructs the parametric equations visible in the connected workbook, including its workbook-specific finishing energy approximation and treatment of cooling time. It does not execute the Google Sheets custom-menu Apps Script. The source contains inconsistencies between some legacy formulas, printed paper equations and cell labels; these are documented rather than hidden. The social screening assessment reproduces the accessible Sheet2 binary criteria; Monte Carlo uncertainty is implemented as a separate browser simulation rather than an exact reproduction of Google Sheets RAND() draws. Original chart mappings have been reconstructed from the provided Apps Script services and view templates. Country reference data are historical and should be updated before research or policy use.
The paper's scope is HSLA steel, with raw material and manufacturing phases only. Results should not be presented as verified product declarations without checking electricity-mix datasets, characterization factors, tool-path-specific energy, asset utilization, data provenance and sensitivity.
Kokare, S., Oliveira, J. P., and Godina, R. (2026). Advancing sustainable manufacturing decision-making through an online life cycle assessment tool for wire-based directed energy deposition. The International Journal of Advanced Manufacturing Technology, 143, 391–419. https://doi.org/10.1007/s00170-025-17351-4.