Injection-molded Plastic Parts Research Articles

Facilitating the Production of 3D-Printed Spare Parts in the Design of Plastic Parts: A Design Requirement Review

Using additive manufacturing for spare part production can ensure that spare parts are available for a long time. However, spare parts are currently not designed for additive manufacturing. This study aimed to find how the production of 3D-printed spare parts can be facilitated in the design of plastic parts. We used a literature review and illustrative case to find how the design requirements for standard injection moulded plastic parts relate to the manufacturing capabilities of additive manufacturing for spare parts. The design requirements were defined by assigning corresponding structural and material properties. These requirements were then used to construct and evaluate the capabilities of additive manufacturing compared to injection moulding. It was found that additive manufacturing is especially suitable for requirements like Accuracy, Heat resistance, and Chemical resistance. However, to fully enable 3D-printed spare parts, certain design challenges still need to be tackled. Designers should pay careful attention to the synergies and trade-offs between design requirements and the challenges that might arise from the combination of certain requirements. Also, designers should ensure products are easily reparable before considering 3D-printed spare parts. If we target these challenges in the design phase, we can facilitate 3D-printed spare parts that enable product repairability.

METHODOLOGY FOR INTELLIGENT PLASTIC INJECTION POINT LOCATION BASED ON GEOMETRIC ALGORITHMS AND DISCRETE TOPOLOGIES FOR VIRTUAL DIGITAL TWIN ENVIRONMENTS

Implementing intelligent design models can revolutionize the use of digital twins, which are crucial in product design by incorporating intelligent algorithms. This perspective is especially important for the design of injection-molded plastic parts, a complex process that often requires expert knowledge and costly simulation software not available to all companies. This article presents an innovative methodology for locating injection points in injection-molded parts using intelligent models with geometric algorithms for discrete topologies. The first algorithm calculates the center of mass of the discrete model based on the center of mass of each triangular facet in the system, ensuring uniform molten plastic distribution during mold cavity filling. Two sub-algorithms intelligently evaluate the geometry and optimal injection point location. The first sub-algorithm generates a geometric matrix based on a two-dimensional nodal quadrature adapted to the part's bounding box. The second sub-algorithm projects the nodal matrix and associated circular areas orthogonally on the part's surface along the demolding direction. The optimal injection point location is determined by minimizing the distance to the center of mass from the first algorithm's result. This novel methodology has been validated through rheological simulations in six case studies with complex geometries. The results demonstrate uniform and homogeneous molten plastic distribution with minimal pressure loss during the filling phase. Importantly, this methodology does not require expert intervention, reducing time and costs associated with manual injection mold feed system design. It is also adaptable to various design environments and virtual twin systems, not tied to specific CAD software. The validated results surpass the state of the art, offering an agile alternative for digital twin applications in new product design environments, reducing dependence on experts, facilitating designer training, and ultimately cutting costs Keywords: Injection Moulding, Product Design, Industrial Design, Geometrical Algorithms, Digital Twin

Influence of process parameters and post-molding condition on shrinkage and warpage of injection-molded plastic parts with complex geometry

Influence of process parameters and post-molding condition on shrinkage and warpage of injection-molded plastic parts with complex geometry

An Automatic, Contactless, High-Precision, High-Speed Measurement System to Provide In-Line, As-Molded Three-Dimensional Measurements of a Curved-Shape Injection-Molded Part

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.

On-demand forklift hailing system for Intralogistics 4.0

The shift to I4.0 is happening. While large companies have a range of solutions to implement that change, small and medium-sized enterprises (SME) fall short on solutions tailored for their specific needs. To support SMEs in their transformation toward I4.0, we propose a lightweight system to hail forklifts in a production facility of a medium-sized enterprise.Existing shop floor workflows are implemented within the system and allow machine operators to hail forklift drivers using an embedded or a web-based client. Forklift drivers receive driving instructions on their smartphones. Shift managers can monitor intralogistic activities on a dashboard. Management can extract relevant production and forklift KPIs from the system.In a two-week evaluation phase, we installed our system in a production facility for injection moulded plastic parts. We equipped 12 machines and two forklifts and registered a total of 690 jobs. We found half of the jobs were picked up in 4:05 min and 80% of all jobs were completed in less than 40:02 min.

Multi-objective optimization of injection-molded plastic parts using entropy weight, random forest, and genetic algorithm methods

Abstract The qualities of injection-molded parts are affected by process parameters. Warpage and volume shrinkage are two typical defects. Moreover, insufficient or excessively large clamping force also affects the quality of parts and the cost of the process. An experiment based on the orthogonal design was conducted to minimize the above defects. Moldflow software was used to simulate the injection process of each experiment. The entropy weight was used to determine the weight of each index, the comprehensive evaluation value was calculated, and multi-objective optimization was transformed into single-objective optimization. A regression model was established by the random forest (RF) algorithm. To further illustrate the reliability and accuracy of the model, back-propagation neural network and kriging models were taken as comparative algorithms. The results showed that the error of RF was the smallest and its performance was the best. Finally, genetic algorithm was used to search for the minimum of the regression model established by RF. The optimal parameters were found to improve the quality of plastic parts and reduce the energy consumption. The plastic parts manufactured by the optimal process parameters showed good quality and met the requirements of production.

