Full Factorial Design Of Experiments Method Research Articles

Comparative property analysis of fused filament fabrication PLA using fresh and recycled feedstocks

Manufacturing industries generate much waste, which can be converted into raw materials for other allied industries. Thermoplastic wastes can be recycled in many cases. Recently these recycled plastics have been used to manufacture the input filament necessary for material extrusion additive manufacturing (MEAM). Polylactic acid (PLA) is one of the most common and potentially useful engineering plastics that can be additively manufactured, but the effect of recycling on the effective properties is still not well known. In this study, the mechanical properties of MEAM-processed PLA were compared for virgin and recycled feedstock. The collected data were analyzed using a full-factorial design of experiments method, providing insight into the effects of layer thickness and infill density on the performance of the material before and after recycling. The results showed a notable degradation in properties after recycling, but this effect was moderated by modifying the studied parameters. This work and its conclusions will serve as a screening study to guide future efforts in this area and promote the wider use of recycled materials in additive manufacturing.

Optimized multi-axial loading to enhance chondrogenesis and limit proteoglycan loss in cartilage explants during bioreactor experimentation

Purpose: Osteoarthritis (OA) is a disease of the whole joint, with associated changes in the subchondral bone, synovium, bone marrow, muscles and ligaments, but with cartilage degeneration being the hallmark of OA. Mechanical loading is a key control signal in the regulation of the metabolic activity (i.e. homeostasis) of the articular chondrocytes, the unique cell population found in cartilage, embedded in their self-produced extra-cellular matrix (ECM). To study the effects of mechanical loading on the biological response of cartilage, explants can be loaded in bioreactors. Multi-axial loading bioreactors that combine compressive with torsional loading can mimic the loading of articular cartilage. This study aims at informing bioreactor protocol development to define mechanical parameters that optimize the biological response of cartilage explants during multi-directional loading experiments. Indeed, the local biological response may vary from chondrogenesis in some parts to proteoglycan loss in others depending on the mechanical parameters. This study uses a finite element method to investigate the effects of multi-axial loading characteristics on the local mechanical environment (stresses and strains) of the explants while accounting for material properties of the non-fibrillar matrix of cartilage explants. The mechanical environment is then related to chondrogenesis (based on maximum compressive principal strain (MCPS) - Zahedmanesh et al., 2014) and proteoglycan loss (based on the fluid velocity (FV) - Orozco et al., 2018). Methods: The cartilage explant was modelled as a 3D cylinder of 8mm diameter and equal height in Abaqus. A fiber-reinforced poroelastic (FRPE) constitutive model (Ebrahimi et al., 2019) was used to define the material, in which the non-fibrillar matrix of the cartilage explant was modelled as a porous hyperelastic (Neohookean) material saturated with fluid (water) and the collagen fiber network was modelled as nonlinear elastic material that can only withstand tensile loading. Four families of organized collagen fibrils (primary fibrils) and 13 families of randomly oriented fibrils (secondary fibrils) formed the collagen network. Ratio of density of the primary to the secondary fibrils was set to 12.16. The bottom surface of the explant was fully constrained and sinusoidal (frequency of 1Hz) unconfined compression and torsion loading were applied to the top surface. Free fluid flow was allowed through the lateral surface. The initial and strain-dependent fibril network moduli were respectively set to 0.41 and 15.42 MPa and the constant for displacement dependence of permeability in the non-fibrillar matrix was set to 3.36 (Ebrahimi et al., 2019). The sensitivity of the maximum values of FV and MCPS was studied at 9 key locations (see figure a), whereby the material parameters of the non-fibrillar matrix and loading range were varied. This resulted in five input parameters: elastic modulus (E=[0.15,0.6]MPa), Poisson’s ratio (nu=[0.1,0.45]), initial permeability (k0=[0.4,1.6]×10-15m4/Ns), compressive strain (Comp=[5,20]%) and torsion loading (Tor=[5,20]°). A full-factorial design of experiment method was used and a first-order polynomial surface including the interactions fitted the responses. Results: MCPS varied between 8.4% and 36.5% and is independent of the material properties of non-fibrillar matrix (E, nu and k0) but has a high and moderate dependency on Compressive (Comp) and torsion loading (Tor) magnitudes, respectively (figure b). The maximum value of MCPS always occurs at the bottom edge, i.e. at the distal cartilage bone interface (point 7 in figure a). FV varies between 1.2×10-7 mm/sec and 4.8×10-5 mm/sec and dominantly depends on initial permeability (k0) and compressive loading (Comp), while being independent of Poisson’s ratio (nu) and slightly dependent on elastic modulus (E) and torsion loading (Tor) (figure c). The maximum value of FV always occurs at the center of the side surface (point 4 in figure a). Conclusions: The parameter sensitivity analysis Results show that by increasing compression and torsion loading within the bioreactor the chondrogenesis (which depends on MCPS) may increase, independent of the material properties studied. Therefore, within the selected ranges studied here, a decrease in material stiffness (softening of the cartilage) due to OA won’t affect the chondrogenesis in cartilage explants. Based on these results, if enough high-quality chondrocyte cells are provided in a cell-seeded implant-cartilage unit, chondrogenesis will increase by increasing the compression and torsion loadings applied within the bioreactor up to at least 20% and 20°, respectively. On the other hand, increasing compression loading will increase FV, thereby degenerating the extracellular matrix through proteoglycan loss. Also, increasing initial permeability (k0) will increase FV under loading, while FV is independent of elastic modulus (E), Poisson’s ration (nu) and torsion loading (Tor). Therefore, to limit proteoglycan loss and enhance chondrogenesis in cartilage explants during bioreactor experiments, compression loading might be decreased (to control FV ) but torsion loading increased (to increase MCPS). In future, follow-up biological studies are needed to test the added value of these model-informed bioreactor protocols.

