Manufacture Parts Research Articles

Study on Polishing Technologies for Additive Manufacturing Parts

Additive manufacturing has a good application prospect in aerospace, medical implantation and other fields, but the molding surface quality is poor, without post processing can not meet the requirements of high service, polishing processing is a key link in the high-performance metal additive manufacturing technology chain. This paper summarizes the characteristics of the ladder effect, the high roughness of the forming surface. In recent years, additive manufacturing technology, also known as 3D printing, has been highly valued by aviation enterprises for its unique advantage in rapid prototyping, particularly for complex metal parts. However, due to the layer-by-layer growing process of 3D printing, the built parts often have poor surface roughness and are not suitable for practical use without post-treatment. Based on this foundation, the primary focus of research in the field of additive manufacturing for metal parts polishing includes electrochemical, laser, and abrasive flow polishing technologies. The progress in these areas is examined with consideration given to various manufacturing processes, different types of metal powder materials, and diverse structures (such as porous structure and high aspect flow channels) found in additive manufacturing samples. This review summarizes the research findings related to surface roughness, material removal, surface residual stress, profile accuracy retention, and other technical indicators associated with the polishing process for additive manufacturing metal parts. Finally, the paper discusses potential future developments in polishing technology for 3D printed metal parts.

Extending product offers with product-related services: the value of configurators

A significant part of traditional manufacturing companies’ value propositions is provided through product-related services, such as transport, packaging, documentation, installation, and after-sales support. However, for many companies, it is challenging to manage such services. To address this issue, this paper investigates the usefulness of configurators in supporting this task. Specifically, product configurators are widely used in product development and sales processes in engineer-to-order (ETO) companies, where they have produced many benefits. To investigate the feasibility of product-related service configurators (PSCs) in manufacturing companies, this paper develops an approach to developing and implementing PSCs. The approach is tested in a case study to investigate its usefulness and the effects of applying PSCs. The study demonstrates the usefulness of the proposed approach and reveals three main types of benefits from PSCs: (1) increased product-related service cost transparency, (2) improved product-related service offerings based on customer characteristics, and (3) more standardised and structured product-related service specifications.

Machinability optimization of carbon fiber reinforced polymer composites for aviation manufacturing parts

PurposeSurface roughness and delamination during the milling of carbon fiber reinforced polymer (CFRP) composite parts in aviation can lead to component rejection. This article aims to optimize cutting conditions to reduce these failures while ensuring compliance with aviation standards. By improving machinability, the goal is to minimize part rejection rates and scrap, optimizing costs and increasing safety.Design/methodology/approachFull factorial experimental design and response surface methodology (RSM) were used to establish relationships between the cutting parameters and the cutting force, delamination and surface roughness. To validate the model and identify significant parameters, analysis of variance (ANOVA) was performed. The cutting parameters were optimized to reduce cutting force and improve surface quality using ANOVA and RSM.FindingsThe lowest response values can be achieved with a cutting speed of 285.35 m/min and a feed of 358.57 mm/min using the Aluminum Chromium Nitride (AlCrN)-coated tool. Accordingly, the optimum cutting force was obtained as 190.97 N, delamination depth as 1.562 mm and surface roughness as 1.431 µm. It has been seen that the obtained surface roughness and delamination values are consistent with aviation literature studies, sectoral data and standards.Originality/valueThis study uniquely examines cutting force, surface roughness and delamination using Titanium Aluminum Nitride (TiAlN)- and AlCrN-coated tools instead of traditional Poly Cyristaline Diamond (PCD) tools. It employs a two-stage experimental framework, starting with a full factorial design followed by RSM. The initial data have been used as inputs for optimization in the second stage to achieve more accurate results.

