Manufacturing Tools Research Articles

Development of heating strategies to reduce crack formation in the manufacturing process of tool components using directed energy deposition

Abstract Due to the increasing number of product variants, the automotive industry is focusing on additive manufacturing processes to enhance the flexibility in the production process. In this context, the directed energy deposition process (DED) offers the potential to manufacture tool components hybrid-additively with the aim to produce vehicle outer skin parts more cost-effectively. The temperature gradient between substrate and additive build-up, which is caused by the heat input of the laser, is a key factor for a crack-free manufacturing process, as it affects the magnitude of residual stresses in the component. In this paper, an improved manufacturing strategy is developed to reduce the temperature gradient and therefore the formation of cracks in the tool component. For this purpose, welding specimens are manufactured additively at different preheating temperatures. Moreover, the influence of a heating plate for in situ heating of the substrate, as well as the influence of stress relief annealing on the manufacturing process is examined to reduce the hardness in the bonding zone of the specimens. In this context, the crack length and hardness in the bonding zone are measured using a metallographic analysis. Based on these results, a tool component is manufactured hybrid-additively without any defects.

State-of-the-art research and corresponding industrial perspective for the use of textured surfaces in machining applications

The subject of surface texturing has been one of the most sought and rigorously researched domains of interest for the research community in the manufacturing field for a few decades. The reason for this is the potential applications of surface texturing techniques in various sectors of manufacturing and services. One of the foremost applications where surface texturing has proven to be highly beneficial is cutting tool manufacturing. There are several other applications of surface texturing like fabrication of aerospace and marine structures where the research is still not well established. Unlike these applications, surface texturing in metal cutting has been established and documented by researchers to a level to get the process implemented in practical industry use cases. The present work critically assesses the work related to surface texturing in metal cutting for tools (cutting tools, drills, milling tools, etc.) that have highlighted the influence of surface texturing on performance improvement. Various manufacturing techniques used to create surface textures and their underlying mechanisms for performance improvement have been discussed. The synergistic effects coupled with the benefits of employing texturing with coatings/ lubrication/ cooling have also been critically analyzed over a wider range of process parameters and work material combinations. Furthermore, the existing finite element (FE) models for textured surfaces have been discussed to identify the ease/complexity of the simulation to achieve the advantages of texturing and their practical validity.

Deep learning-based defect detection in film-coated tablets using a convolutional neural network.

Film-coating is a critical step in pharmaceutical manufacturing. Traditional visual inspections for film-coated tablet defect assessment are subjective, inefficient, and labor-intensive. We propose a novel approach utilizing machine learning and image analysis to address these limitations. Here, defects of four types- chipping, breaking, color non-uniformity and speckling, were manually induced in red-orange film-coated placebo tablets. Utilizing a 3-D printed tray and a unique segmentation approach, images of good and defective tablets were collected. A convolutional neural network (CNN) was employed to quantitatively analyze the defects. The model was trained on a comprehensive dataset of 25,200 images of tablets, augmented through various transformations to improve robustness. The CNN's performance was evaluated using metrics such as accuracy, precision, recall, and F1-score. The multi-class classification model demonstrated an accuracy of 99.7% in detection of defects in film-coated tablets, clearly outperforming static rule-based method which had 45%, 45% and 70% error in detecting dimensions- major axis, minor axis, and surface area of the tablets, respectively. This work demonstrates a valuable tool for pharmaceutical manufacturers, providing a standardized, objective, and efficient method for defect detection in tablets and presents a promising solution for ensuring product quality and accelerating the development of new pharmaceutical products.

3D printed clear resin stir bar sorptive extraction coupled with high performance liquid chromatography for trace chlorophenols analysis in environmental water samples.