Coiling and deploying dynamic optimization of a C-cross section thin-walled composite deployable boom

A C-cross section thin-walled composite deployable boom (C boom) can be flattened and coiled elastically. Furthermore, C boom can be deployed by releasing stored strain energy. Finite element (FE) models of C booms are constructed based on a nonlinear explicit dynamics analysis. The full simulation is divided into six consecutive steps: flattening, end-compacting, releasing, coiling, holding, and deploying around a hub. An optimal design method for the coiling and deploying of the C boom is presented based on the response surface method (RSM). Twenty-seven sample points are obtained by using a full-factorial design of experiment method. Surrogate models of the maximum moment and stress during the fully simulated process, including the mass of the C boom, are created by the RSM. The maximum moment and mass are set as objectives, and the maximum stress is set as a constraint to increase deploying statue stiffness and enhance use times. A multi-objective optimization design of the C boom is performed by sequential quadratic programming algorithm. Lastly, FE models for the optimal design are built to validate the accuracy of the optimization and the response surface results.

A Self-propagating high-temperature synthesis process for the fabrication of Fe(Cr)–Al2O3 nanocomposite

Self-propagating high-temperature synthesis (SHS) was used to fabricate a Fe(Cr)-Al2O3 nanocomposite. The composite was fabricated by the reactions between the powders of Fe, Fe2O3, Cr2O3, and Al. The effect of blending ratio and mechanical activation of the initial powders and the precursor compressing pressure on the microstructure of the final product was studied by optical microscopy, scanning electron microscopy, transmission electron microscopy, and X-ray diffraction. The significance of the effect of each of the aforementioned parameters on the quality of the composite (assessed by measuring the compressive strength and wear resistance) was determined using a full-factorial design of experiments method. The results showed that the best molar powder ratio that produced the most homogeneous product through a sustainable SHS reaction was Fe:Fe2O3:Cr2O3:Al = 10:1:1:4. A lower Fe content caused the Fe(Cr) phase to melt and separate from the rest of the materials.

Multi-Objective Accelerated Process Optimization of Part Geometric Accuracy in Additive Manufacturing

The goal of this work is to minimize geometric inaccuracies in parts printed using a fused filament fabrication (FFF) additive manufacturing (AM) process by optimizing the process parameters settings. This is a challenging proposition, because it is often difficult to satisfy the various specified geometric accuracy requirements by using the process parameters as the controlling factor. To overcome this challenge, the objective of this work is to develop and apply a multi-objective optimization approach to find the process parameters minimizing the overall geometric inaccuracies by balancing multiple requirements. The central hypothesis is that formulating such a multi-objective optimization problem as a series of simpler single-objective problems leads to optimal process conditions minimizing the overall geometric inaccuracy of AM parts with fewer trials compared to the traditional design of experiments (DOE) approaches. The proposed multi-objective accelerated process optimization (m-APO) method accelerates the optimization process by jointly solving the subproblems in a systematic manner. The m-APO maps and scales experimental data from previous subproblems to guide remaining subproblems that improve the solutions while reducing the number of experiments required. The presented hypothesis is tested with experimental data from the FFF AM process; the m-APO reduces the number of FFF trials by 20% for obtaining parts with the least geometric inaccuracies compared to full factorial DOE method. Furthermore, a series of studies conducted on synthetic responses affirmed the effectiveness of the proposed m-APO approach in more challenging scenarios evocative of large and nonconvex objective spaces. This outcome directly leads to minimization of expensive experimental trials in AM.

One Step at a Time

This article reviews that almost every engineer is involved in some way in the planning of experiments, whether they are laboratory experiments, field tests, or computer simulations. A set of techniques called Design of Experiments (DOE) has proved to be an extremely useful methodology for enhancing the effectiveness of that planning. Six Sigma quality programs have been a mechanism for promulgating DOE in industry, but new research shows that a complementary approach, based on a set of adaptive one-factor-at-a-time experiments, leads to better results under many conditions. The full-factorial DOE method, which was initially developed to study agriculture, sets up an experiment for each possible combination of the factors that need to be tested. For example, consider the development of a new clutch. One aspect of its performance is drag torque—the amount of torque the clutch transmits when it is disengaged. A performance goal is to minimize drag torque by appropriate selection of materials, geometry, and other parameters. DOE is a remarkably successful procedure, which has had a profound influence on the professional practice of engineering.