Cast Parts Research Articles

Assessing Cast Aluminum Alloys with Computed Tomography Defect Metrics: A Gurson Porous Plasticity Approach

Aluminum alloys have inherent tendencies to produce casting defects caused by alloying or metal melt flow inside the mold. The traditional detection method for these defects includes reduced pressure tests, which assess metal quality in a destructive manner. This leaves a gap between metal quality assessments and tensile test correlations. Computed tomography (CT) scans offer crucial assistance in evaluating the internal quality of castings without damaging the structure. This provides a valuable opportunity to couple mechanical tests with numerical methods such as finite element analysis to predict the mechanical performance of the alloy. The present study aims to evaluate the internal quality of cast aluminum alloys using CT scans and to correlate the defect metrics obtained from CT scans with mechanical test results. The Gurson-type material model and finite element methodology have been used to validate the correlation studies. Therefore, we propose a more holistic approach to predicting the behavior of metals by coupling damage models with CT scans and mechanical tests. The study investigates several CT metrics such as the defect volume, total defect surface, biggest defect surface, and projected area of defects. The conclusion reveals that CT scans provide crucial assistance in evaluating the internal quality of castings, and CT defect metrics can be used to build correlations between mechanical tests and CT evaluations. The study also suggests that the concept of adjusted representative material yield parameter (ARMY) or computed representative material yield parameter (CRMY) can be used to correlate CT metrics with mechanical strength in cast materials and parts for a given aluminum alloy. Overall, the study proposes a more comprehensive methodology to assess the quality of cast aluminum alloys and couple the quality to mechanical performance.

Ex-Ante Eco-Efficiency Assessment of Dendromass Production: Conception and Experiences of an Innovation Project

Rising demand for bio-based products exerts growing pressure on natural resources such as wood. The agricultural technique of short rotation coppice (SRC) to produce dendromass from fast growing trees has gained relevance to ease the pressure of demand for wood from forests. The European-funded project Dendromass4Europe supports the establishment of SRC in Western Slovakia and its biomass use for four new bio-based materials, in the context of a developing bioeconomy. Along with the development of the SRC and material value chains, eco-efficiency assessment is carried out to anticipate areas of critical concern and derive measures for improvement. Among many other assessment approaches, eco-efficiency assessment was conducted by combining Life Cycle Assessment (LCA) and socio-economic assessment to optimize the whole production system towards eco-efficiency. An LCA study provided necessary data to assess the environmental performance. The added value and revenue results from the socio-economic assessment were used to assess the economic performance. The value chain of the moulded fibre parts shows the best eco-efficiency value, but only because its economic performance is comparatively strong. The lightweight boards have the least environmental impact but do not perform so well in terms of eco-efficiency. The overall eco-efficiency of the full value chain can be optimized only with a specific combination of all four dendromass-based materials within the whole production system.

Experimental Analysis on Polypropylene Moulded Part for Performance of Laser Printing

Now a day plastic injection molding is widely used process to manufacture engineering product and consumer goods typically thermoplastic is combined with rubber or another thermoplastic like RM master batch is used to add color to the Molded part also the laser marking on plastic part and their printing cut, faint issue observed. The aim of the study was to Optimize the injection parameters and processing condition for the laser performance on polypropylene part. To achieve enhanced dark laser marking on polypropylene, the process parameter of plastic injection molding and the Raw material master batch mixing parameter successfully prepared this laser–sensitive composite consisted of a high Laser induced carbonization rate.in plastic injection molding use of raw material and mixing of master batch is considerable factor for faint laser marking. Because of laser does not respond well on carbonization added material, it was evident that master batch having the carbon properties is much more responsible for faint laser marking issue on polypropylene material part. The effect of laser beam interaction (Nd: YVO4) with selected operational parameters on the Quality of graphical features obtained on the surface of polypropylene-molded pieces with different surface Textures (variable parameters of the surface layer). Polypropylene test specimens were injection molded using original injection molds Products with variable end parameters determined by the position of the cavity circle can be identified. The layout of the laser function, the beneficiary texture of the molded piece, the molded color and the support of the marking piece allow the evaluation of the graphic symbol performance by means of laser marking of the type of master back in their rendering relationship. Marked evaluation criteria were adopted for the project.

