Manufacturing Of Components Research Articles

Study on nanosecond pulsed laser micro-milling of stainless steel miniature gears with intersecting-axes

The utilization rate of miniature gears has increased sharply with the widespread application of micro-pumps, electronic equipment, and measuring instruments, and therefore the design and processing of miniature gear has become a subject of intense research. Current research has predominantly focused on machining miniature parallel-axes gears, but well-established machining methods for miniature intersecting-axes gears are still lacking. This paper presented a nanosecond pulsed laser micro-milling technology for miniature intersecting-axes gear (MIAG). Firstly, the 3D models of driving MIAG and driven MIAG were established, and the machining schemes were proposed on the basis of the characteristics of the models. Then, the influences of processing parameters such as defocusing distance, pulse overlap, and scanning strategy on the output responses, including removal rate, surface roughness, and shape accuracy were analyzed. Afterward, some miniature structures, such as miniature grooves, cylinders and cones, were machined by combining these process parameters. Finally, the MIAGs were machined, and the accuracy evaluation and kinematics experiment were carried out. The diameter of driving MIAG was 1.21 mm, and the diameter of driven MIAG was 3.389 mm. The experimental results indicate that the proposed machining technology can process MIAG with relatively high precision and can also offer a valuable reference for the manufacturing of other miniature components.

Development of AI Model for Robotic Vision Inspection of Sheet-Metal Components in Manufacturing

This paper presents an AI-based robotic vision inspection system designed to enhance quality control in the manufacturing of sheet-metal components. The system integrates a sequential AI model with advanced image processing techniques to automate real-time defect detection and classification. Utilizing a high-resolution camera, the system captures images of components on a conveyor, which are pre- processed using grayscale conversion, Gaussian blurring, and Canny edge detection to emphasize structural details. A deep learning model then classifies isolated regions of interest based on normalized, resized images. Feature matching through ORB (Oriented FAST and Rotated BRIEF) enables accurate alignment with reference templates, while automated measurements convert pixel dimensions to physical units, ensuring reliable detection of deviations. With an average accuracy of 88.3%, the system consistently identifies subtle and complex defects, such as scratches and dimensional deviations, under variable lighting and noise conditions. This AI-driven approach reduces the need for manual inspection, minimizes error, and enhances workflow efficiency, representing a major step toward robust, real-time quality assurance solutions in industrial environments.

Optimal structural and functional assemblies in additive manufacturing using the African buffalo-inspired part segregation algorithm

In multilevel or additive production, reduction of the total manufacturing duration is crucial. This may be achieved by harmonizing assembly and production time and by simultaneous collaboration with component manufacturers, thus incurring fewer delays. Here, a novel approach, the African buffalo-inspired part segregation algorithm, is proposed for optimal structural and functional assemblies in additive manufacturing. A mixed-integer nonlinear programming model is developed to assess the near-term optimum criteria area and dispersed materials in a computational manufacturing context. The number of support substances, total production period and total assembly costs are reduced in the time-reduction approach. A specific swarm intelligence-part-based segregation strategy is used to choose the appropriate number of element assemblages, and a part division strategy that complies with the criteria for transverse effect, dimensions and elevations is applied. The proposed approach is evaluated for structures with conventional and free-form borders using two real-size three-dimensional representations. The results indicate that this method may reduce overall production time compared to the time spent constructing one component, under all investigated circumstances.

Driving productivity: a comparison of the Indian automobile manufacturers and component suppliers

ABSTRACT This study analyzes the factors influencing productivity within the Indian Automobile industry, specifically examining the component manufacturers and the original equipment manufacturers (OEMs). Utilising a dynamic panel data model, we assess the effects of lagged R&D stock, trade dynamics, and firm-specific factors on total factor productivity spanning the period 2000–2023. Our findings underscore the significant impact of a firm’s lagged R&D stock, lagged export intensity and the import of embodied and disembodied technology in the industry. While lagged export intensity plays a significant role in improving productivity, with a greater impact on the OEM segment, the influence of technology imports differs across both segments. Specifically, OEMs benefit more from the import of embodied technology or capital equipment, while the component segment from disembodied technology or royalty payments and licensing. We also find that electric vehicle penetration has a significant positive impact on the productivity of the OEM segment alone, indicating that the benefits of the new technology do not have a visible impact on the industry as a whole. These results highlight distinct patterns in technological learning between the two segments, unveiling structural variations within the industry.

