Mold Manufacturing Research Articles

Automated 3D burr detection in cast manufacturing using sparse convolutional neural networks

For automating deburring of cast parts, this paper proposes a general method for estimating burr height using 3D vision sensor that is robust to missing data in the scans and sensor noise. Specifically, we present a novel data-driven method that learns features that can be used to align clean CAD models from a workpiece database to the noisy and incomplete geometry of a RGBD scan. Using the learned features with Random sample consensus (RANSAC) for CAD to scan registration, learned features improve registration result as compared to traditional approaches by (translation error (Delta 18.47 mm) and rotation error(Delta 43 ^circ )) and accuracy(35%) respectively. Furthermore, a 3D-vision based automatic burr detection and height estimation technique is presented. The estimated burr heights were verified and compared with measurements from a high resolution industrial CT scanning machine. Together with registration, our burr height estimation approach is able to estimate burr height similar to high resolution CT scans with Z-statistic value (z=0.279).

Review and preliminary design of 3-D printed ABS polymer torsion element with parametric internal structures

Various components utilized in the automobile dashboard controls are manufactured from plastic by a conventional molding process. With the recent trend and continuously changing designs of the components due to frequently upgraded models, the conventional process of manufacture of these components proves costly owing to the high investment in molds. The technology which has taken attention for these demanding conditions is the Fused Deposition or 3-D printing technology in development and manufacture of plastic components for ready use direct assembly which bypasses the process of mold manufacture and then subsequent plastic molding which is many times involves a considerable amount of production time and high production cost. The strength of the components produced by the 3-D printing method is seen to be greatly influenced by the infill density, print quality, and layer thickness.The depositing parameters have been studied in various studies but they failed to characterize the geometrical arrangement permutations through the addition of internal support structures to enhance the torsion strength of the components. The paper discusses a review of research work over the years proposes the first stage design and analysis of the plain component using Ansys workbench and also the manufacturing of these plain elements with various permutations of infill density, layer thickness, and print quality. The components thus produced will be tested and compared for optimal deposition parameters. By considering the gathered data, an optimization model that realizes the material usage, build time, strength characteristics, and their related variables will be presented and used to assist the components with optimal deposition parameters which will be further considered for design with internal support structures with the incorporation of varied parametric internal structure matrix.

Parallel batching with multi-size jobs and incompatible job families

Parallel batch scheduling has many applications in the industrial sector, like in material and chemical treatments, mold manufacturing and so on. The number of jobs that can be processed on a machine mostly depends on the shape and size of the jobs and of the machine. This work investigates the problem of batching jobs with multiple sizes and multiple incompatible families. A flow formulation of the problem is exploited to solve it through two column generation-based heuristics. First, the column generation finds the optimal solution of the continuous relaxation, then two heuristics are proposed to move from the continuous to the integer solution of the problem: one is based on the price-and-branch heuristic, the other on a variable rounding procedure. Experiments with several combinations of parameters are provided to show the impact of the number of sizes and families on computation times and quality of solutions.

Hybrid manufacturing of Invar mold for carbon fiber layup using structured light scanning

This paper describes coordinate system definition and transfer for five-axis machining of additively-manufactured preforms. In this method, a set of fiducials are attached to the temporarily attached to the part, and their location relative to the preform geometry is calibrated using a structured light scanner. Those fiducials can then be measured in the machine tool to determine the location and orientation of the part. The method is demonstrated by finish-machining a carbon fiber layup mold from an additively manufactured Invar preform. In addition to showing the coordinate transfer methods necessary to machine the part, several key challenges with machining additively-manufactured preforms are discussed and potential solutions are proposed. Unfortunately, the final part was ultimately unusable due to porosity inside the part left from the additive process. Future work will remanufacture this part while taking steps to avoid porosity and other challenges encountered.

Recent research progress of master mold manufacturing by nanoimprint technique for the novel microoptics devices

Recent research progress of master mold manufacturing by nanoimprint technique for the novel microoptics devices

Multi-pass EDM process planning for the surface treatment of powder-bed-fusion AM CORRAX mold steel

Although it is time-consuming to perform multi-pass electrical discharge machining (EDM) processes, EDM is exceedingly feasible for processing the partially unmelt profile of Powder-Bed-Fusion additive manufacture mold steel. This study is based on the principle of removing three times the thickness of the recast layer and reducing the EDM processes depth. And then, the surface quality and the processing time with EDM passes are decreased by adjusting the EDM passes suggested by the machine builder. Secondly, reduction of four more passes and the predetermined depth of processing so that the total processing time can be reduced further. Finally, the efficiency is increased by more than 77% compared to machine builder’s processes, and the final surface roughness reaches the specification of 0.17 μm Ra. The HAZ and recast layer thickness are also reduced drastically and distributed uniformly. Therefore, a new version of the EDM process planning suitable for AM CORRAX mold surface treatment is proposed, and its processing efficiency and surface integrity are investigated.

