*5.2. Experimental Standard*

The current approach to mechanical testing mainly refers to the relevant standards of raw materials and formed parts in their original application fields and utilizes existing standards. There are no specific guidelines for FDM process that prescribe the method of testing mechanical properties. This is one of the reasons that variety can be found when comparing experimental results from different authors. In the existing research, two standards are widely adopted: ASTM and ISO [2,162]. However, some of the standards are intended for materials containing high modulus fibers and are not directly applicable to samples made with FDM process. On the other hand, studies have shown certain composite standards actually improve test consistency on FDM materials [163]. Therefore, a suite of standard test methods should be developed to measure the mechanical property of parts by the FDM process. The authors hope researchers in related fields can work together to solve this urgent and important problem.

### *5.3. Multi-Parameters Optimization*

The properties of FDM built parts exhibit high dependence on process parameters and can be improved by setting parameters at suitable levels. Consequently, experimental approaches are usually adopted to obtain the optimal combination, including Taguchi design [164,165], fractional factorial design [166,167], full factorial design [168,169], facecentered central composites design (FCCCD) [26,79], along with analysis methods such as analysis of variance (ANOVA) [165,166] or signal-to-noise ratio (S/N) [164,170]. Furthermore, some researchers establish the mathematical model between response and parameters (e.g., response surface methodology (RSM) [171,172]) and optimize with various algorithms. For example, particle swarm optimization (PSO) [172,173], artificial neural network (ANN) [134], bacterial foraging optimization(BFO) [26], genetic algorithm (GA) [174], surrogate-based optimization [175], naked mole-rat algorithm (NMRA) [176], and other heuristic optimization methods [177].

Although these optimization methods have achieved satisfactory results, their applicability is limited to some specific problems. In addition, the optimal result may not be achievable in practice, restrained by the parameters setting of the FDM machine. Therefore, exploring new optimization strategies with high efficiency and broad applicability is an

attractive prospect. Besides, multi-objective optimization is a more challenging and complex topic [132,165,178], since the optimal result may correspond to multiple parameter combinations. Therefore, there is a need for more research efforts on multi-parameters optimization for the FDM process in the future.