Possibility of infrared (IR) thermography camera to investigate properties of injected plastic product and optimisation injection process parameter using a design of experiment

ABSTRACT The injection processing parameters have an important role in product quality. The purpose of this research is to estimate the usage of infrared (IR) thermal camera in automatic quality control of polymer parts in the production lines compared to simulation. The evaluation of injected part properties monitored from an IR camera is studied in terms of the defect and mechanical properties. The optimisation of the processing parameters to increase mechanical properties and decrease injection defects is examined. There are four processing factors with four levels namely, melt temperature, mould temperature, injection pressure, and flow rate designed by using Taguchi’s experimental design. The effect of average temperature of injection moulded parts monitored by IR camera on defect and mechanical properties is determined. The variance (ANOVA) method is used to evaluate the influence of each factors. The parameter factors of all experiments are definitely different because of P-values less than 0.05. The optimum processing parameters of PP were a melting temperature of 193°C, mould temperature of 30°C, injection pressure of 50 bar, and flow rate of 40 cm3/s. The suitable processing conditions for ABS are melting temperature, mould temperature, injection pressure, and flow rate of 216°C, 50°C, 90 bar, and 20 cm3/s respectively. IR thermograph accurately monitors short shot, sink mark, and large flash in moulding part; moreover, IR camera can estimate the mechanical properties of ABS by average temperature but cannot accurately measure the mechanical properties of high glossy PP. It is meant that IR thermal imaging technique can automatically control the product quality in the injection moulding process.

Advanced manufacturing concept of a bio-inspired reaction wheel rotor for small- and medium-sized constellation satellites

The increasing number of constellation satellites requires re-thinking of the design and manufacturing process for reaction wheel rotors. Mass optimisation of reaction wheel rotors leads to cost reduction and performance increase. Those optimisations can be realised by taking ideas from nature. Therefore, design principles of diatoms were screened, abstracted and implemented in an algorithm-based design process. In this way, a bio-inspired rotor was created, which considers launch and in-operation loads, is capable of up to 7500 RPM and shows a compact design with a diameter of 282,text {mm}. Regarding mechanical performance, an energy density of 4661,text {J kg}^{-1} and a mass moment of inertia ratio of 0.7584, which considers the component and an idealized design, could be achieved. Compared to a commercial rotor, this is equivalent to a similar inertia ratio and +85 % energy density, but +44 % mass due to manufacturing restrictions. Based on different boundary conditions, different first natural frequency for launch and operation conditions were obtained (658,text {Hz} and 210,text {Hz}). The new design was cast from nano-reinforced aluminium alloy (AlSi10Mg + Al2O3) in 3D-printed sand moulds that were produced via binder-jetting process. Thus, a hybrid manufacturing process was used, by combining additive manufacturing and casting. Post-processing of the cast part via turning and milling was performed to compensate distortion and achieve the required surface quality. Preliminary vibration measurements were performed, showing a large need for balancing to achieve low vibration emissions.

Analysis of Tribodegradation Factors Limiting the Life of the Molds

The paper presents the results of research focused on the analysis of mold wear for high-pressure casting of aluminum alloys. The functional parts of the molds were taken out of operation. Molded parts for aluminum alloy casting molds and cores are made of tool chrome and chromium-molybdenum steels. In the die-casting process, the mold parts and cores are exposed to intense thermal, mechanical, and chemical loads. High melt flow rates of aluminum alloys (up to 120 m.s-1), high pressures (up to 120 MPa) and high maximum surface temperatures of mold parts (up to 550 °C) lead to erosion, abrasion, corrosion, and thermal fatigue of molds. The thermal load of the foundry cores is even higher (up to 600 °C) because they are not connected to the mold cooling system. Thermal cyclic loading from 80 °C to 550 °C leads to high tensile stresses on the surface of the molded parts / cores and consequently to the formation and propagation of thermal cracks. Frequent contact of the surface of the mold part with the melt causes the formation of growths (die soldering) due to corrosion caused by molten metals and consequently shortens the life of the mold parts and cores. Light and electron microscopy was used for mold analysis. Every degrading change in the shape of molds and cores will also affect the quality and dimensions of the castings.