3D printing is an additive manufacturing technology based on digital model files. 3D printing has become a popular manufacturing tool in various fields. Stereolithography offers a series of advantages compared to its counterparts, such as smooth prints, appropriate resolution in all the axes, acceptable organic solvent compatibility and sufficient tightness to the flowing of solutions/solvents at moderate/high pressure. Thus, this work used stereolithography and clear resin (polymethyl methacrylate resin and epoxy resin) to prepare stir bars' coatings, which reduced the size of the fabricated stir bar (1 mm) and no swelling property in organic solvents. In this work, three types of structures were designed as the coatings of stir bars, which were solid, discal, and lattice coatings with equal mass (0.13 g). Because of the largest surface area, lattice coatings were chosen to make clear resin stir bars. The clear resin stir bars were used for stir bar sorptive extraction and combined with high performance liquid chromatography to develop a new method, which was successfully applied to detect four chlorophenols in environmental water samples. Compared with the previous work using melt deposition modeling 3D printing, this work could print hollow structures with higher precision. The stir bars could have higher rotational speed (700 rpm vs 350 rpm), smaller desorption volume (500 μL vs 2 mL), and shorter extraction time (60 min vs 90 min). The stir bars also had excellent mechanical performance and long lifetimes of up to 160 times. LODs of this method were between 0.30 μg/L (2-CP) and 0.97 μg/L (2,4,6-TCP) (S/N = 3), which were below the concentration limits of surface water samples. Relative standard deviations of the stir bars were 1.4-3.9 % (n = 7, c = 10 μg/L).

A metal 3D-printed non-assembly mandrel for profile bending

Abstract In the overall regime of production technologies, bending processes allow production of complex parts and profiles in a precise, flexible, and economical way. It is important to manufacture tools which are universally applicable and easy to manufacture, since modern industries demand flexibility in terms of larger varieties or smaller batch sizes. In the past, it was difficult and time-consuming to manufacture bending tools, especially mandrels, due to their high geometric complexity and tight assembly of swivel joints. In this paper, we present a new design approach for the bending mandrel which is suitable for metal additive manufacturing that incorporates non-assembly mechanisms of joints. The hinges are systematically developed with particular focus on achieving a minimized gap and resultant clearance. As a test case, a mandrel is 3D printed by using the laser powder bed fusion technique and successfully tested in the rotary draw bending process parameterized for narrow bends in stainless steel tubes. In the light of these results, a solution is formulated for ease in manufacturing and faster rate of tool production, in this case bending mandrel. This methodology facilitates the production of small batch sizes and large product variants in bending processes. The research presented in this paper serves as a forward step towards economical production of complex parts in a highly flexible manner.

Particle Trajectory Simulation Facilitates the Development of an Efficient Sample Introduction System for Single-Particle ICP-MS Analysis.

Inductively coupled plasma mass spectrometry (ICP-MS) has demonstrated significant capabilities in the analysis of single events, such as single cells and particles. Researchers have been actively pursuing innovations in ICP-MS sample introduction systems to enhance their transport efficiency, as this is critical for ensuring the accuracy of single-event analysis. However, the majority of prior studies have relied heavily on empirical approaches, with limited attention given to the individual characteristics of particles from a theoretical perspective and a lack of efficient manufacturing tools for optimizing related components. Herein, we developed a high-efficiency sample introduction system for single-event ICP-MS analysis by integrating the computational simulation-aided design, precise 3D printing manufacturing, and rapid experimental testing process. For the first time, we simulated the transport trajectories of individual particles passing through the spray chamber, providing theoretical guidance for the design and optimization process. The statistical analysis of particle trajectories revealed that under the absorption boundary condition, particles between 20 and 100 nm achieved transport efficiencies exceeding 18.8%. In comparison, particles larger than 100 nm exhibited 0% transport efficiency due to increased deposition within the spray chamber. The spray chamber was fabricated in-house using a range of 3D printing technologies and materials, streamlining the process and reducing the cost for both manufacturing and validation. Further optimization of the operating parameters, including an increase in temperature, resulted in a notable transport efficiency of 61.1%. The workflow introduced in this study has the potential to transform the research and development of critical mass spectrometry components moving forward.