Jet fuel oxidation on conventionally and additively manufactured metallic tubes

In the aviation industry, jet fuel is a propellant as well as a coolant. At elevated temperatures, the jet fuel experiences significant thermal stresses, which leads to the oxidation of hydrocarbons when exposed to the heat exchanger walls. As a result, undesirable insoluble carbon deposits on the internal heat exchanger walls impede the effectiveness of the heat exchanger and the aircraft fuel circulatory system, compromising aircraft safety and performance. Here, we study the jet fuel fouling behavior on additively manufactured tubes created using stainless steel, aluminum alloy, titanium, and Inconel, during autoxidation at elevated temperatures. Additively manufactured materials are tested due to their numerous potential benefits for the aerospace industry, which include design flexibility for the creation of complex and optimized parts that enhance performance and aerodynamics. Experiments were conducted using a fuel-fouling open loop system based on the Jet Fuel Thermal Oxidation Test (JFTOT). Scanning electron microscopy and characterization of the internal surface of the tubes showed that the jet fuel reacts differently with different metals and alloys. Comparisons were made with traditionally manufactured materials and benchmarked with the performance of bare copper tubes. We show that additively manufactured tubes were more prone to fuel fouling due to the larger inherent roughness associated with the additive manufacturing process. We show that applying a thin layer of commercially available Silcolloy 2000 coating onto the internal surface minimizes jet fuel degradation significantly. Our work helps to enable the application of additive manufacturing for aircraft thermal management component manufacture by alleviating concerns related to fuel blockage and performance deterioration stemming from surface deposition.

Main Heat Transfer Mechanisms in Hot Stamping Processes: a Review

Objectives: The article reviews heat transfer mechanisms in the hot stamping process, highlighting models to calculate the interfacial heat transfer coefficient (IHTC), crucial for optimizing the cooling and quality of formed products. Theoretical Framework: In hot stamping of high-strength steels, the IHTC is affected by variables such as contact pressure, roughness and temperature. Cooling efficiency directly impacts the temperature distribution and martensitic microstructure of the material, essential for structural strength. Method: The review compares IHTC calculation methods, such as inverse heat conduction analysis and Newton's law of cooling, detailing the convection, radiation and conduction mechanisms at each stage of the process: transfer, positioning and forming. Results and Discussion: The inverse heat conduction analysis proved to be the most accurate for determining the IHTC, compared to the finite element method (FEM). The study suggests that efficient cooling channels and high conductivity materials in the matrix reduce cooling time and improve the quality of the final product. Research Implications: Findings provide a basis for improving thermal management in hot stamping, reducing cycle time and increasing productivity. Originality/Value: This work contributes a comprehensive reference on IHTC, assisting researchers and engineers in optimizing heat transfer and production efficiency in the manufacture of automotive components.

Automating the manufacturing process of control cables for the automotive components industry

The automotive industry is of great importance to the global economy for all the jobs it generates, the materials it exploits, or the technical and technological development it drives. The control cables provide essential functions for any car, such as the opening of doors and windows, or activating the handbrake and accelerator. The process of assembling control cables involves numerous steps and the manufacture of various components, e.g., the spiral, inner and outer coating, spiral terminal, and terminals. This work deals with the injection process of control cable terminals. There is a need to separate the injection set into its constituent parts, namely the terminals and the feeding system (gate), which is carried out manually, potentially leading to health problems, e.g., tendonitis. This paper presents the development of an automated pneumatic system for the separation of control cable terminals from the feeding system. The novel pneumatic system addresses a significant gap in the automation of Zamak terminal injection by handling seven different terminal types under strict spatial limitations. The automated solution resulted in a 39% reduction in production time, enhancing the process efficiency. Moreover, by adopting the Design Science Research (DSR) methodology, the work contributes not only to industrial practice but also to the theoretical understanding of the process. This approach to automating a repetitive and ergonomically challenging task represents a step forward in the field of manufacturing technology that can be extended to other fabrication processes, leading to process improvements and competitiveness.