Injection mold design of reverse engineering using injection molding analysis and machine learning

Plastic composites are used in vehicle components to improve fuel efficiency. Thus, the warpage of injection-molded plastic parts has become a quality issue. Factors, such as product shape and thickness, resin, and other injection molding conditions, can be modified to improve the warpage problem. However, if these factors are set with no possible adjustments, reverse engineering may be required. Reverse engineering is a difficult process that requires many trials and errors; thus, it is only used as a last resort. With respect to the warpage issue, reverse engineering considers the following: (1) Predicting and (2) modeling the warpage in opposite directions. Autodesk Moldflow Insight accommodates these key considerations, but many researchers are reluctant to use it. Although existing injectionmolding analysis programs are mainly used to predict qualitative results, computer-aided engineering (CAE) for reverse engineering requires quantitative analysis. Hence, the considerations are different from the existing analyses. An error in warpage prediction may lead to a costly mold modification because of the molds' complex structures. Quantitative warpage prediction for reverse engineering depends on process variables; thus, understanding how warpages are affected by uncertain process variables is important to improve the reliability of reverse engineering. Moreover, even if appropriate process variables are set, they cannot be applied due to tolerance in lengths. For this reason, mold shrinkage must be identified before designing a mold. This study conducted injection molding analysis for a radiator tank that uses glass fiber-reinforced plastic using Autodesk Moldflow Insight 2018.2. Data for warpage prediction were generated in accordance with five process variables to identify the relationship between the level of warpage and process variables. CAE also showed the level of mold shrinkage that can reduce warpage. In addition, a predictive model was created using the multilayer perceptron (MLP)- supervised learning technique, which is a deep learning method for artificial neural networks. The predictive model was compared with typical regression models, such as polynomial regression (also known as response surface model), EDT and RBF, to determine the optimal approximation model. The real modeling time for a radiator tank product is 1 h, but the MLP approximation model required only 1 min and 8 s to perform 8530 iterations with a similar reliability.

Effect of Melt Temperature on Weld Line Strength

This article deals with the influence of the melt temperature on weld line strength in injection-molded plastic parts. A special mold was created for this investigation to make specimens with a central weld line. The experimental material was polypropylene Sabic PP 90910. Its stress at break is around 16 MPa and its melting temperature is between 200°C and 260°C. In general, the presence of weld lines has a negative impact on mechanical properties. This investigation showed that the strength depends on the melt temperature (160–260°C in this case). Strength was measured using mechanical testing. It was found to increase with the melt temperature, up to 210°C. Above 210°C, degradation of the plastic led to decreasing strengths. A melt temperature range of 190–210°C is therefore optimal for this application. Shear forces and friction were found to play a great role, as they raise the melt temperature during molding. This was confirmed by mold-flow analysis. The melt temperature during molding was up to 18% higher than the initial melt temperature. This investigation has important consequences for the plastic industry. It is relevant to evaluations of the polymer matrix strength in composite materials and the strength of 3D printed parts with multiple weld lines.

Effect of glass fiber distributions on the mechanical and fracture behaviors of injection-molded glass fiber-filled polypropylene with 2-Hole Tension specimens

In this study, various fracture characteristics of plastic parts were experimentally investigated using newly designed specimens with 2 holes to observe the deformation and fracture behaviors of injection-molded plastic parts fabricated by short glass fiber (GF)-reinforced polypropylene (PP). This 2-hole tension (2HT) specimen affects the flow pattern of resin and the orientation of GF during the injection molding process, and various stress concentrations and distributions can be generated under a simple tensile load. The effects of rheological and mechanical variations of the resin on the deformation and fracture behavior of the test specimens were successfully investigated using the 2-hole tension specimens. In addition, it was confirmed that the non-uniform distribution of GF observed in the hole arrays region through the computed tomography (CT) scan is related to the crack propagation pathway, which ultimately affects the fracture behavior of the specimen.

Warpage reduction through optimized process parameters and annealed process of injection-molded plastic parts

The objective of this paper is to achieve the minimization of warpage for an injection molded part. The techniques that were implemented to minimize the warpage are the design of experiment (DOE), response surface methodology (RSM), firefly algorithm (FA), and annealing treatment. The packing time, cooling time, and melt temperature were shown to be significant parameters and FA was employed to seek these suitable values by experimental tests based on simulation software Moldex3D and injection machine. Analysis of variance (ANOVA) was used to validate experiments. Annealing treatment process was then applied to reduce more warpage phenomenon; the results showed that warpage phenomenon decreased dramatically compared with popular optimized parameter methodology. Moreover, the residual stress which was obtained by using photoelasticity showed that it has a direct relationship with warpage reduction. Therefore, the best solution of warpage mitigation can be solved by popular optimum process parameters and also annealing treatment.