An Analytical Study on Opportunities and Challenges towards Implementation of Industry 4.0 Tools for Manufacturing SMEs in Karnataka

In the era of Industry 4.0, there is a significant shift in manufacturing paradigms, offering substantial opportunities for operational efficiency, product quality, and supply chain integration. However, small and medium-sized enterprises (SMEs) in Karnataka face numerous challenges in adopting these advanced technologies. This study investigates the readiness and capacity of manufacturing SMEs in Karnataka to implement Industry 4.0 tools, focusing on financial capacity, workforce skills, infrastructure support, and comparative regional analysis. Data were collected from ten SMEs through structured questionnaires and discussions with officials, supplemented by secondary data from books, research papers, journals, annual reports, and websites. The data were analyzed using SPSS software, employing descriptive statistics, correlation analysis, and ANOVA. The findings reveal moderate to strong correlations between financial capacity, workforce skills, and infrastructure support, highlighting the interconnected nature of these factors. Despite uniform challenges across industries, Karnataka's SMEs demonstrate a high level of readiness and capacity for digital transformation, suggesting that broad-based initiatives could effectively support their adoption of Industry 4.0 technologies. The study provides actionable insights for policymakers and business leaders to facilitate the successful implementation of these advanced manufacturing tools.

Industry of Okladnikov Cave Layer 3 in the Context of the Sibiryachikha Complexes of the Altai Mountains

This paper presents the results of a comprehensive analysis of lithics from layer 3 of Okladnikov Cave and their relevance to the Sibiryachikha industries of the Altai Mountains. Attribute analysis has shown that the industry of layer 3 demonstrates technological and typological similarities with the Sibiryachikha industries. These include radial flaking with an offset technological axis, a predominance of convergent side-scrapers, and a series of planoconvex bifacial tools. Functionally, the site was a camp where horse and rhinoceros carcasses were butchered and consumed. The analysis of flaking sequence integrity revealed similarity between Okladnikov layer 3 and Chagyrskaya industries in terms of primary and bifacial reduction. The initial stages of core decortication were carried out outside the cave, at the rock outcrops. Subsequent stages of core utilization and all stages of bifacial flaking were carried out in situ. The main difference between Okladnikov layer 3 and Chagyrskaya layer 6c/2 industries concerns only the stage of manufacturing and modifying stone tools. At Okladnikov Cave, these processes were much more intense than at Chagyrskaya, which may indicate transportation of numerous tools and blanks made of high-quality raw material from more distant sources.

Development of an Analytical Model for Predicting the Shear Viscosity of Polypropylene Compounds.

The need for an efficient adaptation of existing polypropylene (PP) formulations or the creation of new formulations has become increasingly important in various industries. Variations in viscosity resulting from changes in raw materials, fillers, and additives can have a significant impact on the processing and quality of PP products. This study presents the development of an analytical model designed to predict the shear viscosity of complex PP blends. By integrating established mixing rules with novel fitting parameters, the model provides a systematic and efficient method for managing variability in PP formulations. Experimental data from binary and multi-component blends were used to validate the model, demonstrating high prediction accuracy over a range of shear rates. The proposed model serves as a valuable tool for compounders and manufacturers to optimise PP formulations and develop new recipes with consistent processing and product quality. Future work will include industrial-scale trials and further evaluation against advanced machine learning approaches.

Interactive infill topology optimisation guided by user drawn patterns

ABSTRACT Widespread use of topology optimisation as a design tool for additive manufacturing faces major inhibiting obstacles, such as high computational costs and complexity, concern for other failure modes, and manufacturability. Interactive infill topology optimisation presents an alternative approach to circumvent some of these barriers. The novel contribution of the present work prompts the user to draw a tailored infill pattern, specify regions of interest to locate the infill, and control how strictly the pattern is replicated in the material layout of the design using appearance constraints. This approach improves engineering metrics not directly included in the optimisation formulation by incorporating the user’s engineering experience, thereby avoiding increased computational costs, parameter tuning, and numerical artifacts associated with complex objective functions and constraints. Two 2D benchmark examples increase the linear buckling resistance and energy absorption, respectively, and a 2.5D example minimises compliance while reducing the quantity of overhang supports for additive manufacturing.

