Sustainable design guidelines for additive manufacturing applications

Abstract

Purpose Additive manufacturing (AM) technologies have tremendous applications in industries owing to their unique advantages. Sustainable AM (SAM) is gaining significance because of lightweight structures, lattice geometries and customized parts for industrial applications. To facilitate design for SAM, design guidelines from AM and environment viewpoints are to be analyzed. In this context, this paper aims to present the analysis of SAM guidelines. Design/methodology/approach This work divides 26 identified SAM guidelines into four categories. Grey axiomatic design was used to calculate the weights of guidelines categories. Further, the grey technique for order preference by similarity to ideal solution was used as a solution methodology to prioritize the SAM guidelines. Findings The top identified guidelines are “Design for reusability” and “Optimize part orientation for build time and roughness.” Implementing proper design guidelines leads to many sustainable benefits such as minimum material consumption, energy consumption and emissions. Originality/value This study would facilitate Am product designers to deploy prioritized guidelines for enhancing the effectiveness of the additively manufactured product with sustainability benefits. The prioritized guidelines would guide the AM product designers to take maximum advantage of the AM process by deploying design for AM and design for environment guidelines. This study contributed a structured approach for design engineers and practitioners to deploy guidelines during the early stages of product design to ensure AM feasibility with minimal environmental impact.

Multi-Attribute Decision-Making Methods in Additive Manufacturing: The State of the Art

“…The ranking results show that training towards sustainable AM benefits and limited materials recycling potential are significant challenges. Agrawal [52] presented an approach to analyse the sustainable design guidelines for AM applications. In this approach, twenty-six guidelines are identified and divided into four groups.…”

“…To solve the MADM problems in AM, a large number of MADM methods have been applied in recent decades. The MADM methods used mainly include aggregation operators (AOs) [11][12][13][14][15][16][17][18][19][20][21][22], analytical hierarchy process (AHP) , technique for order of preference by similarity to an ideal solution (TOPSIS) [44][45][46][47][48][49][50][51][52][53][54][55][56], deviation function (DF) [57,58], fuzzy synthetic evaluation (FSE) [59,60], graph theory and matrix approach (GTMA) [61], multi-objective optimisation by ratio analysis (MOORA) [62], knowledge value measuring (KVM) [63], vlsekriterijuska optimizacija i komoromisno resenje (VIKOR) [64][65][66], complex proportional assessment (COPRAS) [67,68], analytical network process (ANP) [69], axiomatic design (AD) [70], elimination et choix traduisant la réalité (ELECTRE) [71], preference selection index (PSI) [72,73], best-worst method (BWM) [74,…”

Effect of Combined Different Sources of Alumina Silicate on Mechanical Properties and Carbonation Depth of Environmentally Friendly Geopolymeric Composite Based on Metakaolin

“…Computer prediction techniques can reduce time and material. Furthermore, classical programming can transfer equations to commuter programs, and algorithms to find the precise outcomes [23][24][25][26][27][28][29][30][31] Topçu and Saridemir [32] investigated the prediction of concrete mixture with extra fly ash through ANNs and FL techniques separately. They concentrated on aggregate diameter to forecast the mechanical properties of concrete.…”

Prediction of the Mechanical Properties of Basalt Fiber Reinforced High-Performance Concrete Using Machine Learning Techniques

“…On the other hand, machine; earning (ML), as a sub-field of artificial intelligence, has attracted attention in various industries, such as the medical, mechanical, and manufacturing industries [ 21 , 22 , 23 ]. ML has been greatly applied to the material sciences and civil engineering within the past decade, mainly for the prediction of the mechanical properties of different types of concretes [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ].…”