Influence of debinding parameter and nano-ZrO2 particles on the silica-based ceramic cores fabricated by stereolithography-based additive manufacturing

The silica-based ceramic core is an essential part of the investment casting of hollow turbine blades due to their excellent thermal stability and easy demoulding. In this study, the green bodies of silica-based ceramic cores with nano-ZrO2 were fabricated by stereolithography (SLA). Firstly, the effects of the burying process and debinding heating rates on the performance and microstructure of silica-based cores were investigated. The results revealed that the burying powders provide support and a stable thermal field for the SLA ceramic cores during debinding and sintering. Ceramic cores with smaller shrinkage and higher apparent porosity were produced during the burying process at a heating rate of 0.5 °C/min. In addition, the influence of nano-ZrO2 content on the mechanical properties of silica-based ceramics was investigated. Nano-ZrO2 particles can improve bulk density and mechanical properties by promoting sintering. Additionally, it can reduce the cracks on the surface of ceramic samples. The optimal content of nano-ZrO2 should be between 1.5 and 2 wt%, producing silica-based cores with low shrinkage rate (2.91–3.19%), high apparent porosity (27.11–27.97%) and high flexural strength at room temperature (24.51–24.65 MPa) and high temperature (27.81–29.69 MPa). This work provides promising ways to fabricate high-quality ceramic cores for turbine blade application.

Rapid tooling: investigation of soft-tooled micro-injection moulding process characteristics using in-line measurements and surface metrology

PurposeThe purpose of this study is to demonstrate and characterise a soft-tooled micro-injection moulding process through in-line measurements and surface metrology using a data-intensive approach.Design/methodology/approachA soft tool for a demonstrator product that mimics the main features of miniature components in medical devices and microsystem components has been designed and fabricated using material jetting technique. The soft tool was then integrated into a mould assembly on the micro-injection moulding machine, and mouldings were made. Sensor and data acquisition devices including thermal imaging and injection pressure sensing have been set up to collect data for each of the prototypes. Off-line dimensional characterisation of the parts and the soft tool have also been carried out to quantify the prototype quality and dimensional changes on the soft tool after the manufacturing cycles.FindingsThe data collection and analysis methods presented here enable the evaluation of the quality of the moulded parts in real-time from in-line measurements. Importantly, it is demonstrated that soft-tool surface temperature difference values can be used as reliable indicators for moulding quality. Reduction in the total volume of the soft-tool moulding cavity was detected and quantified up to 100 cycles. Data collected from in-line monitoring was also used for filling assessment of the soft-tool moulding cavity, providing about 90% accuracy in filling prediction with relatively modest sensors and monitoring technologies.Originality/valueThis work presents a data-intensive approach for the characterisation of soft-tooled micro-injection moulding processes for the first time. The overall results of this study show that the product-focussed data-rich approach presented here proved to be an essential and useful way of exploiting additive manufacturing technologies for soft-tooled rapid prototyping and new product introduction.

COMPARISON OF FINISHING MILLING STRATEGIES USING TOPOGRAPHY OF THE MACHINED SURFACE

The plastics processing and injection molding industry is known for the fact that the future molded part contains a variety of geometric shapes and, in some cases, free surfaces. To produce these shapes, knowledge of CAM systems is required to enable the programmer to select the necessary milling strategies designed to achieve the best possible quality and dimensional accuracy. However, it is equally necessary to understand and appreciate the effect of each strategy and which strategy is best suited for a given type of surface. The paper compares finishing milling strategies by evaluating the topography of the machined surface. The material was AlCu4Mg aluminium alloy, where Constant-Z and Spiral strategy – circle-type finishing strategies were selected for the production process. Surface topography analysis was evaluated and compared at three different heights of the produced part with respect to the tool contact with the machined surface, which showed a variation in toolpaths and therefore also in the quality of machining. The surface topography results demonstrated the Constant Z strategy to be the more suitable strategy for producing the shaped surface, which achieved uniform toolpaths over the whole height of the part.

The Optimization of working parameters to improve the quality of plastics in an injection molding process

Improvement is required in any industry to increase the productivity by reducing the defects rate and remove the overall waste produced during manufacturing process. In this research paper, the working parameters are optimised that are responsible for process variation. A plastics manufacturing company, the major problems were flow marks and air bubbles appeared on the surface of mould part a car side mirror plastic cover. Because of these problems, the overall production of company was decreasing that resulted in customer dissatisfaction and company could not achieve its daily production target. The responsible working parameters for process variation are found to be injection pressure, melting temperature along with viscosity and flow rate of molten material during injection moulding process. The injection speed and screw speed are also considered. The analysis of variance (ANOVA) test was conducted to find most critical working parameters. It is identified that response surface methodology (RSM) can be used to optimise the working parameters to improve the quality of moulded part that will increase process efficiency by 50%. The data was collected from four machines to find which machine is causing more problems that is found to be Tederic 450 tone machine. This machine has more problems as compared to other machines. Acrylonitrile Butadiene Styrene (ABS) material is used for the production of car side mirror plastic cover. The Rejection of this moulded part was counted 35% of total rejection. After optimization, the rejection is reduced to 16% is recorded which is significant improvement.