The design and additive manufacturing of an eco-friendly mold utilized for high productivity based on conformal cooling optimization

This study proposes an eco-friendly (ECO) mold that is fabricated using the laser powder bed fusion (LPBF) method, which utilizes a three-dimensional (3D) cooling channel. Unlike previous studies, we designed surface-type conformal cooling channels (CCC) based on the design concept for additive manufacturing, at a constant depth corresponding to the casting part. Pin fins were installed inside the surface-type cooling channels and aligned through numerical analysis to induce the turbulence of the coolant uniformly through the cooling channel while generating less thermal stress during solidification of the as-cast Al-Si alloy-based product, which increased the cooling efficiency of the proposed ECO mold. Moreover, the ECO mold was utilized to cast an automobile piston to demonstrate its feasibility at an industrial level. The results showed that, the casting process decreased significantly from 133 to 105 s per piston (21% reduction) compared to the conventional mold with a single columnar cooling channel. Additionally, the average grain size of the ECO mold decreased from 502 to 398 µm. The ultimate tensile strength and Rockwell hardness increased by 12.5% and 5.5%, respectively.

Replicative manufacturing of metal moulds for low surface roughness polymer replication

Tool based manufacturing processes like injection moulding allow fast and high-quality mass-market production, but for optical polymer components the production of the necessary tools is time-consuming and expensive. In this paper a process to fabricate metal-inserts for tool based manufacturing with smooth surfaces via a casting and replication process from fused silica templates is presented. Bronze, brass and cobalt-chromium could be successfully replicated from shaped fused silica replications achieving a surface roughnesses of Rq 8 nm and microstructures in the range of 5 µm. Injection moulding was successfully performed, using a commercially available injection moulding system, with thousands of replicas generated from the same tool. In addition, three-dimensional bodies in metal could be realised with 3D-Printing of fused silica casting moulds. This work thus represents an approach to high-quality moulding tools via a scalable facile and cost-effective route surpassing the currently employed cost-, labour- and equipment-intensive machining techniques.

GA-AHP Method to Support Robotic Polishing Process Planning

The finishing stage of mould manufacturing is generally completed via mechanical polishing and manually conducted. Not only is this the most expensive phase of the process but also is currently struggling with a deficit of skilled workers. To address these issues, and support the wider needs of Industry 4.0, the manufacturing community has investigated robotic technologies to support the polishing process. The work reported here, investigating the polishing process planning and optimization for mould manufacture is part of a larger project aiming to automate 80% of the current manual process. Presented in this article is an optimization strategy for robotic polishing process sequencing aiming at satisfying polishing sequence rules and the shortest polishing time simultaneously. A hybrid approach combining both genetic algorithm (GA) and analytical hierarchical process (AHP) is proposed based on the specific characteristics of polishing process sequencing. A multi-objective fitness function is defined using AHP including the calculation of polishing time and evaluation of polishing process rules. The proposed GA-based process sequencing has been successfully demonstrated on test piece examples.

Optimizing the Mechanical and Microstructure Characteristics of Stir Casting and Hot-Pressed AA 7075/ZnO/ZrO2 Composites

The composite was made using the stir cast manufacturing method. Many parameters, like stirring speed, stirring time, ZrO2% reinforcement, and cast temperature, are evaluated in a Taguchi experimental design to see how they affected the composite properties. In terms of composite properties, ZrO2% reinforcement and the stir speed have the most significant impact. There were 25.02% gains in ultimate tensile strength and hardness, as well as a decrease in composite wear loss, when the optimal stir casting parameters were used compared to the initial stir casting settings. To get insight into the process and the qualities of the composite, the hot-pressing parameters were studied. Pressure, followed by temperature, is the most critical factor in determining the properties of composites. When a hot-pressing setting was determined to reduce the wear loss by a significant 39.3%, it was deemed perfect by the superranking concept. Under ideal conditions, hot-pressing procedures reduced wear loss by 40.8% while boosting ultimate tensile strength and hardness by 19.83% and 9.6%, respectively. The resulting microstructures and worn surface morphologies from stir casting and hot pressing show vastly different properties.