초·중·고 발명 실습실 디지털 제작 도구의 위험 요인 분석에 따른 안전 기준안 탐색

This study investigated how to ensure the safe use of various digital manufacturing tools and equipment, such as 3D printers, in invention labs at elementary, middle, and high schools to prevent potential accidents. These tools and equipment present various hazards such as abrasions, contusions, fractures, punctures, cuts, lacerations, corneal damage, inhalation of harmful substances, poisoning, electric shock, burns, and fire. The likelihood of these risks varies depending on the type of task and tools involved, requiring appropriate prevention and control measures. This study proposes safety standards for the operation of invention labs, emphasizing the importance of both technical and managerial aspects. The managerial safety standards focus on three key areas: inspection, maintenance management, and education, contributing to practical safety management in these labs.

Proteomic Approach Using DIA-MS Identifies Morphogenesis-Associated Proteins during Cardiac Differentiation of Human iPS Cells.

Human-induced pluripotent stem cell (hiPSC)-derived cardiomyocytes have potential applications in regenerative medicine. The quality by design (QbD) approach enables the efficiency and quality assurance in the manufacturing of hiPSC-derived products. It requires a molecular understanding of hiPSC differentiation throughout the differentiation process; however, information on cardiac differentiation remains limited. Proteins associated with the early stages of cardiac differentiation would be useful in the cardiomyocyte quality assessment. Here, we performed quantitative proteomics of hiPSC intermediate cells in the early phase of cardiac differentiation to better understand their molecular characteristics. Proteomic profiles suggested that day 5-7 cells were in the morphogenetic stage of cardiac differentiation. Trophoblast glycoprotein (TPBG) was the most up-regulated protein in the morphogenetic stage; it was previously shown to be up-regulated during differentiation into neural stem cells. Proteomics of TPBG-knockdown cells revealed that TPBG is involved in cell proliferation and is related to the cardiomyocyte yield, suggesting that it could be used as a marker in QbD development. Our approach helps us understand the molecular basis of hiPSC differentiation and could be a powerful tool in QbD-based manufacturing.

Evaluation of Prediction Models for the Capping and Breaking Force of Tablets Using Machine Learning Tools in Wet Granulation Commercial-Scale Pharmaceutical Manufacturing.

Background/Objectives: This study aimed to establish a predictive model for critical quality attributes (CQAs) related to tablet integrity, including tablet breaking force (TBF), friability, and capping occurrence, using machine learning-based models and nondestructive experimental data. Methods: The machine learning-based models were trained on data to predict the CQAs of metformin HCl (MF)-containing tablets using a commercial-scale wet granulation process, and five models were each compared for regression and classification. We identified eight input variables associated with the process and material parameters that control the tableting outcome using feature importance analysis. Results: Among the models, the Gaussian Process regression model provided the most successful results, with R2 values of 0.959 and 0.949 for TBF and friability, respectively. Capping occurrence was accurately predicted by all models, with the Boosted Trees model achieving a 97.80% accuracy. Feature importance analysis revealed that the compression force and magnesium stearate fraction were the most influential parameters in CQA prediction and are input variables that could be used in CQA prediction. Conclusions: These findings indicate that TBF, friability, and capping occurrence were successfully modeled using machine learning with a large dataset by constructing regression and classification models. Applying these models before tablet manufacturing can enhance product quality during wet granulation scale-up, particularly by preventing capping during the manufacturing process without damaging the tablets.

In Situ Programmable, Active, and Interactive Crystallization by Localized Polymerization.

Additive manufacturing has sought active and interactive means of creating predictable structures with diverse materials. Compared to such active manufacturing tools, current crystallization strategies remain in statistical and passive programs of crystals via macroscale thermodynamic controllers, commonly lacking active means to intervene in crystal growth in a spatiotemporal manner. Herein, a strategy toward active and interactive programming and reprogramming of crystals, realized by real-time tangible feedback on growing crystals by delicately controlling the degree of in-situ, localized photopolymerization of polymeric structures via additive manufacturing is presented. Using this strategy, crystals can be seeded, guided, and even reprogrammed in a supersaturated liquid resin. In principle, the localized formation of sparse polymeric networks within supercooled resins can induce density fluctuation to trigger seed nucleation instantaneously, whereas the formation of dense networks can lower molecules' mobilities to inhibit crystal growth. Assisted by these active triggers and deterministic procedural aspects in additive manufacturing, growing crystals can be tangibly interacted through programmed polymeric structures, strengthening deterministic characteristics in crystal growth. It is suggested that crystal growth can be programmable with deterministic hierarchies within the created crystal's morphologies within the background of inherent stochasticity in crystallization, launching an era of convolutional growth of crystals.

