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Sustainable materials are essential for next generation infrastructures that not only improve their functionality but also have minimal impact on the environment. The application of biochar has been proposed for thermally active structures owing to its lower thermal conductivity ( K ) and unparalleled carbon stability in recent studies. This study investigates the applicability of the biochar-based soil composite (BbSC) at varying density states (1.1–1.3 Mg m −3 ), intending to substitute conventionally used granular materials in thermal backfills. The BbSC was prepared by amending the locally available soil with biochar varying from 5 to 15% by weight. The BbSC was prepared in the dry and wet states by varying the molding water content from 10 to 30% at the compaction stage of sample preparation. Later, the K and volumetric heat capacity ( C ) were examined. Moreover, underground granular backfills might interact with moisture due to groundwater movement and rainfall. Therefore, another set of BbSC samples was injected with water from the bottom to simulate a near-saturation state, and their thermal characteristics were compared. The experiments revealed that the decrease in the thermal conductivities of BbSC upon increment in biochar content is consistent with only up to 10% biochar content. Moreover, K of BbSC increases with the increase in moisture content. From the measured data, linear regression was performed along with the sensitivity analysis to quantify the relationship between thermal characteristics and the initial molding state (dry density, initial water content, and biochar content) for BbSC. The developed equations can be helpful for the geotechnical and environmental engineering community in designing the large-scale application of the BbSC for sustainable thermal backfills.

The objective of this study is to propose an intelligent methodology for efficiently optimizing the injection molding parameters when multiple constraints and multiple objectives are involved. Multiple objective functions reflecting the product quality, manufacturing cost and molding efficiency were constructed for the optimization model of injection molding parameters while multiple constraint functions reflecting the requirements of clients and the restrictions in the capacity of injection molding machines were established as well. A novel methodology integrating variable complexity methods (VCMs), constrained non-dominated sorted genetic algorithm (CNSGA), back propagation neural networks (BPNNs) and Moldflow analyses was put forward to locate the Pareto optimal solutions to the constrained multiobjective optimization problem. The VCMs enabled both the knowledge-based simplification of the optimization model and the variable-precision flow analyses of different injection molding parameter schemes. The Moldflow analyses were applied to collect the precise sample data for developing BPNNs and to fine-tune the Pareto-optimal solutions after the CNSGA-based optimization while the approximate BPNNs were utilized to efficiently compute the fitness of every individual during the evolution of CNSGA. The case study of optimizing the mold and process parameters for manufacturing mice with a compound-cavity mold demonstrated the feasibility and intelligence of proposed methodology.

Due to the fact that different injection molding conditions tailor the mechanical response of the thermoplastic material, such effect must be considered earlier in the product development process. The existing approaches implemented in different commercial software solutions are very limited in their capabilities to estimate the influence of processing conditions on the mechanical properties. Thus, the accuracy of predictive simulations could be improved. In this study, we demonstrate how to establish straightforward processing-impact property relationships of talc-filled injection-molded polypropylene disc-shaped parts by assessing the thermomechanical environment (TME). To investigate the relationship between impact properties and the key operative variables (flow rate, melt and mold temperature, and holding pressure), the design of experiments approach was applied to systematically vary the TME of molded samples. The TME is characterized on computer flow simulation outputs and defined by two thermomechanical indices (TMI): the cooling index (CI; associated to the core features) and the thermo-stress index (TSI; related to the skin features). The TMI methodology coupled to an integrated simulation program has been developed as a tool to predict the impact response. The dynamic impact properties (peak force, peak energy, and puncture energy) were evaluated using instrumented falling weight impact tests and were all found to be similarly affected by the imposed TME. The most important molding parameters affecting the impact properties were found to be the processing temperatures (melt and mold). CI revealed greater importance for the impact response than TSI. The developed integrative tool provided truthful predictions for the envisaged impact properties.