Design of an Automobile Injection Mould Based on Automation Technology

At present, there are frequent occurrences in the industry of manufacturers only having products, but they have no product data, resulting in manufacturers unable to open molds to produce the products themselves and can only buy them at high prices. This article takes the rear case of an automobile generator produced by a company as an example, which aims to show the design of automobile injection molds through automation technology. This paper studies the use of automation technology to achieve the automatic model reconstruction of products, and uses the CAD technology to design injection molds based on the obtained digital models of products. This paper also uses the CAE technology to verify the feasibility and rationality of the designed mold, exploring the optimization method of the injection molding process of the mold. In this paper, a special product pouring system and demolding mechanism are designed according to the product process requirements. The design greatly simplifies the mold structure while fulfilling the product process requirements, which avoids the use of expensive hot runners and complex flip-chip mold structures, reducing the mold manufacturing cost. Secondly, the overall design of the injection mold is carried out, and the external dimensions and other main parameters of the mold are determined. Then, Moldflow software is used to simulate the injection molding process, verifying the feasibility and rationality of the designed mold. The optimization method of the injection molding process is also proposed, and the optimal injection process parameters are determined to ensure that the mold can produce qualified products and ensure high production efficiency. It can be seen from the analysis results that the maximum warpage deformation of the plastic part under all effects is 0.65 mm, which meets the product process requirements.

Aluminodipine conborate phosphate molding and core mixtures for steel and iron castings

It has been experimentally established that the introduction of lithium, sodium, and potassium dipinaconborates into a 10 % aqueous solution of monosubstituted aluminum phosphate leads to a sharp decrease in viscosity and limiting wetting angle. Based on the data obtained, it was concluded that the acceptable content of dipinaconborate is 8–10 wt %, at which the viscosity and limiting wetting angle characteristics meet the requirements for the practical use of binders for cold-hardening mixtures in foundry production. It was found that dipinaconborates increase the binding property of monosubstituted aluminum phosphate. At the same time, the binding property of aluminum dipineconborate phosphate compositions increases in the series of dipineconborates: potassium dipineconborate < sodium dipineconborate < lithium dipineconborate. The aluminum dipineconborate phosphate composition is a combination of monosubstituted aluminum phosphate with lithium, sodium and potassium dipineconborate separately. On the basis of aluminum dipineconborate phosphate binders, core and molding cold-hardening mixtures have been developed with the composition, %: sand 2K1O202 (GOST 2138–91) - 80; clay PZ (GOST 3226-93) - 10; aluminum dipineconborate phosphate binder in the form of a 20 % aqueous solution - 10. Aluminum dipineconborate phosphate mixtures were tested for such physical and mechanical properties as durability, friability, mouldability, strength, residual strength and knockout work. The obtained characteristics of the developed mixtures for the manufacture of molds and cores for steel and iron castings meet the requirements of foundry practice and can be recommended for the manufacture of cores and molds cured in cold tooling.

Revealing the significance of the influence of vanadium on the mechanical properties of cast iron for castings for machine-building purpose

The object of study in this work is cast iron with lamellar graphite, modified with two types of modifiers – FeSi75 and FeSi40V7. In this work, the influence of vanadium on the mechanical properties of cast iron used for castings for engineering purposes was determined. The existing problem lies in the fact that ignorance of the influence of the alloying element on the mechanical properties of the alloy does not allow determining its consumption rates during the melting process. This can lead to unnecessary costs for materials for melting and casting, and not be justified in terms of the expected improvement in properties. To determine the effect of vanadium on properties, three indicators of the quality of cast iron are considered: tensile strength, stiffness, and a generalized quality index for mechanical properties. A decision is proposed on the procedure for checking the significance of the influence of vanadium within the considered range of variation V=0.04–0.078 % on these indicators. It has been established that the introduction of vanadium into cast iron as part of the FeSi40V7 modifier leads to a decrease in the tensile strength by 4 %, but to an increase in rigidity by 2 %. A significant influence of vanadium with a probability of 95 % was also established with respect to the generalized quality indicator for mechanical properties – the introduction of vanadium contributes to a drop in this indicator by about 5 %. As a result, it was concluded that the use of vanadium in the composition of FeSi40V7 within the final content in cast iron at the level of 0.04–0.078 % can be expedient only if it is necessary to increase the hardness of cast iron due to the promotion of carbide formation during alloy crystallization. The presented study will be useful for machine-building enterprises that have foundries in their structure, where cast iron is smelted for the manufacture of castings.