Increasing the wear resistance of plunger pairs of high-pressure fuel pumps using extreme pressure additives

The paper presents studies and substantiates extreme pressure additives that are in demand in almost all areas where heavy equipment and machines operate: in heavy industry, metallurgy and metalworking, machine tool manufacturing, aircraft and shipbuilding, automotive production, construction, and power engineering. At the same time, the development and use of this type of additives in energy facilities of the agro-industrial complex is very difficult due to their relative high cost, and the lack of a complete scientific study of the problem does not contribute to the widespread use of extreme pressure additives in agricultural tractor engines. Therefore, there was a need to conduct scientific research to assess the effect of extreme pressure additives on the performance of high-pressure fuel pumps and, based on the information obtained, to develop production recommendations. In the course of the research, a functional model of the plunger pair performance indicator was obtained, taking into account the performance properties of summer diesel fuel with extreme pressure additives. The results of experimental studies are presented, taking into account the performance properties of summer diesel fuel with extreme pressure additives. The results of production tests of plunger pairs of high-pressure fuel pumps using summer diesel fuel with an extreme pressure additive have been obtained. The use of summer diesel fuel with an extreme pressure additive allows increasing the service life of plunger pairs of high-pressure fuel pumps from 1230 to 2214 hours; recommendations for using fuel for 14 kN traction class engines have been developed. The friction coefficient decreases from 0.005 to 0.001 when plunger pairs operate on summer diesel fuel with an extreme pressure additive. The results have been obtained for selecting the component composition of the additive in diesel fuel and recommendations for using the extreme pressure additive in diesel fuel have been developed.

Application of Powder-Bed Fusion of Metals Using a Laser for Manufacturing of M300 Maraging Steel Tools Intended for Sheet Metal Bending.

This paper presents the results of a pilot application of Powder-Bed Fusion of Metals Using a Laser (PBF-LB/M) for the fabrication of M300 (1.2709) maraging steel sheet metal bending tools. S235 steel was used as a substrate for the fabrication of bending punches. The main goal of the research was to determine the usability of such tools without heat treatment, which would contribute to the increase in the cost of tool production. Industrial tests of tools were conducted during the forming of Inconel 625 and AW-6061 T0 aluminium alloy sheets. The punches were subjected to tests of surface roughness, hardness, microstructure, porosity, and geometric quality in order to verify the quality and accuracy of tools made by the PBF-LB/M technique before and after experimental investigations in industrial conditions in a selected manufacturing company. It was found that tools with an M300 steel working layer after the PBF-LB/M process without heat treatment show suitability for bending sheet metal in a certain range of force parameters, ensuring obtaining elements after bending from Inconel 625 and AW-6061 T0 aluminium alloy sheets of the required geometric quality.

Technology of manufacturing copper and bronze tools of the Petrovka Culture of the Southern Trans-Urals and Middle Tobol region

The article presents the results of metallographic analysis of the Petrovka Culture tools from the southern Trans-Urals and Middle Tobol River region of the 19th–18th centuries BC (47 items). A certain correlation has been determined between the functional purpose of an item, the type of raw material, and the tool manufacturing scheme. The tools were mainly made of copper contaminated with impurities, obtained from oxide-carbonate ores with the addition of chalcocite-covellite minerals. A butted axe, sickles, knives with handles, tanged chisels, hooks, and some awls were made of copper, both by casting in a mold with subsequent finishing and by forming forging. Copper tools obtained by casting often had casting defects — shrinkage cracks and warping of the metal. In most cases, the tools were finished either in the regime of incomplete hot forging at 300–500°C, or hot forging at 600–800°C and pre-melting temperatures of 900–1000°C. During the Petrovka period, tin and tin-arsenic bronze started being used for manufacturing adzes, chisels, handled knives, the majority of awls, needles, spear-heads, and arrows. More progressive types of alloys in terms of fluidity, filling mold without defects in the form of low-alloy tin and tin-arsenic bronzes (Sn up to 7%, As up to 4%) came from related tribes of the Petrovka Culture of Saryarka, possibly from the Petropavlovsk Ishim region. The resulting castings were of high quality with smooth surface without metal warping defects. Subsequent finishing was carried out by selecting optimal heat treatment regimes mainly at 600–800°C or 900–1000°C, as well as using incomplete hot forging at 300–500°C. The hardness of the tools finished by forging with heating significantly exceeded the microhardness of the processed copper by 1.5–2 times.