A robot in-place geometric model reconstruction method for additive manufacturing parts by fusing 3D scanner measuring data

Abstract Geometric model reconstruction of additive manufacturing (AM) parts based on in-place measurement (IPM) is meaningful in realizing the robotic post-processing of AM parts. However, the low motion accuracy of the robot and the low efficiency of the IPM system restrict the development of the in-place model reconstruction. This paper presents a reconstruction method that integrates the 3D scanner and the robotic IPM system to improve the accuracy and efficiency of the model reconstruction. First, considering the measurement errors in the robotic IPM system, an error correction model is developed by measuring the calibration ball (CB) at the boundary of the measurement space. For each CB, a modified correction matrix (MCM) is constructed based on measurement data and geometric parameters of the CB to compensate for measurement errors. Second, a CB in the measurement space is measured, and the MCMs of each CB at the boundary are combined using the neural network model. Based on the measurement data of the CB, a geometric error correction model for the entire measurement space is developed, which corrects the spatial distribution of geometric errors in the robotic IPM system and enhances its accuracy within the measurement space. Finally, a local motion algorithm based on IPM data is proposed to calibrate the large volume of point cloud data from a 3D scanner. A high-precision model of AM parts is quickly reconstructed by fusing the few high-accuracy robotic IPM data with the large volume of low-accuracy point cloud data obtained by the 3D scanner. In order to verify the effectiveness of the method proposed in this paper, the measurement experiments on the CB are conducted. The results show that the model reconstruction accuracy can reach 0.06 mm compared with point cloud data obtained by the 3D scanner. The AM concave and convex parts are measured and the manufacturing geometric accuracy is analyzed by the proposed method. Compared to the digital model, the maximum deviation of the AM concave part is 1.3864 mm, and the maximum deviation of the AM convex part is 1.6802 mm.

Development of an autonomous Internet of Things system for distributed production of boxed flat sheet metal parts in railcar manufacturing

AbstractRailcar manufacturing takes place in a multi‐tier production environment, where scheduling and resource allocation are two of many critical components of operations management. In general, organizations harness the opportunities presented by enterprise information systems (EIS) with connected devices to enhance the efficiency of their supply chains. However, systematic vulnerabilities emerge in these EIS due to a lack of unified and synchronized data management systems across enterprises. Industry 4.0 (i4.0) prompted the adoption of cyber‐physical systems (CPS) in industry, creating new opportunities to address these vulnerabilities. A key challenge identified in planning and developing autonomous Internet of Things (IoT) system lies in integrating and managing data from multiple sources, which come in various formats, and is generated in different scales, using different platforms. Hence, the study centred on developing a cloud‐native autonomous manufacturing application, with bi‐directional data flow for production of boxed flat sheet metal parts in a distributed network of manufacturing suppliers. Serverless computing enables integration of information technology and operational technology (IT/OT) for real‐time control and monitoring of industrial Internet of Things operations (IIoT). An autonomous manufacturing system is developed for the closed‐loop manufacturing supply chain (SC), utilizing a system‐to‐system (SoS) approach.

Powder Bed Additive Manufacturing Using Machine Learning Algorithms for Multidisciplinary Applications: A Review and Outlook