Hierarchical macro to nano press molding of optical glasses by using metallic glasses

The fabrication of ideal optical components is a world-wide grand challenge facing us. Benefiting from the unique thermoplastic nature, precision glass molding (PGM) is considered as a promising method for rapid and precise fabrication of optical components. The success and proliferation of such methods critically rely on the manufacturing of robust and durable master molds. Although silicon-based molds relying on photolithographic forming are facile to manufacture, they are brittle and have limited longevity when used as the molds. To address this challenge, here, we propose a hierarchical macro to nano mold preparation method based on the thermoplastic forming ability of metallic glasses (MGs), which excels in micro and nano scale fabrication. After subsequently crystallizing the as-formed MG, we demonstrate that the crystalline MG is suitable as an excellent mold for molding optical glasses with service temperature up to 600 °C. Using this ‘spawning’ process, several metallic molds including macroscopic lens arrays, microscale gratings, and nano-lens arrays were successfully prepared, respectively. The minimum feature size of the mold is 300 nm and the optimum surface roughness is 7.43 nm. Correspondingly, we prepared hierarchical optical glass components with excellent optical properties using MG as molds.

Microstructural characteristics and material removal mechanisms in nano-scale finished surface of mirror-like injection mould fabricated by laser metal deposition and post treatments

The manufacture of injection moulds with mirror-like surfaces by laser metal deposition is a state-of-the-artdevelopment trend in manufacturing industries. The high surface quality and long-term service performance are two critical demands for laser-metal-deposited injection moulds. In this paper, the microstructure and material removal mechanisms of laser-metal-deposited moulds after nano-scale surface finishing and post treatment were analyzed. Our results confirmed that the differences in temperature gradient and melting composition during solidification would result in both columnar and equiaxial dendritic αʹ-Fe grains distributed in the mould surfaces, and the surface defects of the moulds after nano-scale finishing were directly related to the aggregation of brittle M23(C,B)6 networks stripped out from inhomogeneous αʹ-Fe matrices. Both laser-surface-heat and vacuum-furnace-heat post treatments were beneficial to the microstructural homogenization. The former could achieve directional solidification microstructures with columnar αʹ-Fe dendrites and well-distributed M23(C,B)6 networks, effectively relieve stress concentration during finishing and successfully enhance surface quality and performance. The latter would produce the formation of retained γ-Fe and M2(C,B) particles, weakening surface quality of moulds. Therefore, the laser-surface-heat post treatment seemed to be one of the most promising ways to address microstructural homogenization of laser metal deposited moulds.

과학수사에 활용하기 위한 개, 고양이 및 다양한 동물의 모소피 주형의 제작과 유형 분류

Among the manufacturing of cast of cuticle in animal hair for scientific investigation use, appearance, drying time, and damage to samples of five methods (C-CA, C-NC, C-PVA, C-PRG, and C-PRL) were compared. It was confirmed that the C-NC method was most appropriate for the cast method because the C-NC method did not show damage to the sample due to the appearance, the drying time of the gel was about 3 minutes, and the cuticle was distinguishable. In addition, as a result of classifying the morphology forms obtained from a total of 80 species (Dog; 34 species, Cat; 18 species, wild animal; 28 species) into 10 types (WR, WIN, WIC, MP, MD, MB, SS, SA and ST), the types of morphology appearing according to the same species or species may be similar. In addition, it was confirmed that dogs tend to be dominant in WIC (37%) and WIN (28%) types, cats in WR (59%) types, wild animals in WIC (32%) and WR (22%) types, and MB type is water deer, and SS type is a family or species characteristic that can only be distinguished from other animals.

Cellular automaton simulation and experimental validation of eutectic transformation during solidification of Al-Si alloys

The morphology of eutectic silicon in solidification microstructure is critical to the performance of Al-Si-based alloys. Simulating eutectic Si phase formation has been a challenge in ICME (integrated computational materials engineering) based design and manufacturing of solidification products of Al-Si-based alloys. In this study, our previous three-dimensional (3-D) cellular automaton (CA) model for α-Al dendritic growth was extended to include eutectic (α-Al + Si) transformation in multi-dendrite domains, providing a complete solidification simulation of critically important Al-Si based alloys. The quantitative results of the Si phase in the eutectic microstructure were experimentally validated using scanning electron microscopy and deep etching techniques. The simulation results show a good agreement with the experimental observations and calculations by the Scheil model and lever rule. This 3-D CA model is useful for predicting and optimizing the solidification microstructure including eutectic transformation during solidification processing such as casting, potentially welding, and additive manufacturing.