Thermal damage diagnosis in micro grinding processes based on raw acoustic emission signals and convolution neural networks

A micro-grinding process is usually the last machining operation among the various processes employed in the manufacturing of metallic components. It is generally the first option when the manufactured products require a combination of low surface roughness values and narrow dimensional tolerances. However, one of the main challenges of a micro-grinding process is avoiding high specific energy and the corresponding intense heat generation, as this can damage the workpiece. The energy consumed during micro-grinding is almost entirely converted into heat, making the component susceptible to thermal damage. As the occurrence of this damage compromises the service life of manufactured components, the implementation of monitoring systems is essential to guarantee the quality of the manufactured products improving process efficiency. In this study, a new approach based on convolution neural networks (CNNs) is proposed to predict the occurrence of thermal damage in the micro-grinding process. This approach uses raw acoustic emission (AE) signals as inputs into CNN, which is a more technically feasible approach to performing real-time monitoring. To obtain the AE data, micro-grinding experiments were conducted on N2711 grade steel (which is susceptible to thermal damage) under different cutting conditions (ae = 5–50 µm). The obtained AE signals were labeled based on the offline analyses performed on the workpieces, which identified the occurrence of thermal damage through visual inspections, microhardness, and microstructural analysis. For the conditions employed in this work, thermal damage was obtained after grinding with depth of cut values > 10 µm. Total 3960 AE signals were obtained of which 2200 of them were associated with thermal damage. The results depict that the proposed CNN model was able to successfully classify normal and thermal damage AE signals reaching an accuracy of 98.6% over the dataset (0.4% false positives and 1.0% false negatives).

Carbon Footprint of Additively Manufactured Precious Metals Products

Traditionally, precious metals are processed by either lost-wax casting or the casting of semi-finished products followed by cold or hot working, machining, and surface finishing. Long process chains usually conclude in a high material input factor and a significant amount of new scrap to be refined. The maturing of Additive Manufacturing (AM) technologies is advantageous with regard to resources among other criteria by opening up new processing techniques like laser-based powder bed fusion (LPBF) for the production of near net shape metal products. This paper gives an insight into major advantages of the powder-based manufacturing of precious metal components over conventional methods focusing on product carbon footprints (PCF). Material Flow Cost Accounting (MFCA) for selected applications show energy and mass flows and inefficient recoverable losses in detail. An extended MFCA approach also shows the greenhouse gas (GHG) savings from avoiding recoverable material losses and provides PCF for the products. The PCF of the precious metals used is based on a detailed Life Cycle Assessment (LCA) of the refining process of end-of-use precious metals. In the best case, the refining of platinum from end-of-life recycling, for example, causes 60 kg CO2e per kg of platinum. This study reveals recommended actions for improvements in efficiency and gives guidance for a more sustainable production of luxury or technical goods made from precious metals. This exemplary study on the basis of an industrial application shows that the use of AM leads to a carbon footprint of 2.23 kg CO2e per piece in comparison with 3.17 kg CO2e by conventional manufacturing, which means about a 30 percent reduction in GHG emissions and also in energy, respectively.

The Influence of Ultrasonic Irradiation of a 316L Weld Pool Produced by DED on the Mechanical Properties of the Produced Component

Additive manufacturing of metallic components often results in the formation of columnar grain structures aligned along the build direction. These elongated grains can introduce anisotropy, negatively impacting the mechanical properties of the components. This study aimed to achieve controlled solidification with a fine-grained microstructure to enhance the mechanical performance of printed parts. Stainless steel 316L was used as the test material. High-intensity ultrasound was applied during the direct energy deposition (DED) process to inhibit the formation of columnar grains. The investigation emphasized the importance of amplitude changes of the ultrasound wave as the system’s geometry continuously evolves with the addition of multiple layers and assessed how these changes influence the grain size and distribution. Initial tests revealed significant amplitude fluctuations during layer deposition, highlighting the impact of layer deposition on process uniformity. The mechanical results demonstrated that the application of ultrasound effectively refined the grain structure, leading to a 15% increase in tensile strength compared to conventionally additively manufactured samples.