Geochemical fractionation, bioavailability, ecological and human health risk assessment of metals in topsoils of an emerging industrial cluster near New Delhi.

Urban spaces have become sink for metal-rich waste, particularly in unorganized industrial clusters and metro-cities. Geochemical distribution of metals in different forms and their mobility and bioavailability in topsoils of Bhiwadi Industrial Cluster (BIC) near New Delhi are studies following m-BCR-SEP. Contamination factor (Cf), risk assessment code (RAC), ecological risk assessment (Er), and carcinogenic and non-carcinogenic health risk (HRA) were calculated to assess health and environmental risks. Residual fraction (F4) contained considerable amounts of Cd (57.2%), Cr (81.5%), Fe (86.1%), Mn (62.5%), Ni (58.3%), and V (71.4%). Pb was present in reducible fraction (F2; 52.8%), whereas Cu was distributed in F2 (33.3%) and F4 (31.6%). Zn showed equal distribution in acid exchangeable (F1; 33.9%) and oxidizable fraction (F3; 32.5%). High Cf was observed for Zn (0.9-20.9), Cu (0.46-17) and Pb (0.2-9.9). RAC indicated high risk of Cd, Cu, Mn, Ni, and Zn due to their high mobility and toxicity. High potential bioavailability of Cu, Pb, and Zn (> 65%) was found in samples collected near to metal casting, electroplating, and automobile part manufacturing industries. Considerable to extremely high ecological risk was observed for Cd, low to high risk for Cu, low risk to moderate risk for Cr, Mn, Ni, Zn, and Pb. All topsoil samples were in low to very high-risk range for metals. Ingestion was major pathway of metals followed by dermal and inhalation. Children were more prone to non-carcinogenic risks (hazardous index: 3.6). Topsoils had high carcinogenic risk to exposed population for Cd, Cr, Ni, and Pb.

Complex parts machining path planning through cross-source point cloud processing

When cleaning cast parts, production machinery such as robots and smart machines must be able to operate tools along appropriate machining paths. Planning the machining path using 3D point cloud processing techniques is a suitable approach. The local point clouds generated from in situ scans and the standard point clouds, however, differ in scale and accuracy, and these issues make the registration of cross-source point clouds a difficult academic topic. The study presented here suggests a novel cross-source point cloud registration technique that combines a point cloud slicing algorithm and a curve fitting algorithm to plan machining paths for complex cast parts. The results demonstrate the practicality of the proposed method, with the uniformity of the surface profile and the operational effectiveness of the machining process being 57.90% and 67.74% higher, respectively, for the machining paths generated using the proposed method compared to the manual method. By comparing trials of various machining path curve fitting algorithms, we find that the Fourier fitting approach performs best. In addition, the co-planar features chosen for coarse registration will be better registered if they are closer to the plane containing the intended machining path. The proposed method may be useful for online inspection by UAVs and interactive robots.

Additive Design Mimics the Strength and Architect of Nature

Abstract: It is believed that the best parts’ design and performance is that which mimics the creation of nature. Palm is a very good example of an extra ordinary tree which must attract the attention of engineers and designers. In contrast with other types of trees, palms have a vascular, jumble, and spongy tissue stem instead of a wooden one. This is the reason why palm trees can with stand strong hurricanes while trees cannot. The stem of a palm is composed of three main parts. Those are the main stem body, the central core, and the leaves growing from the central core in circular and axial patterns along the core. This natural combination tissues of different construction provides an extra-flexibility to the main stem, enhance the relative movement of these components, and as a result enables the palm to bend massively without any catastrophic fracture. This exceptional construction inspired the author to design and manufacture a part which mimics the most public tree at the home country. The model was additively manufactured from 316L, tested for bending with and without heat treatment, and compare with cast part of similar material and dimensions. The aim was always to achieve improved mechanical properties and performance of AM parts with complex geometry.