Clustering-based adaptive data augmentation for class-imbalance in machine learning (CADA): additive manufacturing use case

AbstractLarge amount of data are generated from in-situ monitoring of additive manufacturing (AM) processes which is later used in prediction modelling for defect classification to speed up quality inspection of products. A high volume of this process data is defect-free (majority class) and a lower volume of this data has defects (minority class) which result in the class-imbalance issue. Using imbalanced datasets, classifiers often provide sub-optimal classification results, i.e. better performance on the majority class than the minority class. However, it is important for process engineers that models classify defects more accurately than the class with no defects since this is crucial for quality inspection. Hence, we address the class-imbalance issue in manufacturing process data to support in-situ quality control of additive manufactured components. For this, we propose cluster-based adaptive data augmentation (CADA) for oversampling to address the class-imbalance problem. Quantitative experiments are conducted to evaluate the performance of the proposed method and to compare with other selected oversampling methods using AM datasets from an aerospace industry and a publicly available casting manufacturing dataset. The results show that CADA outperformed random oversampling and the SMOTE method and is similar to random data augmentation and cluster-based oversampling. Furthermore, the results of the statistical significance test show that there is a significant difference between the studied methods. As such, the CADA method can be considered as an alternative method for oversampling to improve the performance of models on the minority class.

The Investigation of Key Factors in Polypropylene Extrusion Molding Production Quality

This study took food-grade polypropylene packaging products as the research project and discussed how to control the polypropylene extrusion sheet thickness and vacuum thermoforming quality and weight. The research objective was to find the key factors for reducing costs and energy consumption. The key aspects that may influence the polypropylene extrusion molding quality control were analyzed using literature and in-depth interviews with scholars and experts. These four main aspects are (1) key factors of polypropylene extrusion sheet production, (2) key factors of the extrusion line design, (3) key factors of polypropylene forming and mold manufacturing, and (4) key factors of mold and thermoforming line equipment design. These were revised and complemented by the scholar and expert group. There are 49 subitems for discussion. Thirteen scholars and experts were invited to use qualitative and quantitative research methods. A Delphi questionnaire survey team was organized to perform three Delphi questionnaire interviews. The statistical analyses of encoded data such as the mean (M), mode (Mo), and standard deviation (SD) of various survey options were calculated. Seeking a more cautious research theory and result, the K-S simple sample test was used to review the fitness and consistency of the scholars’ and experts’ opinions on key subitem factors. There are ten key factors in the production quality, including “A. Main screw pressure”, “B. Polymer temperature”, “C. T-die lips adjustment thickness”, “D. Cooling rolls pressing stability”, “E. Cooling rolls temperature stability”, “F. Extruder main screw geometric design”, “G. Heating controller is stable”, “H. Thermostatic control”, “I. Vacuum pressure”, and “J. Mold forming area design”. The key factors are not just applicable to classical polypropylene extrusion sheet and thermoforming production but also to related process of extrusion and thermoforming techniques in expanded polypropylene (EPP) sheets and polylactic acid (PLA). This study aims to provide a key technical reference for enterprises to improve quality to enhance the competitiveness of products, reduce production costs, and achieve sustainable development, energy savings, and carbon reductions.

A Machine Learning Strategy for Race-Tracking Detection During Manufacturing of Composites by Liquid Moulding

This work presents a supervised machine learning (ML) model to detect race-tracking disturbances during the liquid moulding manufacturing of structural composites. Race-tracking is generated by unexpected resin channels at mould edges that may induce dry spots and porosity formation. The ML model uses the pressure signals recorded by a sensor network as input, providing a classification of the race-tracking event from a set of possible scenarios, and a subsequent variable regression for their position, size and strength. Such a model is based on the residual network (ResNet), a well-known artificial intelligence architecture that makes use of convolutional neural networks for image recognition. Training of the ML classifier and regressors was carried out with the aid of a synthetically generated simulation data set obtained throughout computational fluid dynamics simulations. The time evolution of the pressure sensors was used as grey-level images, or footprints, as inputs to the ResNet ML. The trained model was able to recognise the presence of race-tracking channels from the pressure data yielding good accuracy in terms of label prediction as well as position, size and strength. The model correlation was carried out with a set of injection experiments performed with a constant thickness closed mould containing induced race-tracking channels. The ability of ML models to provide an approximation to the inverse problem, relating the pressure sensor distortions to the cause of such events, is analysed and discussed.