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.

The gloss transition defect of injection-molded surfaces should be mitigated because it creates a poor impression of product quality. Conventional approaches for the suppression of the gloss transition defect employ a trial-and-error approach and additional equipment. The causes of the generation of a low-gloss polymer surface and the surface change during the molding process have not been systematically analyzed. This article proposes the causes of the generation of a low-gloss polymer surface and the occurrence of gloss transition according to the molding condition. The changes in the polymer surface and gloss were analyzed using gloss and topography measurements. The shrinkage of the polymer surface generates a rough topography and low glossiness. Replication to the smooth mold surface compensates for the effect of surface shrinkage and increases the surface gloss. The surface stiffness and melt pressure influence the degree of mold surface replication. The flow front speed and mold temperature are the main factors influencing the surface gloss because they affect the development rate of the melt pressure and the recovery rate of the surface stiffness. Therefore, the mold design and process condition should be optimized to enhance the uniformity of the flow front speed and mold temperature.

With one measurement configuration, existing gloss measurement methodologies are generally restricted to specific gloss levels. A newly developed image-analytical gloss parameter called “clarity” provides the possibility to describe the perceptual result of a broad range of different gloss levels with one setup. In order to analyze and finally monitor the perceived gloss of products, a fast and flexible method also for the automated inspection is highly demanded. The clarity parameter is very fast to calculate and therefore usable for fast in-line surface inspection. Coated metal specimens were deformed by varying degree and polished afterwards in order to study the clarity parameter regarding the quantification of varying surface gloss types and levels. In order to analyze the correlation with the human gloss perception a study was carried out in which experts were asked to assess gloss properties of a series of surface samples under standardized conditions. The study confirmed clarity to exhibit considerably better correlation to the human perception than alternative gloss parameters.

The touch-feel sensation of product surfaces arouses growing interest in various industry branches. To entangle the underlying physical and material parameters responsible for a specific touch-feel sensation, a new measurement system has been developed. This system aims to record the prime physical interaction parameters at a time, which is considered a necessary prerequisite for a successful physical description of the haptic sensation. The measurement setup enables one to measure the dynamic coefficient of friction, the macroscopic contact area of smooth and rough surfaces, the angle enclosed between the human finger and the soft-touch surfaces and the vibrations induced in the human finger during relative motion at a time. To validate the measurement stand, a test series has been conducted on two soft-touch surfaces of different roughness. While the individual results agree well with the literature, their combination revealed new insights. Finally, the investigation of the haptics of polymer coatings with the presented measuring system should facilitate the design of surfaces with tailor-made touch-feel properties.

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.

Dynamic mold surface temperature control was examined for its influence on the warpage. A test mold, featuring two different rapid heat cycle molding (RHCM) technologies was used to manufacture complex plate-shaped parts having different ribs, varying thin-wall regions, and both, circular and rectangular cut-outs. The mold's nozzle side is equipped with the areal heating and cooling technology BFMOLD®, where the heating/cooling channels are replaced by a ball-filled slot near the cavity surface flooded through with hot and cold water sequentially. Two local electrical ceramic heating elements are installed into the mold's ejection side. Based on a 2³ full-factorial design of experiments (DoE) plan, varying nozzle temperature (T_nozzle), rapid heat cycle molding temperature (T_RHCM) and holding pressure (p_n), specimens of POM were manufactured systematically. Five specimens were examined per DoE run. The resulting warpage was measured at 6 surface line scans per part using the non-contact confocal topography system FRT MicroProf®. Two warpage parameters were calculated, the curvature of a 2^nd order approximation a, and the vertical deflection at the profile center d. Both, the influence strength and the acting direction of the process parameters and their interactions on a and d were calculated by statistical analysis. Linear mathematical process models were determined for a and d to predict the warpage as a function of the process parameter settings. Finally, an optimum process setting was predicted, based on the process models and Microsoft Excel GRG solver. Clear and significant influences of T_RHCM, p_n, T_nozzle, and the interaction of T_RHCM and p_n were determined. While T_RHCM was dominant close to the gate, p_n became more effective as the flow length increased.