Additive manufacturing overcomes the limitations associated with conventional processes, such as fabricating complex parts, material wastage, and a number of sequential operations. Powder-bed additives fall under the category of additive manufacturing process, which, in recent years, has captured the attention of researchers and scientists working in various fields of science and engineering. Production of powder bed additive manufacturing (PBAM) parts with consistent and predictable properties of powders used during the manufacturing process plays an important role in deciding printed parts' reliability in aeronautical, automobile, biomedical, and healthcare applications. In the PBAM process, the most commonly used powders are polymer, metal, and ceramic, which cannot be effectively used without understanding powder particles' physical, mechanical, and chemical properties. Several metallic powders like titanium, steel, copper, aluminum, and nickel, several polymer polyamides (nylon), polylactide, polycarbonate, glass-filled nylon, epoxy resins, etc., and the most commonly used ceramic powders like aluminum oxide (Al2O) and zirconium oxide (ZrO2) can be utilized depending upon the method being adopted during PBAM process. Adoption of some post-processing techniques for powder, such as grain refinement can also be employed to improve the physical or mechanical properties of powders used for the PBAM process. In this paper, the effect of powder parameters, such as particle size, shape, density, and reusing of powder, etc., on printed parts have been reviewed in detail using characterization techniques such as X-ray computed tomography, scanning electron microscopy, and X-ray photoelectron spectroscopy. This helps to understand the effect of particle size, shape, density, virgin and reused powders, etc., used during the PBAM process. This article has reviewed the selection of appropriate process parameters like laser power, scanning speed, hatch spacing, and layer thickness and their effects on various mechanical or physical properties, such as tensile strength, hardness, and the effect of porosity, along with the microstructure evolution. One of the drawbacks of additive manufacturing is the variability in the quality of printed parts, which can be eliminated by monitoring the process using machine learning techniques. Also, the prediction of the best combination of process parameters using some advanced machine learning algorithms (MLA), like random forest, k nearest neighbors, and support vector machine, can be effectively utilized to quantify the performance parameters in the PBAM process. Thus, implementing machine learning in the additive manufacturing process not only helps to learn the fundamentals but helps to identify, predict and help to make actionable recommendations that help optimize printed parts quality. The performance of various MLAs has been evaluated and compared for projecting future research directions and suggestions. In the last part of this article, multidisciplinary applications of the PBAM process have been reviewed in detail. Additive manufacturing processes carried out by using conventional machines, called hybrid additive manufacturing, have also been reviewed by discussing their methods and arrangements in detail.

Minimizing distortion with additive manufacturing parts using Machine Learning

Additive manufacturing technologies are increasingly being used in both academic and corporate settings as an efficient and precise method of manufacturing. However, the accuracy of affordable 3D printing methods is compromised by distortion of the plastic filament. These inaccurate 3D prints may not be usable; incorrect sizing of specific features, such as holes, can mean the part does not fit in the larger assembly and would need to be discarded. This can contribute to plastic pollution due to mismanagement, compounded by difficult access to the recycling process of the most popular filament, Polylactic Acid (PLA). In this study, we investigated to what extent the distortion introduced in the additive manufacturing process can be predicted and minimized. We hypothesized that predicting the computer-aided design (CAD) dimensions based on historical print misalignments would be effective in minimizing this distortion. We used a histogram-based gradient boosting regressor and trained it on 3D printing data of hole dimensions printed in PLA filament, obtained from Mendeley Data. Our model predicts the CAD geometry that results in the desired final print dimension, accounting for distortion. The model had a root mean squared error of 0.0614 mm, a mean absolute error of 0.0452 mm, and an R2 score of 99.617%. These results can be used to minimize inaccuracies and prevent waste production.

COMPARISON OF 3D-PRINTED PARTS’ QUALITY USING PRINTERS WITH “COREXY” AND “DELTA” KINEMATICS

Additive manufacturing, or 3D printing, describes technologies that manufacture parts by depositing thin layers of molten material on top of each other and creating the final part layer by layer. Each layer is built on the basis of geometry designed in CAD systems. Additive manufacturing technology opens up new design approaches: "design manufacturing" versus the traditional "design for manufacturing" approach. Geometric freedom allows you to design products as they are visualized, without manufacturing restrictions. Recently, 3D printers with Delta-type kinematics have gained popularity, which is an alternative to standard, cartesian 3D printers. These models use a more complex control system due to differences in the generation of print paths, but may have some advantages over a cartesian configuration. In order to expand the knowledge of additive manufacturing, a comparative study was conducted with cartesian and Delta printers to evaluate the printing performance of the test part. This article examines 3D printers with CoreXY and Delta kinematics, compares their characteristics, and identifies key differences in printing processes. As an example, for quality comparison, arbitrary parts printed in batches of three units from the same material with the same settings inside the slicer program were considered. Parts from both 3D printers were scanned using a LIDAR scanner, and the resulting scan models were transferred to a CAD environment for comparison. The results of the comparison were obtained by the shape and quality of the surface, the production time of one part and batch of parts, mass and dimensional characteristics. Looking at the results, it can be seen that the parts printed by the 3D printer with Delta kinematics have a better surface quality without post-processing, while the parts printed by the 3D printer with kinematics CoreXY correspond as closely as possible to the dimensions specified in the CAD model.