Tribological performance of 410L martensitic stainless steel parts manufactured by Laser directed energy deposition

The processing of AISI 410L martensitic stainless steel (MSS) by laser directed energy deposition (L-DED) for additive manufacturing (AM) of wear-resistant components is an alternative to the high C-content MSS grade, with difficult processability. This work assesses the tribological performance of low C-content 410L MSS additively manufactured by L-DED. Two sets of L-DED parameters were investigated, one targeting Production and high deposition rates, and the other for Resolution and increased geometric freedom. Half of the multilayer samples were subjected to a post heat-treatment route. Samples were evaluated by the dry-sliding linearly reciprocating ball-on-flat tribological test (ASTM G133) in the as-built and heat-treated states. Commercially annealed 410 MSS was the reference material. In the L-DED as-built, microstructures were composed of ferrite with martensite at the grain boundaries. Heat-treatment provides recrystallization, martensite tempering, and hardness decrease. The more refined microstructure of L-DED resolution as-built led to greater hardness and lower wear coefficient in the ASTM G133 test. Wear tracks revealed wider bands of compact tribolayers on its surface, which reduced the action of adhesive and abrasive mechanisms. The other L-DED as-built and heat-treated conditions, and 410 reference material demonstrated a large amount of free debris, indicating lower wear resistance.

Attack of discarded soda-lime glass with sodium aluminate for the manufacturing of sustainable geopolymer components

Discarded soda-lime glass (SLG) may contain small amounts of ceramic, metallic, and polymeric contaminants, therefore recycling of this material is far from ideal. The quality of newly made glass products made by remelting of cullet, as a result, declines. Consolidation at low temperatures to form materials similar to geopolymers could enable the complete re-use of contaminated cullet. SLG powders, either as received or after pre-washing in an acid solution, were added to a sodium aluminate solution and mechanically stirred at a low speed for three hours at room temperature. The formation of microporous semi-crystalline monoliths involved suspensions casting into plastic molds, followed by an overnight cure at 75 °C. The monoliths prepared from both untreated SLG particles and pre-washed SLG particles contained crystalline phases of zeolite LTA and hydrosodalite. The mechanical characteristics showed good agreement with the properties of cementitious materials, with compressive strength ranging from 22 to 29 MPa and flexural strength ranging from 13.2 to 19.9 MPa. Furthermore, the technique effectively produced Venetian terrazzo-like samples by adding coarse glass particles as fillers with particle sizes of up to 3 mm, which could lead to significant material and energy savings in their fabrication. The suggested method could be expanded to include other challenging-to-reuse glass formulations, providing attractive and versatile recycled materials.

Environmental Impact of Wind Farms

The aim of this article is to analyse the global environmental impact of wind farms, i.e., the effects on human health and the local ecosystem. Compared to conventional energy sources, wind turbines emit significantly fewer greenhouse gases, which helps to mitigate global warming. During the life cycle of a wind farm, 86% of CO2 emissions are generated by the extraction of raw materials and the manufacture of wind turbine components. The water consumption of wind farms is extremely low. In the operational phase, it is 4 L/MWh, and in the life cycle, one water footprint is only 670 L/MWh. However, wind farms occupy a relatively large total area of 0.345 ± 0.224 km2/MW of installed capacity on average. For this reason, wind farms will occupy more than 10% of the land area in some EU countries by 2030. The impact of wind farms on human health is mainly reflected in noise and shadow flicker, which can cause insomnia, headaches and various other problems. Ice flying off the rotor blades is not mentioned as a problem. On a positive note, the use of wind turbines instead of conventionally operated power plants helps to reduce the emission of particulate matter 2.5 microns or less in diameter (PM 2.5), which are a major problem for human health. In addition, the non-carcinogenic toxicity potential of wind turbines for humans over the entire life cycle is one of the lowest for energy plants. Wind farms can have a relatively large impact on the ecological system and biodiversity. The destruction of animal migration routes and habitats, the death of birds and bats in collisions with wind farms and the negative effects of wind farm noise on wildlife are examples of these impacts. The installation of a wind turbine at sea generates a lot of noise, which can have a significant impact on some marine animals. For this reason, planners should include noise mitigation measures when selecting the site for the future wind farm. The end of a wind turbine’s service life is not a major environmental issue. Most components of a wind turbine can be easily recycled and the biggest challenge is the rotor blades due to the composite materials used.

