Polypropylene (PP) parts produced by means of extrusion-based additive manufacturing, also known as fused filament fabrication, are prone to detaching from the build platform due to their strong tendency to shrink and warp. Apart from incorporating high volume fractions of fillers, one approach to mitigate this issue is to improve the adhesion between the first deposited layer and the build platform. However, a major challenge for PP is the lack of adhesion on standard platform materials, as well as a high risk of welding on PP-based platform materials. This study reports the material selection of build platform alternatives based on contact angle measurements. The adhesion forces, investigated by shear-off measurements, between PP-based filaments and the most promising platform material, an ultra-high-molecular-weight polyethylene (UHMW-PE), were optimised by a thorough parametric study. Higher adhesion forces were measured by increasing the platform and extrusion temperatures, increasing the flow rate and decreasing the thickness of the first layer. Apart from changes in printer settings, an increased surface roughness of the UHMW-PE platform led to a sufficient, weld-free adhesion for large-area parts of PP-based filaments, due to improved wetting, mechanical interlockings, and an increased surface area between the two materials in contact.

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 aimed at optimizing the injection molding process of an automotive oil filter housing made from PA6.6. Mass accumulations in its design intensely increased the cooling time. In a first successful approach, a copper alloy mold insert (λ = 106 Wm ⁻¹ K ⁻¹ ) that contains two externally cooled heat‐conducting copper pins (λ = 310 Wm ⁻¹ K ⁻¹ ) was installed. We hypothesized that a biomimetic cooling channel structure in a steel mold insert would even perform superior. Using simulation software Sigmasoft® v5.0, the mold insert materials (steel Bohler X20Cr13, Ampcoloy® 83), the plastics grade PA66‐GF35 (λ = 0.27 Wm ⁻¹ K ⁻¹ or λ = 0.40 Wm ⁻¹ K ⁻¹ ), and three cooling designs were evaluated for their impact on cooling the hot spots in the part: the copper pin‐system, a conformal cooling channel, and blood‐vessel like channels. In the latter, the major artery branches into two sub‐arteries, which further divide into two capillary tubes each. The capillaries merge into two sub‐veins and those fuse in the major vein again. As expected, the plastics heat conductivity dominates the cooling. The biomimetic (blood‐vessel) channels, as hypothesized, cooled the major hot spot more efficient than the conformal channel and the copper pin system do. In detail, compared to the heat‐conductive insert, the cycle time may be reduced by 10 s, in spite of the lower heat conductivity λ (23.5 Wm ⁻¹ K ⁻¹ ) of the steel insert. Using the biomimetic (blood‐vessel) structure in a heat‐conductive mold insert would reduce the cycle time by a further second. However, raising the heat conductivity of the plastics would save another 15s. Experimental tests proved the cooling efficiency. Nevertheless, cooling channels only perform well, if they are close enough to the mass accumulation. In conclusion, transferring biologic cooling structures to injection molding seems to be promising. Ongoing advances in Additive Manufacturing will help to efficiently implement these structures into mold inserts for injection molding. POLYM. ENG. SCI., 59:E180–E188, 2019. © 2018 The Authors. Polymer Engineering & Science published by Wiley Periodicals, Inc. on behalf of Society of Plastics Engineers.

When two compatible polymer melts are brought into contact, a diffuse interphase is formed that governs the adhesion between them. The degree of interdiffusion is defined mainly by the processing conditions and the physico‐chemical properties of the materials. Despite the great industrial relevance of co‐extrusion in manufacturing multilayer structures, little research has dealt with interdiffusion under these processing conditions, where polymer‐polymer interfaces are subject to shear load. In this work, procedures for investigating interdiffusion both under static and real co‐extrusion flow conditions were developed. Using various grades of poly(methyl methacrylate) and styrene‐co‐acrylonitrile copolymer, we studied both theoretically and experimentally the effects of interfacial contact time, temperature, material compatibility, and interfacial shear stress on interdiffusion. Spectroscopic analysis of the interfaces using confocal Raman microscopy showed that the general relationships between interdiffusion, time (Fick's law) and temperature (Arrhenius relationship) are identical under both static and co‐extrusion conditions. However, the interdiffusion rate is significantly higher under shear influence. Since the prediction of the interdiffusion process supports material selection and process design in multilayer manufacturing, a simple analytical model was developed to quantify the effects of aforementioned material properties and processing conditions on the apparent mutual interdiffusion coefficient.

Intermeshing co‐rotating twin‐screw extruders are very versatile because their screw configurations can be tailored both to the application and to the properties of the materials used. Finding the best screw configuration is one of the main purposes of twin‐screw extrusion modeling, and requires models that accurately predict conveying and power consumption. The better the process can be predicted, the better the requirements of the final product can be met. We present novel prediction models of the conveying and power‐consumption behaviors of intermeshing co‐rotating twin‐screw extruder kneading blocks for Newtonian fluids. These are based on numerical simulations and therefore consider the complex three dimensional (3D) geometry of this element type without the need for common simplifications. Our models are thus capable of including all leakage flows and gap influences, which are usually ignored, for example, by the flat‐plate model. Since our models are derived by symbolic regression based on genetic programming, they consist of algebraic functions and are low‐threshold. They can be used to calculate various process parameters for individual kneading blocks or entire screw configurations, as illustrated by a use case.

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.