Path Planning Algorithm Analysis of Multiple AGV

Intelligent logistics is an important part of intelligent manufacturing, and Automated Guided vehicle (AGV), as a part of seamless enterprise production system and storage system, is an important technical equipment to achieve intelligent logistics, and has been widely used in factories, warehousing and logistics environment such as automatic handling scenarios. Since a single AGV in the same site can no longer meet the trend of automated factory, it is important to effectively schedule and plan paths for multiple AGVs in limited space. Aiming at multi-AGV system, this paper analyzes and organizes the common path planning algorithms in the market, in order to improve the effectiveness and safety of AGVs. The A* algorithm and the ant colony algorithm's principles will be analyzed in the first part of the paper. Then describe the research status of the two respectively and summarize the advantages and defects respectively. Finally, prospect the development progress of AGV coordinated scheduling and path planning.

Recent Advances in the Synthesis of Sulfonamides Intermediates

AbstractSulfonamides are one of the most important synthons in drug synthesis, which can increase the water solubility of drugs and regulate their metabolism in vivo. According to statistics, nearly 30% of sulfur-containing drugs on the market contain sulfonamide groups, including omeprazole, hydrochlorothiazide, and other best-selling drugs. Synthesis of sulfonamide is therefore a very important part of new drug development and active pharmaceutical ingredient manufacturing. In this review, we will focus on the recent 5-year advances in the field of synthetic research and structural modification of sulfonamide-containing drugs and their intermediates. The synthesis strategies, including S−N construction, C−N cross-coupling, N−H functionalization, and C−H sulfonamidation, are discussed, hoping to provide new ideas for the researchers to prepare sulfonamides in a green and efficient way.

Selective Deposition and Fusion of AISI 316L: An Additive Manufacturing Process for Space Environments via Direct Ink Writing and Laser Processing

Abstract Unlocking the potential of additive manufacturing (AM) for space exploration hinges on overcoming key challenges, notably the ability to manufacture or repair parts on-site during exploration missions with consideration of quality, feedstock utilization, and challenges involved in microgravity environments. While there are multiple efforts to investigate the use of existing metal AM processes such as powder bed fusion (PBF), directed energy deposition (DED), and filament-based material extrusion, each process comes with a different set of challenges in space environments. Here, we introduce a new AM method that integrates the benefits of direct ink writing (DIW) to selectively deposit metallic pastes with laser-based processing to locally debind and subsequently melt and fuse metal powder, layer by layer, enabling the manufacturing of AISI 316L samples with densities exceeding 99.0%. The impact of process parameters on single-track dimensions, surface morphology, and porosity was characterized. The efficacy of laser debinding was assessed via secondary-ion mass spectrometry, permitting the carbon content to be estimated at 0.0152%, which is safely below the acceptable limit (0.03 wt%) for AISI 316L.

Enabling additive manufacturing part inspection of digital twins via collaborative virtual reality

Digital twins (DTs) are an emerging capability in additive manufacturing (AM), set to revolutionize design optimization, inspection, in situ monitoring, and root cause analysis. AM DTs typically incorporate multimodal data streams, ranging from machine toolpaths and in-process imaging to X-ray CT scans and performance metrics. Despite the evolution of DT platforms, challenges remain in effectively inspecting them for actionable insights, either individually or in a multidisciplinary, geographically distributed team setting. Quality assurance, manufacturing departments, pilot labs, and plant operations must collaborate closely to reliably produce parts at scale. This is particularly crucial in AM where complex structures require a collaborative and multidisciplinary approach. Additionally, the large-scale data originating from different modalities and their inherent 3D nature pose significant hurdles for traditional 2D desktop-based inspection methods. To address these challenges and increase the value proposition of DTs, we introduce a novel virtual reality (VR) framework to facilitate collaborative and real-time inspection of DTs in AM. This framework includes advanced features for intuitive alignment and visualization of multimodal data, visual occlusion management, streaming large-scale volumetric data, and collaborative tools, substantially improving the inspection of AM components and processes to fully exploit the potential of DTs in AM.

