Purpose

This paper aims to optimize the process parameters of the fused deposition modelling (FDM) process using the Grey-based Taguchi method and the results to be verified based on a technique for order preference by similarity to ideal solution (TOPSIS) and analytical hierarchy process (AHP) calculation.

Design/methodology/approach

The optimization of process parameters is gaining a potential role to develop robust products. In this context, this paper presents the parametric optimization of the FDM process using Grey-based Taguchi, TOPSIS and AHP method. The effect of slice height (SH), part fill style (PFS) and build orientation (BO) are investigated with the response parameters machining time, surface roughness and hardness (HD). Multiple objective optimizations were performed with weights of w1 = 60%, w2 = 20% and w3 = 20%. The significance of the process parameters over response parameters is identified through analysis of variance (ANOVA). Comparisons are made in terms of rank order with respect to grey relation grade (GRG), relative closeness and AHP index values. Response table, percentage contributions of process parameters for both GRG and TOPSIS evaluation are done.

Findings

The optimum factor levels are identified using GRG via the Grey Taguchi method and TOPSIS via relative closeness values. The optimized factor levels are SH (0.013 in), PFS (solid) and BO (45°) using GRG and SH (0.013 in), PFS (sparse-low density) and BO (45°) using TOPSIS relative closeness value. SH has higher significance in both Grey relational analysis and TOPSIS which were analysed using ANOVA.

Research limitations/implications

In this research, the multiple objective optimizations were done on an automotive component using GRG, TOPSIS and AHP which showed a 27% similarity in their ranking order among the experiments. In the future, other advanced optimization techniques will be applied to further improve the similarity in ranking order.

Practical implications

The study presents the case of an automotive component, which illustrates practical relevance.

Originality/value

In several research studies, optimization was done on the standard test specimens but not on a real-time component. Here, the multiple objective optimizations were applied to a case automotive component using Grey-based Taguchi and verified with TOPSIS. Hence, an effort has been taken to find optimum process parameters on FDM, for achieving smooth, hardened automotive components with enhanced printing time. The component can be explored as a replacement for the existing product.

中文翻译：

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使用基于灰色的Taguchi和TOPSIS方法对汽车零部件进行熔融沉积建模过程的参数优化

目的

本文旨在使用基于灰色的Taguchi方法优化熔融沉积建模（FDM）工艺的工艺参数，并基于类似于理想溶液（TOPSIS）和分析层次工艺的顺序偏好技术对结果进行验证（ AHP）计算。

设计/方法/方法

工艺参数的优化正在开发强大产品方面发挥潜在作用。在这种情况下，本文介绍了使用基于灰色的Taguchi，TOPSIS和AHP方法对FDM工艺进行参数优化。利用响应时间加工时间，表面粗糙度和硬度（HD）研究了切片高度（SH），零件填充样式（PFS）和构建方向（BO）的影响。使用w1 = 60％，w2 = 20％和w3 = 20％的权重执行多目标优化。通过方差分析（ANOVA）可以确定过程参数相对于响应参数的重要性。根据灰色关联等级（GRG），相对亲和力和AHP指数值的等级顺序进行比较。响应表

发现

使用GRG（通过Taguchi方法）确定最佳因子水平，通过相对亲和力值（TOPSIS）确定最佳因子水平。优化的因子水平是使用GRG的SH（0.013英寸），PFS（固体）和BO（45°），使用TOPSIS相对接近度值的SH（0.013英寸），PFS（稀疏低密度）和BO（45°）。SH在使用ANOVA分析的灰色关联分析和TOPSIS中都具有较高的意义。

研究局限/意义

在这项研究中，使用GRG，TOPSIS和AHP对汽车零部件进行了多目标优化，在实验中它们的排名顺序相似度为27％。将来，将应用其他高级优化技术来进一步提高排名相似度。

实际影响

这项研究提出了汽车部件的情况，说明了实际意义。

创意/价值

在一些研究中，优化是在标准试样上进行的，而不是在实时组件上进行的。在这里，使用基于灰色的Taguchi将多目标优化应用于汽车零部件，并通过TOPSIS进行了验证。因此，已经努力寻找在FDM上的最佳工艺参数，以在增加印刷时间的情况下获得光滑，硬化的汽车零部件。可以探索该组件以替代现有产品。