Abstract The concept of residence time distribution (RTD) is crucial in many chemical engineering applications. In the case of co-rotating twin-screw extrusion, it is an important process parameter, particularly when dealing with reactive systems, degradation issues, or scale-up problems. In this review paper, after introducing the basic notions concerning RTD, the various measurement methods are detailed. Then, experimental results on the influence of the main parameters of the extrusion process (screw speed, feed rate, barrel temperature, screw profile, etc.) are presented. Finally, the various theoretical approaches to describe and/or predict RTD are reviewed.

Abstract In this work we investigate the influence of closed (“C”) or open (“O”) extrusion degassing, and low (5 MPa – “L”) or high (90 MPa – “H”) holding pressure during injection molding on the properties of wood-plastic composite (WPC) based on high-density polyethylene (HDPE) flakes compatibilized with 5 wt% maleic anhydride-modified polyethylene (MAPE), 5 wt% lubricant (Struktol® TPW 113), and 40 wt% cashew nutshell powder (CNSP). Two reference compounds were extruded with closed degassing and injected at 90 MPa of holding pressure: (1) HDPE flakes extruded with 5 wt% MAPE and 5 wt% Struktol® TPW 113 (HDPEad_C-H) and (2) HDPE flakes previously melt mixed in an internal mixer with 5 wt% CNSL (HDPEr/5CNSL) and then extruded with the same additive content (HDPEad/5CNSL_C-H). At 90 MPa holding pressure, the extract content of WPC slightly increased and CNSL acted as a lubricant and poor plasticizing agent, reducing the crystallinity and density of the additivated matrix (HDPEad_C-H), increasing the flow rate without significantly changing the tensile strength. On the other hand, low holding pressure (5 MPa) favored the volatilization and expansion of the residual CNSL in the WPC, which acted as a blowing agent improving filling of the mold cavity and avoiding sink marks. Sample extruded with open degassing (atmospheric pressure) did not significantly change the extract content, but favored CNSL diffusion from particles to matrix, which subsequently during injection molding led to its entrapment at the interface, resulting in low adhesion, especially at high holding pressure that hinders CNSL expansion and vaporization. These findings contribute to understand the role of residual CNSL of CNSP in WPC properties and thus, to strengthen the plastics recycling chain and reduce carbon footprint.

Abstract Extrusion is a key process in mechanical recycling. In a degassing step, volatile components, including all impurities and moisture, are removed from a polymer melt to ensure consistently high quality of the recyclates. Predicting devolatilization performance is therefore of interest in the design of degassing screws; in the plastics industry, it also plays an important role in the transition from a linear to a circular economy. Using two different devolatilization models, we first modelled the degassing process of a lab-scale twin-screw extruder and an industrial-scale recycling single-screw extruder. We then predicted the devolatilization performance of both machines, validated the results with experimental data obtained from emissions tests carried out with post-industrial and post-consumer polypropylene materials and performed linear regression analysis to compare our two models in terms of predictive quality. Our results showed that both models are equally suitable for reliable prediction of the devolatilization performance.

Abstract A hybrid composite incorporating veli karuvelam and peepal fibers was fabricated, with the addition of nanosilica as a filler to enhance composite performance. The impact of nanosilica and layering sequence on the physical properties of the composites was investigated. Initially, two types of hybrid composites i.e. peepal/veli karuvelam/peepal (PVP) and veli karuvelam/peepal/veli karuvelam (VPV) were developed by means of hand layup method, varying the layering sequence of veli karuvelam and peepal fibers. The PVP composite was identified as the most promising based on tensile strength (71 MPa), attributed to the optimal packing of high-strength peepal fibers. Subsequently, nanosilica was incorporated into the PVP composite at different weight percentages (1, 2 and 3), and its influence on composite performance was analyzed. The PVP composite containing 2 wt% SiO2 showed impressive mechanical properties, with a tensile strength of 76 MPa, a flexural strength of 104 MPa, an impact strength of 4.5 kJ/m2, and a hardness of 89 on the Shore D scale. The findings indicate that a PVP composite containing 2 wt% SiO2 has potential for use in engineering applications.