Using the Ishikwa diagram for problem analysis in the laser cutting process

Laser cutting is a computer numerically controlled process widely used in the automotive industry for cutting metal materials needed to manufacture parts used in automobile production. The production process represents all the conscious actions of a company's employees carried out with the help of various machines, equipment, or installations on raw materials, materials, or other components to transform them into products or services with a certain market value. The Ishikawa diagram, also known as the cause-and-effect diagram, is often used in industrial process analysis, defect prevention, and product design processes. The paper presents the results of the research conducted on the use of the Ishikawa diagram in analyzing the causes that lead to negative effects in the laser cutting process of S235JRH structural steel pipes. After defining the problem, by applying the "WHO, WHAT, WHERE, WHEN, HOW MUCH, HOW, WHY" method, all possible causes were identified using the "5 Whys" method. The analysis identified 11 probable causes, and through further analysis, it was concluded that 3 of them were already present in the process, influencing the laser cutting of the pipes and generating quality problems. All the identified causes built the Ishikawa diagram consisting of 7Ms.

A code-based method for carbon emission prediction of 3D printing: A case study on the fused deposition modeling (FDM) 3D printing and comparison with conventional approach

This study presents a method for predicting carbon emissions from 3D printing based on the G-code and compares the emissions between FDM and injection molding (IM) processes. Traditional manufacturing methods for (polylactic acid) PLA plastic products involve IM, which require extensive equipment and metal molds to be manufactured first. This process involves repetitive heating and machining, low material utilization, high energy consumption, and high pollution. 3D printing using a gradual accumulation of materials to manufacture parts has transformed traditional manufacturing methods. However, concerns regarding the environmental impact of 3D printing have been raised in previous studies. To achieve sustainability in 3D printing, it is important to make effective adjustments by predicting the carbon emissions before printing. In this study, we propose a method for predicting carbon emissions from 3D printing by integrating processing characteristics with G-code instructions. The deviation of the model was confirmed to be between 3.86% and 5.82% through a case study of FDM 3D printing. The optimal process parameters were determined from carbon emissions predictions, resulting in carbon emissions reductions of up to 43.77%. Carbon emissions were evaluated using both the traditional IM method and FDM 3D printing for manufacturing PLA plastic products. The results showed that the carbon emissions from FDM were significantly lower than those from traditional IM in small batches or customized production. In the traditional IM method, mold manufacturing produces high carbon emissions, accounting for more than 99% of the entire process. The findings of this study offer valuable guidance to manufacturers in selecting appropriate manufacturing methods and formulating production strategies. By predicting carbon emissions in the FDM process and adjusting the parameters based on product quality requirements, significant reductions in carbon emissions can be achieved during the production of small batches or customized PLA plastic products. The carbon emission prediction method proposed in this study presents a more sustainable solution for 3D printing technology, which is crucial for advancing sustainable production and environmental protection in this field.

Machine learning enabled 3D printing parameter settings for desired mechanical properties

ABSTRACT Additive manufacturing facilitates the production of parts with tailored mechanical properties, yet achieving specific stress–strain responses remains a significant challenge due to the intricate relationship between printing parameters and material behaviour. This study introduces a novel approach utilising long short-term memory (LSTM) models to predict parameter configurations for material extrusion additive manufacturing (MEX) parts, aiming to meet specific stress–strain requirements. The proposed framework transforms raw tensile test data for LSTM compatibility, yielding a coefficient of determination of 0.8648 and a mean square error of 0.1348 in inverse prediction tasks. Additionally, validation with new data resulted in an error percentage below 2%. Our approach enables the efficient design of customised parts with accurate mechanical properties, reducing the need for extensive experimentation and allowing adaptation to various additive manufacturing processes.

