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Unidirectional fiber-reinforced thermoplastic (UD) tapes are used increasingly in lightweight construction for local reinforcement of plastic parts. A critical step in UD tape manufacturing is spreading of the fiber rovings by mechanical, electrostatic, pneumatic, or acoustic means to obtain a homogeneous carpet. This is a prerequisite for homogeneous impregnation, which in turn is a requirement for defect-free reinforced components. A major challenge in fiber spreading is the inconsistent and scattering width of the fiber rovings within a bobbin or between different bobbins caused by the manufacturing process of the rovings. Especially for heavy tows – carbon-fiber rovings consisting of more than 24K filaments – variable input quality of the rovings renders obtaining a constant final roving width with existing spreading techniques impossible. We propose a novel spreading technique based on the principle of forced conveying that aims to provide constant final widths that are largely independent of the quality of the input roving. In a feasibility study, we investigated the forced conveying spreading behavior of a 60K carbon fiber roving; our new spreading method provided constant final widths independent of initial width and cross-sectional shape. Statistical analysis showed that both the wrap angle and the circumferential speed of the roller and their quadratic influences have highly significant effects on the final spread width. With an R 2 of 0.9972, the quadratic model determined has good predictive accuracy for the spread width in the measured range.

Corrugated pipes offer both higher stiffness and higher flexibility while simultaneously requiring less material than rigid pipes. Production rates of corrugated pipes have therefore increased significantly in recent years. Due to rising commodity prices, pipe manufacturers have been driven to produce corrugated pipes of high quality with reduced material input. To the best of our knowledge, corrugated pipe geometry and wall thickness distribution significantly influence pipe properties. Essential factors in optimizing wall thickness distribution include adaptation of the mold block geometry and structure optimization. To achieve these goals, a conventional approach would typically require numerous iterations over various pipe geometries, several mold block geometries, and then fabrication of pipes to be tested experimentally—an approach which is very time-consuming and costly. To address this issue, we developed multi-dimensional mathematical models that predict the wall thickness distribution in corrugated pipes as functions of the mold geometry by using symbolic regression based on genetic programming (GP). First, the blow molding problem was transformed into a dimensionless representation. Then, a screening study was performed to identify the most significant influencing parameters, which were subsequently varied within wide ranges as a basis for a comprehensive, numerically driven parametric design study. The data set obtained was used as input for data-driven modeling to derive novel regression models for predicting wall thickness distribution. Finally, model accuracy was confirmed by means of an error analysis that evaluated various statistical metrics. With our models, wall thickness distribution can now be predicted and subsequently used for structural analysis, thus enabling digital mold block design and optimizing the wall thickness distribution.

Unidirectional (UD) fiber-reinforced thermoplastic tapes provide excellent specific mechanical properties; thus, they are being increasingly used for the targeted local reinforcement of plastic components in lightweight construction applications. An essential step in the production of UD tapes is fiber spreading, the aim of which is to expand fiber rovings from an initial width to a defined final width. Using a test rig under realistic conditions, we systematically investigated the factors that influence fiber spreading by deflection. Carbon-fiber rovings with various numbers of filaments were guided over deflection rods, and roving width before and after spreading was recorded with cameras. A full design of experiments (DoE) plan was set up, in which (i) the number of rods, (ii) rod diameter, (iii) immersion depth of the rod, and (iv) take-off speed of the fiber roving were systematically varied. We statistically evaluated the results of the experiments and found that the main factors that influenced the response variables investigated were number of rods and rod diameter, followed by immersion depth. We also observed that a higher number of filaments in the roving led to more complexity and greater variability. Our results can be used to optimize the spreading configuration in the production of UD tapes.

In the manufacturing of injection-molded plastic parts, it is essential to perform a non-destructive (and, in some applications, contactless) three-dimensional measurement and surface inspection of the injection-molded part to monitor the part quality. The measurement method depends strongly on the shape and the optical properties of the part. In this study, a high-precision (±5 µm) and high-speed system (total of 24 s for a complete part dimensional measurement) was developed to measure the dimensions of a piano-black injection-molded part. This measurement should be done in real time and close to the part’s production time to evaluate the quality of the produced parts for future online, closed-loop, and predictive quality control. Therefore, a novel contactless, three-dimensional measurement system using a multicolor confocal sensor was designed and manufactured, taking into account the nominal curved shape and the glossy black surface properties of the part. This system includes one linear and one cylindrical moving axis, as well as one confocal optical sensor for radial R-direction measurements. A 6 DOF (degrees of freedom) robot handles the part between the injection molding machine and the measurement system. An IPC coordinates the communications and system movements over the OPC UA communication network protocol. For validation, several repeatability tests were performed at various speeds and directions. The results were compared using signal similarity methods, such as MSE, SSID, and RMS difference. The repeatability of the system in all directions was found to be in the range of ±5 µm for the desired speed range (less than 60 mm/s–60 degrees/s). However, the error increases up to ±10 µm due to the fixture and the suction force effect.

Automatic in-line process quality control plays a crucial role to enhance production efficiency in the injection molding industry. Industry 4.0 is leading the productivity and efficiency of companies to minimize scrap rates and strive for zero-defect production, especially in the injection molding industry. In this study, a fully automated closed-loop injection molding (IM) setup with a communication platform via OPC UA was built in compliance with Industry 4.0. The setup included fully automated inline measurements, in-line data analysis, and an AI control system to set the new machine parameters via the OPC UA communication protocol. The surface quality of the injection molded parts was rated using the ResNet-18 convolutional neural network, which was trained on data gathered by a heuristic approach. Further, eight different machine learning models for predicting the part quality (weight, surface quality, and dimensional properties) and for predicting sensor data were trained using data from a variety of production information sources, including in-mold sensors, injection molding machine (IMM) sensors, ambient sensors, and inline product quality measurements. These models are the backbone of the AI control system, which is a heuristic model predictive control (MPC) method. This method was applied to find new sets of machine parameters during production to control the specified part quality feature. The control system and predictive models were successfully tested for two groups of quality features: Geometry control and surface quality control. Control parameters were limited to injection speed and holding pressure. Moreover, the geometry control was repeated with mold temperature as an additional control parameter.

This study used the Discrete Element Method (DEM) coupled with the Moving Particle Semi-implicit (MPS) method to investigate the process of drying in the centrifugal unit of a pelletizing system in polymer processing. The effects of various flight angles (10°, 45°, and 70°) and rotor speeds (1280, 1600, and 1920 rpm) on drying efficiency, polymer pellet transport, polymer pellet accumulation, and power consumption were examined. The results showed that the flight angle significantly influenced drying performance. At 1600 rpm, the 10° flight angle configuration required the least power (10.94 kW) but resulted in inefficient water separation, which led to an increase in water droplets (i.e., higher moisture content) in the upper part of the centrifugal unit and near the outlet. With a 70° flight angle, water removal was most effective, but polymer pellet transport efficiency was lower due to centrifugal forces becoming dominant. A 45° flight angle provided the best balance between drying efficiency and power consumption, requiring 16.42 kW while achieving the most efficient polymer pellet transport. Rotor speed also played a crucial role: lower speeds enhanced water removal and reduced power demand but limited throughput, whereas higher speeds facilitated centrifugal separation at the cost of increased power consumption. The optimal combination of the rotor speed and flight angle was found to be 45° at 1280 rpm, which offered an effective trade-off between drying performance and power efficiency.