Abstract This research investigates the production of composite materials by utilizing a polystyrene-based resin (PBR) as the matrix and a blend of coconut fiber (CF) and rubber tire (RT) as fillers. The composites were produced in varying proportions, and their mechanical and chemical properties were characterized through hardness tests, Fourier-Transform Infrared Spectroscopy (FTIR), scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDS) analyses. The findings highlight the notable influence of filler type and proportion on the mechanical and chemical attributes of the composites. The hardness tests demonstrated a substantial enhancement in composite hardness with the incorporation of CF and RT fillers, with CF exerting a more pronounced effect. FTIR analysis disclosed the presence of aromatic and aliphatic groups in all composites, and the introduction of CF and RT particles led to the emergence of additional peaks. EDS analysis indicated that carbon was the predominant element in all composites, followed by oxygen, while the SEM images revealed a heterogeneous microstructure for all composites, with good dispersion of CF and RT particles in the PBR matrix. The resulting composites exhibit potential applications in diverse fields such as construction, automotive, and packaging.

Abstract An important machine setting parameter of the injection moulding process is the injection velocity, which influences the local process parameters in the mould cavity and as a result the final part properties. In order to avoid surface defects by too low or too high melt front velocities along the flow path, profiling of the injection velocity can be necessary. Therefore, in previous work, a methodical approach has been developed, which calculates an injection velocity profile that can be set directly at the injection moulding machine, in order to gain a constant melt front velocity along the flow path. Using the existing approach, two different part geometries are injection moulded with an injection velocity profile from two different materials. The resulting microscopic and macroscopic part properties are evaluated and compared with the samples produced at a constant injection velocity. By keeping the melt front velocity constant, the surface quality can be specifically influenced, while other properties are hardly affected by profiling the injection velocity.

Abstract Data-based process monitoring in injection molding plays an important role in compensating disturbances in the process and the associated impairment of part quality. Selecting appropriate features for a successful online quality prediction based on machine learning methods is crucial. Time series such as the injection pressure and injection flow curve are particularly suitable for this purpose. Predicting quality as early as possible during a cycle has many advantages. In this paper it is shown how the recording length of the time series affects the prediction performance when using machine learning algorithms. For this purpose, two successful molding quality prediction algorithms (k Nearest Neighbors and Ridge Regression) are trained with time series of different lengths on extensive data sets. Their prediction performances for part weight and a geometric dimension are evaluated. The evaluations show that recording time series until the end of a cycle is not necessary to obtain good prediction results. These findings indicate that early reliable quality prediction is possible within a cycle, which speeds up prediction, allows timely part handling at the end of the cycle and provides the basis for automated corrective interventions within the same cycle.

Abstract This study aimed to develop novel hybrid composites with graphene (Gr) fillers incorporated in the epoxy (E) matrix with Caesar weed fiber (CF), and roselle fiber (RF) as reinforcements. Compression molding methods were used to fabricate hybrid composite materials with a variable-weight graphene filler in a constant fiber epoxy matrix. On the basis of the results, the mechanical characteristics of the composite with 6 wt% Gr exhibited the greatest flexural strength, tensile strength, and impact strength. This occurred because 6 wt% Gr particles are more uniformly dispersed in an epoxy matrix, resulting in better compatibility between reinforcementand matrix, thus increasing the mechanical properties. The composite with 8 wt% Gr filler reinforcement had the maximum hardness rating and the lowest percentage of water absorption. According to the results, adding graphene fillers to the CF/RF/E composite significantly improved the mechanical and water absorption performances. Scanning electron microscopy was used to examine the surfaces of the fabricated samples. The weight fraction of the graphene filler was optimized to enhance the mechanical properties of the composite for use in various engineering applications, such as automobile, defense, marine, sports, and musical instruments.