Xolography for 3D Printing in Microgravity.

Xolography is a volumetric 3D printing technique utilizing intersecting light beams within a volume of photopolymer for a spatially controlled photopolymerization. Unlike layer-based methods, Xolography creates structures continuously within a closed photopolymer vat, eliminating the prevalent need for support structures and allowing full geometrical freedom at high printing speeds. The volumetric working principle does not rely on gravity, making Xolography an outstanding technology for additive manufacturing under microgravity conditions as illustrated in a set of experiments during a parabolic flight campaign. The microgravity environment obviates the need for rheology control of resins, enabling the use of low-viscosity formulations (e.g., 11mPas) while maintaining the fast and precise 3D printing of acrylic polymer resins and hydrogels. Xolography's speed and reliability facilitate rapid iterations of a print task between Earth's gravity and microgravity conditions. This capability positions Xolography as an ideal tool for material research and manufacturing in space, offering significant cost and efficiency advantages over traditional methods.

Digital technology integration innovation and visual symbol design of Huizhou ink painting skills under bioengineering

In response to the problems of insufficient interactive experience and unstable inheritance caused by information loss and deformation in the teaching process of Huizhou ink painting skills, this article explores a new method to protect and inherit Huizhou ink painting skills through the integration of biotechnology and digital technology. At the same time, a modern visual symbol design is developed to revitalize this ancient art form in contemporary society and promote its dissemination and exchange worldwide. Firstly, traditional Huizhou ink painting materials were analyzed using biotechnology to understand their composition, structure, and aging mechanism. Scientific and effective protective measures were proposed, and a database was constructed by collecting and matching images of each step in the process. Then use 3D modeling to model the manufacturing tools. By extracting visual elements from micro ink artwork images, drawing basic graphics, and completing visual symbol design. Finally, integrate Unity and Vuforia Engine to design an interactive experience module for Weizhou ink painting production skills, creating an immersive digital interactive environment. In the improved micro ink production process experience display experiment, compared with the existing graphic and textual display methods, the audience's satisfaction and innovation scores for the digital integration method were 19.1% and 21.6% higher, respectively. The conclusion indicates that bioengineering analysis can accurately identify and delay the aging process of ink painting materials, while digital technology has successfully achieved high-quality digital reconstruction of classic works.

Modelling and Optimization of Banana/Plantain Fiber Extraction Systems through Dimensional Analysis

This study presents a comprehensive parametric modelling approach for a locally produced plantain and banana fiber extractor, utilizing the Buckingham Pi theorem to analyze the complex interactions among various operational parameters. Plantain and banana fibers are increasingly recognized for their diverse industrial applications, including textiles, paper, packaging, and food supplements. The mechanical extraction of these fibers is prevalent, necessitating a well-designed process that conserves the natural properties of the fibers while maximizing cost-effectiveness. This research aims to optimize the design and operational parameters of the extractor to enhance its extraction capacity and overall efficiency. Employing the Buckingham Pi theorem, the study constructs dimensionless Pi terms from key parameters impacting the extractor's performance. The analysis reveals a coefficient of correlation (R²) of 0.9923 for extraction efficiency and 0.9867 for extraction capacity, indicating a strong predictive capability of the developed model. These findings provide valuable insights into optimizing the performance of the plantain and banana fiber extractor, ultimately contributing to more efficient and sustainable fiber extraction processes. This model serves as a critical tool for designers and manufacturers aiming to improve the economic viability of fiber extraction systems.