Three different commercial nucleating agents (LAK, talc, and calcium carbonate) were added at different weight percentages into poly (lactic acid) (PLA) in order to investigate the mechanical and thermo-mechanical behavior of blends in correlation to injection molding parameters. After as-sessing the best content of each nucleating agent, analyzing isothermal and non-isothermal crys-tallization, two cycle times that can be industrially adopted were selected. Crystallinity highly impacts the flexural modulus, while it improves the heat deflection temperature only when the crystallinity percentage is above 50%; nevertheless, an excessive crystallinity content leads to a decrement of impact resistance. LAK does not appear to be sensitive to cycle time while talc and calcium carbonate proved to be effective if a cycle time of 60 s is adopted. Since the choice of nu-cleating agent is not univocal, the identification of the best nucleating agents is subject to the technical specifications required by the application, accotuing for the most important commercial requirements (productivity, temperature, and impact resistance).

Driven by global carbon neutrality goals, bamboo fiber-reinforced PP composites have shown significant potential for automotive applications due to their renewability, low carbon emissions, and superior mechanical properties. However, the environmental complexities associated with compression molding process parameters, which impact material properties and carbon emissions, pose challenges for large-scale adoption. This study systematically optimized the compression molding process of bamboo fiber-reinforced PP composites through a three-factor, five-level experimental design, focusing on preheating temperature, preheating time, and holding time. Additionally, an innovative life cycle assessment (LCA) was conducted to evaluate the environmental impact. The results indicated that at a preheating temperature of 220 °C, preheating time of 210–240 s, and holding time of 40–50 s, the material achieved a tensile strength of 35 MPa and a flexural strength of 45 MPa, with a 15% reduction in water absorption. The LCA further highlighted energy consumption, the compression molding process, and material composition as the primary contributors to carbon emissions and environmental impacts, identifying key areas for future optimization. This study provides an optimized framework for compression molding bamboo fiber-reinforced PP composites and establishes a theoretical foundation for their low-carbon application in the automotive industry. Future work will explore the optimization of bamboo fiber content and process parameters to further enhance material performance and reduce environmental impact.

The reinforcement and matrix of a polymer material can be composited into a single polymer composite (SPC), which is light weight, high strength, and has easy recyclability. The insert injection molding process can be used to realize the multiple production of SPC products with a short cycle time and wide processing temperature window. However, injection molding is a very complicated process; the influence of several important parameters should be determined to help in the future tailoring of SPCs to specific applications. The effects of varying barrel temperature, injection pressure, injection speed, and holding time on the properties of the insert-injection molded polypropylene (PP) SPC parts were investigated. It was found that the sample weight and tensile properties of the PP SPCs varied in different rules with the variations of these four parameters. The barrel temperature has a significant effect, followed by the holding time and injection pressure. Suitable parameter values should be determined for enhanced mechanical properties. Based on the tensile strength, a barrel temperature of 260 °C, an injection pressure of 127.6 MPa, an injection speed of 0.18 m/s, and a holding time of 60 s were determined as the optimum processing conditions. The best tensile strength and peel strength were up to 120 MPa and 19.44 N/cm, respectively.

Volume shrinkage and warpage deformation are very critical quality indicators in the plastic injection molding (PIM) of box-shaped thin-walled plastics. These two performance indexes are greatly affected by the molding parameters. Therefore, in this paper, six optimization algorithms and inverse-deformation designs (IDD) are used to reduce volume shrinkage and warpage deformation. Firstly, six important molding parameters, namely filling time (A), plasticity temperature (B), mold temperature (C), holding time (D), maximum holding pressure (E) and cooling time (F), are determined, and the L 25 (5 6 ) orthogonal experimental design (OED) is established. Taguchi grey correlation (TGC) theory analysis is used to determine the optimal combination of molding parameters. Secondly, different combinations of Box-Behnken design (BBD) response surface method, BP neural network (BPNN) training, and NSGA-II genetic algorithms are used to generate four combined optimization algorithms, in order to perform multi-objective optimization of the six molding parameters. The result shows that the effectiveness of four optimization analyses are ranked as follows: BPNN-BBD-NSGA-II > BPNN-BBD > BBD-NSGA-II > BBD. The BPNN-BBD-NSGA-II method holds the best prediction results. Finally, a global optimization platform based on NX/Moldex3D is established considering the IDD theory to simulate the molding process. Optical scanning instruments are used to examine the molding quality. The result proves that the warpage deformation in box-shaped thin-walled injection-molded products is almost completely eliminated and a high molding quality can be achieved. This research is favorable for designing the molding process and guiding the molding of box-shaped thin-walled injection-molded products.