Polylactide Composites Reinforced with Pre-Impregnated Natural Fibre and Continuous Cellulose Yarns for 3D Printing Applications.

Achieving high-performance 3D printing composite filaments requires addressing challenges related to fibre wetting and uniform fibre/polymer distribution. This study evaluates the effectiveness of solution (solvent-based) and emulsion (water-based) impregnation techniques to enhance fibre wetting in bleached flax yarns by polylactide (PLA). For the first time, continuous viscose yarn composites were also produced using both impregnation techniques. All the composites were carefully characterised throughout each stage of production. Initially, single yarns were impregnated and consolidated to optimise formulations and processing parameters. Solution impregnation resulted in the highest tensile strength (356 MPa) for PLA/bleached flax filaments, while emulsion impregnation yielded the highest tensile strength for PLA/viscose filaments (255 MPa) due to better fibre wetting and fibre distribution. Impregnated single yarns were then combined, with additional polymer added to produce filaments compatible with standard material extrusion 3D printers. Despite a reduction in the mechanical performance of the 3D-printed composites due to additional polymer impregnation, relatively high tensile and bending strengths were achieved, and the Charpy impact strength (>127 kJ/m2) for the viscose-based composite exceeded the reported values for bio-derived fibre reinforced composites. The robust mechanical performance of these filaments offers new opportunities for the large-scale additive manufacturing of structural components from bio-derived and renewable resources.

Investigation on the mechanical and environmental behaviour of 3D-printed molds for manufacturing of CFRP components

Investigation on the mechanical and environmental behaviour of 3D-printed molds for manufacturing of CFRP components

Microstructure control of ultra-high-strength AlZnMgCu alloys fabricated by wire arc directed energy deposition: the role of variable polarity electric pulse

ABSTRACT In the manufacturing of large-scale ultra-high strength AlZnMgCu alloy components, wire arc directed energy deposition can be an efficient and flexible candidate. However, the fabricated AlZnMgCu alloys are faced with problems like coarse microstructures and high sensitivity to defects. Although metallurgical methods like micro-alloying and ceramic particle addition have shown considerable effects in microstructural control, pure process control methods that don’t affect the composition still lack investigation. This work proposed an effective control method by simply adding electric pulses during manufacturing. Results show that the application of pulses significantly reduces pore defects. A 2 Hz pulse can reduce the porosity by 65.7% to 0.122%. Low-frequency pulses effectively promote grain refinements. At 2 Hz, the deposited metal exhibited a mix of short columnar and fine equiaxed grains with a near-random orientation. Applying electric pulses leads to at least a 4.8% increase in UTS and a 225.0% increase in elongation.

How does progressive subsidy reduction affect the innovation and economic performance of new energy vehicle firms?

Effective subsidy withdrawal methods are important to minimize the adverse effects of such withdrawal on the industries supported by these subsidies. China adopted the progressive subsidy reduction policy (PSRP) for the new energy vehicle (NEV) industry in 2017. We examine how this policy impacts the innovation and economic performance of NEV firms (NEVFs). We first build a theoretical model to explore the evolution of the intrinsic relationship between NEVFs’ innovation and their economic performance. Then, we exploit the PSRP's implementation as a quasi-natural experiment, and examine its effects on the NEVFs’ innovation and economic performance based on Chinese listed firm data from 2015 to 2021. We find that the PSRP encourages NEVFs to improve innovation, but harms their economic performance in the short run. Furthermore, the PSRP promotes the innovation of firms in the growth and mature stages, original equipment manufacturers, and state-owned enterprises (SOEs), but does not affect their economic performance. Finally, the PSRP fails to improve the innovation of firms in the decline stage, component manufacturers, and non-SOEs, and worsens their economic performance. These findings provide valuable insights for governments on designing subsidy reduction policies and corresponding coping strategies for firms.