Pedestrian safety alarm system based on binocular distance measurement for trucks using recognition feature analysis

As an essential part of modern smart manufacturing, road transport with large and heavy trucks has in-creased dramatically. Due to the inside wheel difference in the process of turning, there is a considerable safety hazard in the blind area of the inside wheel difference. In this paper, multiple cameras combined with deep learning algorithms are introduced to detect pedestrians in the blind area of wheel error. A scheme of vehicle-pedestrian safety alarm detection system is developed via the integration of YOLOv5 and an improved binocular distance measurement method. The system accurately measures the distance between the truck and nearby pedestrians by utilizing multiple cameras and PP Human recognition, providing real-time safety alerts. The experimental results show that this method significantly reduces distance measurement errors, improves the reliability of pedestrian detection, achieves high accuracy and real-time performance, and thus enhances the safety of trucks in complex traffic environments.

Risk Assessment in the Selection of Parts for Additive Manufacturing

Risk Assessment in the Selection of Parts for Additive Manufacturing

Industry 4.0 factors affecting SMEs towards sustainable manufacturing

Industry 4.0 (I4.0) is one of the essential topics that has been researched extensively in the research domain. In the same way, there are also several research available to check the connections between I4.0 and sustainable manufacturing. It is because of the increasing concern of stakeholders over environmental challenges that manufacturing units are often blamed. It is also true that a major part of manufacturing is done by SMEs in almost every developed or developing economy of the world including India. Therefore, the present research work took place to identify the factors that are essential for sustainable manufacturing using I4.0 in Indian SMEs. In the present study, a total of six factors were identified from the previous studies, which are technological factors (TF), organizational factors (OF), environmental factors (ENF), societal factors (SF), economic factors (ECF), and external stakeholders' factors (ESF) considering the triple bottom line (TBL) approach of the sustainability model. Each factor consists of a few sub-factors, and a total of thirty-five factors were developed. The best-worst method (BWM) and Hierarchical Decision Model (HDM) were applied to bring the results. The result suggests that OF and TF are ranked number one and two respectively. ECF and ENF ranked at three and four whereas, ESF and SF ranked at five and six respectively. The study helps firm managers revolutionize their organizations’ approach to the sustainability model by looking at the Industry 4.0 factors affecting SMEs toward sustainable manufacturing. When managers and practitioners try to convert their business digitally, this study also enables them to prioritize the many I4.0 factors that are most important to their organization. The model was validated by a regional application in Saudi Arabia.

Finite Element Analysis and Machine Learning in the Loop Additive Manufacturing Process Parameter Optimization to Acquire the Desired Dynamics of Manufactured Parts

Finite Element Analysis and Machine Learning in the Loop Additive Manufacturing Process Parameter Optimization to Acquire the Desired Dynamics of Manufactured Parts

Investigating the effects of powder feeding rate on microstructure transformation in linear laser beam additive manufactured thick-walled structures

The coarse columnar crystals that grow through multiple layers are commonly observed in laser additive manufactured structures, with fractures tending to occur in regions where there are abrupt changes in grain morphology. This can lead to a degradation of the mechanical properties of the samples. In this study on laser additive manufacturing, thick-walled structures made of 304 stainless steel were created using a linear beam spot with a rectangular powder feeding nozzle. By adjusting the powder feeding rate to influence the thermal cycling characteristics during the laser additive manufacturing process, a hierarchical grain structure that combines coarse and fine equiaxed grains was achieved, enhancing the forming efficiency and overall mechanical performance of the structure. The results from the thermal cycling characteristics and microstructure analysis indicate that increasing the powder feeding rate during the additive manufacturing process can decrease the hierarchical cooling rate, temperature gradient, and heat accumulation effect. This reduction in turn decreases the grain size and facilitates the transformation of columnar crystals into equiaxed crystals. Furthermore, the transformation of low-angle grain boundaries into high-angle grain boundaries in the interlayer region helps to reduce stress concentration, weaken anisotropic tendencies, and mitigate intergranular fracture tendencies, ultimately improving the mechanical properties of the overall structure. In actual engineering applications, the powder flow can be adjusted to control the temperature gradient and cooling rate of the deposition layer, thereby altering the grain morphology and optimizing the mechanical properties of additive manufacturing parts.