Automated 3D Defect Detectionm based on Simulated Reference

Bringing the Computed Tomography (CT) technology into production lines brings many advantages due to the complete and detailed 3D characterization and localization of defects. But compared to laboratory systems, Inline-CT systems must meet several challenges, the most significant one is certainly the limited available time for the measurement procedure and data evaluation. Visual inspection of 3D data sets of complex parts within the production rate is almost impossible, so automated image evaluation algorithms are mandatory for that task. Fast measurement procedures often mean short integration times and a lower number of projections which are two factors that increase quantum noise and thus decrease image quality significantly. In addition to that, scatter artifacts also have a negative influence on the image quality. All these conditions constitute challenges to the image evaluation algorithm. The Fraunhofer EZRT has developed a method for automated defect detection in cast parts by using reference (that means flawless) parts for comparison. The advantage of this method is that image artifacts are considered, since they appear in both data sets in the same way and have almost no negative influence on the reliability of the evaluation result. Most recent work took this method one step further by using not real flawless parts, but instead using simulations to generate reference data sets in a very convenient and quick way for any number of different parts.

Prediction of weld-line width and sink-mark depth of plastic injection moulded parts using neural networks

Fast-moving consumer goods companies rely on thermoplastic materials like polymethyl methacrylate (PMMA) for producing large quantities of affordable packaging products. In the current study, the experimental data of plastic injection moulding was collected from existing literature. It was found that the process parameters of the plastic injection moulding process influence the defects like weld-line and sink-mark on PMMA products. Therefore, an Artificial neural network model (ANN) was developed to estimate the permissible range for these defects. The 8–11-11–2 architecture was chosen because it yielded the least values of mean square error (MSE) and average error (AE) for training, testing and validation datasets. The predicted results were compared with experimental results to determine the accuracy of the developed model. Using performance parameters and various visual illustrations, the model has been validated. The obtained correlation coefficient (R-value) of 0.99954 signifies that the ANN model has good compatibility with the experimental dataset. This can be demonstrated by the precise prediction values produced by the ANN model. Therefore, the deterioration of mechanical properties of PMMA material can be minimized as the ANN model succeeded in predicting optimal weld-line width and sink-mark depth during the plastic injection moulding process.

AI-Powered Multi-Class Defect Segmentation in Industrial CT Data

Industrial computed tomography is a key technology for non-destructive testing for many applications. The growing demand of automated testing requires an efficient automated data analysis. In this work we propose a solution using a neural network for voxel-accurate segmentation of 4 relevant defect classes in industrial casting parts and additively manufactured parts. Defect classes are pores, cracks, inclusions and pore nests. The network was trained and evaluated with a dataset of 20,000 samples. We used a modified U-Net architecture originally developed for medical image segmentation which we optimized for our industrial CT-Data and the multi-class segmentation task. In this work we show that we were able to achieve promising results of ~82 % MeanIoU score and individual defect class MeanIoU values of up to 95 % segmentation performance and qualitatively a high classification performance.

Investigating the failure behavior of cast Al-11Ce-0.4Mg alloys using in-situ scanning electron microscopy tensile testing

Within the last decade, research on Al-Ce-Mg alloys has reported promising results for use in cast part applications. In this paper, the failure behavior of cast Al-11Ce-0.4Mg (wt%) was investigated experimentally with focus on the effect the matrix and intermetallic phases have on the fracture propagation behavior at failure. For the first time, in-situ SEM tensile testing was used to study the failure behavior of cast Al-Ce alloys, reporting results for uniaxial, DIC, and single edge notch tensile tests. The results of the in-situ SEM tensile testing were compared with the materials characterization experiments, which included serial sectioning, EBSD, EDS, and fractography. Analysis of EBSD and EDS mapping of cast Al-11Ce-0.4Mg showed that the cast microstructure was a hypereutectic two phase Al-Ce alloy with grains encompassing large complex colonies of laminar eutectic Al11Ce3 intermetallic. The uniaxial tensile results reported the effect casting defects have on the strength and ductility of the alloy, and DIC in-situ testing showed that the eutectic colonies plastically deform less than the matrix phase. In-situ SEM single edge notch tensile testing displayed how the strength of an individual phase affected the crack propagation direction in the alloy. The results of both the materials characterization and in-situ tensile testing experiments on the failure of this alloy revealed further directions for future alloy development that can improve both the strength and fracture toughness of Al-Ce-Mg alloys.

