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Understanding the relationship between injection molding parameters and the acoustic properties of polymers is crucial for optimizing the design and performance of acoustic-based polymer devices. In this work, the impact of injection molding parameters, such as the injection velocity and packing pressure, on the acoustic parameters of polymers, namely the elastic moduli, is studied. The measurements lead to calculating material parameters, such as the Young's modulus and Poisson's ratio, that can be swiftly measured and determined thanks to this method. Polymethyl methacrylate (PMMA) was used as the molding material, and using PMMA LG IG 840, the parts were simulated and injection molded, applying a 'design of experiment' (DOE) statistical method. The results indicated a correlation between the injection molding process parameters and the acoustic characteristics, such as the elastic moduli, and a specifically decreasing trend with increase in the injection velocity. Notably, a relative decrease in the Young's modulus by 1 % was observed when increasing the packing pressure from 90 MPa to 120 MPa . Similarly, a decrease in the Poisson's ratio of 2.9 % was observed when the injection velocity was increased from 16 mm / s to 40 mm / s . This method can be used to fine-tune the material properties according to the needs of a given application and to facilitate the characterization of different polymer acoustic properties essential for acoustic-based polymer devices. [ABSTRACT FROM AUTHOR]

In-situ co-axial meltpool monitoring has become a popular tool for digitising the laser powder bed fusion (L-PBF) process, providing baseline data for certification. Each layer produces an image where the pixel position represents the laser coordinates and the pixel intensity denotes the sensor response. The 3D image stacks represent the infrared emission during the manufacturing of the physical component. However, interpreting monitoring data remains a challenge. To address this issue, this study evaluates the performance of a near-infrared photodiode in detecting typical geometrical features such as porosity and overhanging structures ranging from the micro-to-meso scale. Monitoring data is highly sensitive to heat accumulation around overhanging structures and can quantify dross formation based on hotspots. Cold spots, which represent a lack of fusion porosity at scan track intersections, can indicate a probability of defect formation. However, the sensitivity and predictive value of monitoring data for porosity are low due to the healing of defects in subsequent layers. Local process variables, such as the scan strategy and part orientation, significantly influence dross and hot spot formation. This study shows the potential of NIR photodiodes in deriving metrics for in-line certification of L-PBF components, leading to improved process control and quality assurance. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Demand trends towards mass customization drive the need for increasingly productive and flexible assembly operations. Walking-worker assembly lines can present advantages over fixed-worker systems. This article presents a multiproduct parallel walking-worker assembly line with shared automated stations, and evaluates its operational performance compared to semiautomated and manual fixed-worker lines. Simulation models were used to set up increasingly challenging scenarios based on an industrial case study. The results revealed that semiautomated parallel walking-worker lines could achieve greater productivity (+30%) than fixed-worker lines under high-mix low-volume demand conditions. [ABSTRACT FROM AUTHOR]

Featured Application: In this work, a methodology for the quantitative assessment of additive manufacturing and conventional manufacturing technology value chains is presented for the production of injection molding tools. The development of injection molding tools is an expensive, time-consuming, and resource-intensive process offering little to no flexibility to adapt to variations in product design. Metal additive manufacturing can be used to produce these tools in a cost-effective way. Nevertheless, in an industrial context, effective methods are missing for the selection of the most suitable technology for the given tooling project. This paper presents a method to compare process chains based on additive and conventional subtractive technologies for the manufacturing of metal tooling for injection molding. The comparison is based on a technology focused-performance analysis (TFPA) through computer simulation performed using Tecnomatix Plant Simulation developed by Siemens Digital Industries Software combined with a customized cost–benefit economic analysis tool. The analysis of the technology comparison highlights potential bottlenecks for production, such as the printing phase and the heat treatment. It also gives a deeper understanding of the technology maturity level of conventional milling machines against laser powder bed fusion machines. The result is that the total costs for an insert made by AM and CM are indeed rather similar (the cost difference between the two tooling process chains is lower than 5%). The cost analysis reveals major costs drivers in the production of high-performance molding tools, such as the cutting tools employed for the milling steps and their changeover frequency. The industrial case of a 32-cavity mold insert for plastic injection molding is used to perform the study, develop the analysis, and validate the results. [ABSTRACT FROM AUTHOR]