Robustness of prefabricated construction supply chain network against underload cascading failure

A prefabricated construction supply chain(PCSC) is a complex network with high interdependency between entities. After disturbance, it is prone to cascading failure, leading to project delays or budget overruns. Therefore, it is necessary to model a robust network against cascading failure to achieve a sustainable prefabricated construction system. This study explores the functional robustness of a prefabricated construction supply chain network (PCSCN) against underload cascading failure. First, the PCSCN is constructed as a three-echelon supply chain network based on complex network theory, which can characterize the general characteristics of PCSC and provide the network foundation for the subsequent numerical simulation research. Then, a more realistic underload cascading failure model that adds the new element of substitute nodes is established to describe load loss propagation in the PCSCN. Finally, the Order Fulfillment Rate(OFR) is used as the robustness index to quantify network robustness from a functional perspective. The numerical simulation results indicate that in the PCSCN, the larger the initial load is, the more important the node, and component manufacturers are more important than building material suppliers. In addition, the node capacity upper bound parameter α has a positive relationship with robustness, the failure coefficient β has a negative relationship, and the edge weight adjustment coefficient θ has no significant impact on robustness. This research can provide guidance for developing cascade control and defense strategies in PCSCN risk management.

Smartification Furniture Manufacturing: A Furniture Prototype with Thermal-Based Sensors (Visions and Challenges)

Considering the swift advancement of technology paralleling the industrial revolution, Industry 4 and Industry 5 have emerged. The industry's developing focus has shifted beyond merely incorporating technical and interactive components. Concern for human beings and user's well-being has become a fundamental aspect of the furniture industry. This study proposes the manufacturing of an interactive furniture component that can be produced with limited skills. This prototype of smart furniture, utilizing existing materials, enhances safety and improves the security health of the users in the environment. The product is a living room table that produces a light warning when a thermal object, which may pose a risk of external heat to the user and occupants, is detected. This device is designed for consumers requiring continuous assistance, specifically children, the elderly, and the deaf. This will enable them to integrate into family dynamics without the need for a caregiver to alert them to potential hazards. The light warning followed international design guidelines, using red for severe heat and blue for extreme cold. The study provides a strategic design framework that assists the industry in developing interactive furniture production. This methodology also provides a development concept for common pre-manufactured furniture and techniques for incorporating interactive features. The study encountered multiple problems in practical implementation, the foremost being the lack of existing industrial design models. We encountered logistical challenges with the availability of raw materials and the compatibility among the industrial service provider, the furniture designer, and the techie tasked with integrating the interactive component.

AI-based spatially resolved parameter prediction in laser metal deposition for increased process stability

In the additive manufacturing of components using powder-based laser metal deposition (LMD), the heating of the volume during buildup is a decisive factor for process stability and contour accuracy. If the process parameters remain constant, this intrinsic heating leads to deviations in the deposited layer thickness during the process because of changes in the melt pool volume. This leads to contour deviations and potentially causes the process to fail if the process parameters are no longer within the suitable range. Particularly in the case of complex geometries, this previously required time-consuming process development for adapted process parameters and buildup strategies. This paper examines the potential of data-driven approaches to enhance the stability and precision of LMD processes. To this end, a machine learning (ML) model is employed to optimize laser power settings. The objective of the study is to reduce the thermally induced geometric deviations that often occur during the LMD process by utilizing experimental data. The methodology employs the use of the alloy Inconel 718, renowned for its high strength and temperature resistance, in conjunction with the utilization of computer numerical control machines equipped with laser and imaging systems. The ML model is trained to predict the optimal laser power required to obtain consistent melt pool properties. The results demonstrate that the ML approach is an effective means of reducing geometric deviations.