A method to predict early-ejected plastic part air-cooling behavior towards quality mold design and less molding cycle time

Abstract It is a common industrial practice to eject injection-molded plastic parts early, at a high temperature, and allow the parts to cool down in the air. This practice shortens the cycle time and reduces production cost. However, current commercial injection molding simulation software tools can only consider the in-mold cooling process. The simulation of the air-cooling stage after ejection is not well supported in such tools even though the air-cooling shrinkage is significant when plastic parts are ejected at high temperatures. The authors propose a Moldflow™-Ansys™ integrated FEA method to simulate the air-cooling process so that the air-cooling shrinkage can be considered at the early design stage and the quality of the part can be ensured with less molding cycle time. A real industrial case study is provided to show the procedure and its validation. The proposed method integrates Moldflow™ and Ansys™ by feeding Moldflow™ simulation results as the intermediate state data set into Ansys™ for air-cooling effect simulation. With a real testing product part ejected at a high temperature, the proposed approach shows promising predictions of the 3D warpage displacement. In this way, the cost factor of molding cycle time can be considered at the mold design stage and a cost-effective design can be developed.

Computer-aided engineering analysis for early-ejected plastic part dimension prediction and quality assurance

Early ejection of injection-molded plastic parts aims to shorten the production cycle. However, the early ejection process may induce unfavorable deformations. No commercial simulation tool is available to predict the transitional mechanical properties of the plastic part and the possible deformations induced by early ejection. The authors propose a method to simulate the early ejection process and the plastic part’s mechanical response by integrating Moldflow™ and Ansys™. A real industrial case study is provided to show the procedure and its validation. Compared to the stand-alone molding simulation, the proposed method can predict the part’s final dimensions more accurately. By considering the ejection-induced deformations at the mold design stage, the mold can be better designed to facilitate early ejection, while the part is only partial solidified.

Cost analysis in injection moulded plastic parts designing

One of the major problem in plastic part design consists in the cost optimization. The designer should take into consideration some relevant aspects that are related to the functional role of the part, the mechanical and thermal loads, and the part cost. The part cost depends on three main components: the mould cost, the equipment operation cost and the material cost. The injection cycle time, the part volume and the material nature can estimate the last two components of the part cost. The mould cost is more difficult to estimate, and current CAD software did not integrate such a feature. The paper present a methodology to estimate the mould cost, taking into consideration the part geometry and its dimensional prescriptions. In addition, a spreadsheet is provided in order to estimate the mould cost.

Residual stress distributions and their influence on post-manufacturing deformation of injection-molded plastic parts

Post-manufacturing thermal exposure of injection-molded parts may result in unexpected permanent deformation. In this study, the mechanism of the post-manufacturing thermal deformation of injection-molded parts is investigated. A simple stress lattice part was used, and injection-molding experiments were carried out to characterize the deformation that occurred in post-manufacturing thermal cycling. The stress lattice part was chosen so that the thermal stress could be investigated with different cooling rates in the same part. The stress lattice parts were thermally cycled using a thermal chamber. A series of computer-aided engineering (CAE) analyses, including injection-molding analysis and finite element analysis (FEA), were performed to describe the deformation following the thermal cycle. The results of these analyses were in good agreement with the experimental results, and revealed that the residual stress formed during the injection molding process was the primary cause of the permanent deformation following the post-manufacturing thermal cycling.

Warpage and Shrinkage Optimization of Injection-Molded Plastic Spoon Parts for Biodegradable Polymers Using Taguchi, ANOVA and Artificial Neural Network Methods

In this study, it is attempted to give an insight into the injection processability of three self-prepared polymers from A to Z. This work presents material analysis, injection molding simulation, design of experiments alongside considering all interaction effects of controlling parameters carefully for green biodegradable polymeric systems, including polylactic acid (PLA), polylactic acid-thermoplastic polyurethane (PLA-TPU) and polylactic acid-thermoplastic starch (PLA-TPS). The experiments were carried out using injection molding simulation software Autodesk Moldflow® in order to minimize warpage and volumetric shrinkage for each of the mentioned systems. The analysis was conducted by changing five significant processing parameters, including coolant temperature, packing time, packing pressure, mold temperature and melt temperature. Taguchi's L27 (35) orthogonal array was selected as an efficient method for design of simulations in order to consider the interaction effects of the parameters and reduce spurious simulations. Meanwhile, artificial neural network (ANN) was also used for pattern recognition and optimization through modifying the processing conditions. The Taguchi coupled analysis of variance (ANOVA) and ANN analysis resulted in definition of optimum levels for each factor by two completely different methods. According to the results, melting temperature, coolant temperature and packing time had significant influence on the shrinkage and warpage. The ANN optimal level selection for minimization of shrinkage and/or warpage is in good agreement with ANOVA optimal level selection results. This investigation indicates that PLA-TPU compound exhibits the highest resistance to warpage and shrinkage defects compared to the other studied compounds.