Most lenses on the market can be made by injection molding. However, defects such as weld lines and residual stress are often generated during the production of concave products. Compression molding eliminates these problems, and the cost of the required molding machinery is relatively low compared to other processes. The process can also be highly automated, which is essential for plastic manufacturing. This study proposes an innovative mold for compression molding for a double-concave lens. There is no requirement for angular alignment because the lens is axisymmetric. A compact compression mold without leader pins is created. Parameter optimization for the powder and granular PMMA is optimized using the Taguchi method. The significant compression molding parameters are the pre-pressing period, the pressing temperature, the pressing force, and the pressing period. The surface profiles of finished products are measured, and a factor response analysis is used to determine the effect of various parameters on the finished product. The pressing force was shown to be the most significant factor for powder. However, granules need a long enough pre-pressing time because the gap widens. A confirmation experiment was conducted with the optimized parameters. The profile of the compressed double-concave lens is measured and compared with the mold insert. Powder PMMA is more suitable for compression molding than granular PMMA. However, regardless of the material type, the compression-molded double-concave lens is 96% replicable or more.

This publication deals with the study of the mechanical properties of injection-molded polypropylene parts depending on the process parameters and the distance from the gate location in which the mechanical properties were investigated. Due to the fact that the mechanical properties of injection-molded parts are not the same at all locations, this research was designed to investigate the inhomogeneity of the properties of injection-molded parts along the length of the product. The inhomogeneity is affected by various influences, including distance from the sprue mouth, melt and mold temperature, injection pressure, crystal structure, and others. It was demonstrated that mechanical properties are not uniform over the entire injected product. Contrary to popular belief, mechanical properties can vary along the flow length due to uneven cooling and process parameters. Injection pressure and mold temperature significantly affect the mechanical properties of the injection-molded parts. The limiting injection pressure is 40 MPa and the mold temperature is 40 °C. The difference in individual spots in an injected article was up to 37%. Changes in mechanical properties are closely related to changes in morphology (crystallinity measured by DSC) caused by different injection molding process parameters. As is evident from the aforementioned results, the possible benefits of this work for injection molding of polymer products are apparent. Suitably chosen gate location, surface of the cavity, and process parameters can ensure targeted improvement of mechanical properties in stressed parts of a product.

The key of the multi-objective optimization of injection molding processes lies in achieving a balance between the accuracy of the surrogate model and the multiple objectives while taking the diversity and interdependence of process parameters into consideration. However, the sampling process for building high-precision surrogate models requires a large number of sample points, resulting in high modeling costs for other regions. Moreover, the selection of Pareto fronts often relies solely on the magnitudes of objective values, without considering the uncertainties associated with the information. To address these issues, this research proposes a novel multi-objective optimization method for injection molding process parameters, using hierarchical sampling and integrated entropy weighting. The method introduces a unique hierarchical sampling approach to enhance the accuracy of the surrogate model in injection molding, with a specific focus on critical components. Additionally, our method incorporates entropy calculations for multiple objective defect value parameters during the multi-objective optimization process, enhancing the rationality of the optimization process. The proposed method is utilized to optimize the injection molding parameters of a thin-walled propeller blade. The result shows that our surrogate model fits well and exhibits superior performance compared to the response surface method in optimizing multiple objectives.