Application of Machine Learning for Prediction and Process Optimization—Case Study of Blush Defect in Plastic Injection Molding

Injection molding is one of the most important processes for the mass production of plastic parts. In recent years, many researchers have focused on predicting the occurrence and intensity of defects in injected molded parts, as well as the optimization of process parameters to avoid such defects. One of the most frequent defects of manufactured parts is blush, which usually occurs around the gate location. In this study, to identify the effective parameters on blush formation, eight design parameters with effect probability on the influence of this defect have been investigated. Using a combination of design of experiments (DOE), finite element analysis (FEA), and ANOVA, the most significant parameters have been identified (runner diameter, holding pressure, flow rate, and melt temperature). Furthermore, to provide an efficient predictive model, machine learning methods such as basic artificial neural networks, their combination with genetic algorithms, and particle swarm optimization have been applied and their performance analyzed. It was found that the basic artificial neural network (ANN), with an average accuracy error of 1.3%, provides the closest predictions to the FEA results. Additionally, the process parameters were optimized using ANOVA and a genetic algorithm, which resulted in a significant reduction in the blush defect area.

Chemical Speciation, Bioavailability and Human Health Risk Assessment of Metals in Surface Dust from an Industrial Cluster in India

In this study, distribution of metals in different geochemical forms, their mobility and bioavailability in bulk surface dust samples of Bhiwadi industrial cluster (BIC) in Rajasthan, India, was assessed by modified Community Bureau of Reference (m-BCR) sequential extraction procedure. Potential risk of metals in surface dust to environment and human health was evaluated using Contamination factor (Cf), Mobility Factor (MF) and Risk Assessment Code (RAC), and carcinogenic and non-carcinogenic health risk. Residual fraction contained significant amount of metals as Cd(55.86%), Cr(86.05%), Fe(90.06%), Mn(69.94%), Ni(66.08%), and V(71.80%). Pb(52.43%) was present in reducible fraction, while Cu was equally distributed in reducible (27.66%) and oxidizable (28.20%) fractions. Zn was equally distributed in acid exchangeable (33.15%) and reducible (35.01%) fractions. High Cf values were observed for Zn (1.32-16.98), followed by Pb (0.38-11.23) and Cu (0.26-8.22). RAC indicated high risk of Cd, Mn, Ni and Zn to environment due to their high mobility and toxic nature. Zn, Pb, Cu and Cd showed highest mobility (potential bioavailability) in samples collected around metal casting, electroplating, and automobile part industries. Data indicated that metals can bio-available with the changes in redox conditions in environment. Ingestion was major pathway for carcinogenic and non-carcinogenic health risks followed by dermal and inhalation. Hazardous Index value (6.32) indicated higher susceptibility of children for non-carcinogenic risk as compared to adults. Carcinogenic risk of Cr, Cd, Ni and Pb was higher than acceptable levels in surface dust, suggesting a high risk of cancer to exposed population.

Strategic cost and sustainability analyses of injection molding and material extrusion additive manufacturing

AbstractEconomic and environmental costs are assessed for four different plastics manufacturing processes, including cold and hot runner molding as well as stock and upgraded material extrusion three dimensional (3D) printers. A larger stock 3D printer was found to provide a melting capacity of 14.4 ml/h, while a smaller printer with an upgraded extruder had a melting capacity of 36 ml/h. 3D printing at these maximum melting capacities resulted in specific energy consumption (SEC) of 16.5 and 5.28 kWh/kg, respectively, with the latter value being less than 50% of the lowest values reported in the literature. Even so, analysis of these respective processes found them to be only 2.9% and 3.8% efficient relative to their theoretical minimum energy requirements. By comparison, cold and hot runner molding with an all‐electric machine had SEC of 1.28 and 0.929 kWh/kg, respectively, with efficiencies of 9.9% and 13.6% relative to the theoretical minima. Breakeven analysis considering the cost and carbon footprint of mold tooling found injection molding was preferable at a production quantity of around 70,000 units. Parametric analysis of model inputs indicates that the breakeven quantities are robust with respect to carbon tax incentives but highly dependent on mold costs, labor costs, and part size. Dimensional and mechanical properties of the molded and 3D printed specimens are also characterized and discussed.