Warpage prediction of the injection-molded strip-like plastic parts

For most strip-like plastic injection molded parts, whose cross section size is much smaller than their length, the traditional Hele-Shaw model and three-dimensional model do not work well in the prediction of the warpage because of their special shape. A new solution was suggested in this work. The strip-like plastic part was regarded as a little-curved beam macroscopically, and was divided into a few one-dimensional elements. On the section of each elemental node location, two-dimensional thermal finite element analysis was made to obtain the non-uniform thermal stress caused by the time difference of the solidification of the plastic melt in the mold. The stress relaxation, or equivalently, strain creep was dealt with by using a special computing model. On the bases of in-mold elastic stress, the final bending moment to the beam was obtained and the warpage was predicted in good agreement with practical cases.

Reducing ghost marks in injection-molded plastic parts by using external gas-assisted holding pressure

Ghost marks are gloss-related defects in injection-molded plastics that cause uncertain visual darkness depending on the viewing angle. Conventionally, these minor defects are not substantial, but they cannot be overlooked when they diminish the appearance quality of plastic parts such as dashboards in luxury automobiles. This work investigated the causes and effects of ghost marks through experimental observations of a tensile-test sample, which was assessed to determine the improvement of its appearance quality with respect to processing parameters including mold and melt temperatures as well as injection speed and pressure. To reduce defects, external gas-assisted injection molding (eGAIM) that engages the packs on the reverse of in-molded parts with external gas pressure was further employed. For objectively evaluating the gloss qualities of the tested samples, the captured images of part surfaces were converted into grayscale images, and the degrees of defect were then quantized as the deviation values of the grayscale images. The results showed that high mold and melt temperatures as well as high injection speed and pressure facilitated increasing the quality of the parts, but exhibited limitations. Furthermore, in this study, employing external GAIM with gas packing pressure above 3MPa successfully eliminated ghost marks.

Using magnetic levitation for non-destructive quality control of plastic parts.

Magnetic levitation (MagLev) enables rapid and non-destructive quality control of plastic parts. The feasibility of MagLev as a method to: i) rapidly assess injection-molded plastic parts for defects during process optimization, ii) monitor the degradation of plastics after exposure to harsh environmental conditions, and iii) detect counterfeit polymers by density is demonstrated.

Surface roughness reduction using spray-coated hydrogen silsesquioxane reflow

Surface roughness or texture is the most visible property of any object, including injection molded plastic parts. Roughness of the injection molding (IM) tool cavity directly affects not only appearance and perception of quality, but often also the function of all manufactured plastic parts. So called “optically smooth” plastic surfaces is one example, where low roughness of a tool cavity is desirable. Such tool surfaces can be very expensive to fabricate using conventional means, such as abrasive diamond polishing or diamond turning. We present a novel process to coat machined metal parts with hydrogen silsesquioxane (HSQ) to reduce their surface roughness. Results from the testing of surfaces made from two starting roughnesses are presented; one polished with grit 2500 sandpaper, another with grit 11.000 diamond polishing paste. We characterize the two surfaces with AFM, SEM and optical profilometry before and after coating. We show that the HSQ coating is able to reduce peak-to-valley roughness more than 20 times on the sandpaper polished sample, from 2.44(±0.99)μm to 104(±22)nm and more than 10 times for the paste polished sample from 1.85(±0.63)μm to 162(±28)nm while roughness averages are reduced 10 and 3 times respectively. We completed more than 10,000 injection molding cycles without detectable degradation of the HSQ coating. This result opens new possibilities for molding of affordable plastic parts with perfect surface finish.

Two-Component Injection Molding of Molded Interconnect Devices

Molded Interconnect Device (MID) can be defined as that an injection-molded plastic part combining with electrical and mechanical functions in a single device. This study is to examine the application of micro injection molding technology to the two-component molding process for the MID fabrication. The process involves the first shot of a plastic component with channel patterns on the surface. A second shot by micro injection molding technology is applied to fill the channel with the plateable plastics. The effects of the micro injection molding process parameters on filled line width of the two-component MID will be investigated. It is concluded that, for a MID component, the molding conditions must be designed carefully to keep the thickness variation below the allowable value. It is also found from the experiments that the thickness interference may in the range from 92 m to 196 m to have adequate molding at the second shot.