Manufacture Parts Research Articles

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.

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.

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.

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 de-bind 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 de-binding 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.

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.

Drilling parameter optimisation for three-dimensional prints of acrylonitrile butadiene styrene

Abstract Over the past decade, additive manufacturing has transformed the industry, leading to a range of innovative products. In this study, the acrylonitrile butadiene styrene specimen was produced using the fused deposition modeling process. To assess the effectiveness of additive manufacturing parts in final product development, it’s crucial to establish the optimal drilling process parameters to minimize thrust force and torque. The investigation utilized two different drill geometries. Drilling with an end mill resulted in a 24.22% reduction in thrust force and a 12.37% decrease in torque compared to using a twist drill. Additionally, factors such as orientation, infill density, feed rate, and drill geometry have a significant impact on drilling forces.

Importance of RNase monitoring during large-scale manufacturing and analysis of mRNA-LNP based vaccines

Ribonucleases (RNases) are ubiquitous in nature, being able to cleave a wide range of polyribonucleotides. While the presence of microbial and viral contamination in sterile manufacturing is highly studied and controlled, there are no standardized practices for evaluating RNase in the production facility. Since the COVID-19 pandemic, mRNA-LNP based vaccines have become part of routine large-scale manufacturing. The unstable nature of mRNA poses new challenges to safeguard the working efficacy of mRNA – Lipid nanoparticle (LNP) based vaccines or therapeutics, where the presence of RNase in the formulation process could have a profound impact on the mRNA integrity. In this article, lessons learned are presented with respect to the evaluation of RNase contamination during LNP drug product formulation and analysis. Using sensitive detection methods, the potential presence of RNase in the manufacturing of mRNA-LNPs was investigated. Additionally, capillary gel electrophoresis (CGE) data, used to measure mRNA integrity, demonstrate the quality of the active mRNA substance and importance of suitable RNase control strategies. The results and cases presented in this paper should pave the way forward for evaluation and control strategies dedicated to mRNA-LNP based vaccines and therapeutics.

Kinematics-guided data-driven energy surrogate model for robotic additive manufacturing

With the increase in the usage of industrial robots for manufacturing applications, there will be a corresponding rise in energy consumption. Especially, robotic additive manufacturing (AM) has demonstrated significant potential for working autonomously in hazardous or extraterrestrial settings, as well as for producing large-scale structures. Recent advancements have made it possible for teams of robots to collaborate for printing structures even larger than their sizes together. Utilizing robots efficiently for AM is essential for achieving energy sustainability goals. The literature on energy models for robots and AM processes lacks a comprehensive energy model for robotic AM. To address this gap, a multi-point trajectory-based energy model is proposed for robotic AM. The model is created to help users manufacture parts in an energy-efficient way. These simulations require a significant amount of computation time, which limits their usage to offline purposes only. Hence, we propose two energy surrogate models for real-time predictions: a pure data-driven model and a kinematics-guided data-driven model. The kinematics-guided approach is an enhanced version of pure data-driven model which learns from inverse kinematics solutions along the trajectory to understand the robot’s dynamics and kinematics. There were two test cases conducted. The first test case consisted of paths that were feasible, printable, and within the robot’s reach, while the second test case consisted of a mixture of paths that were both feasible and infeasible. In comparison to the pure data-driven approach, the kinematics-guided approach stood out in both testing cases. Two real-world case studies have been used to demonstrate that the proposed approach can be used in lieu of simulations for real-time applications. Especially in the second case, where the part was placed very close to the robot making a portion of the part infeasible to print, the proposed approach correctly identified that it was not possible to print the geometry due to the robot’s kinematics. The proposed kinematics-guided approach is 195 and 700 times faster than the simulation results, respectively, in two case studies, and thus supporting the use of the proposed approach for real-time applications.

Study on ultrasonic-assisted abrasive flow polishing of internal flow channels in additive manufacturing

By studying the movement of ultrasonically assisted abrasives, combined with simulation experiments of abrasive flow in internal flow channels, the study on ultrasonic assisted abrasive flow machining of internal flow channels in additive manufacturing is carried out. Simulations were performed on the channels of AlSi10Mg aluminum alloy printed components, yielding the optimal process parameters for abrasive flow machining: a processing pressure of 10 MPa, abrasive particle size of 270 mesh, and abrasive concentration of 60%. By comparing the surface morphology of the combined straight and curved flow channels before and after applying ultrasonically assisted abrasive flow machining, it was evident that the application of ultrasonic assistance yielded superior removal of surface defects on additive manufacturing parts. However, its ability to remove the spheroidization effect in deeper concave areas is limited. The efficiency of abrasive flow machining and the surface quality of the parts were enhanced with the incorporation of ultrasonic vibration. Under the optimal polishing process parameter combination of an ultrasonic frequency of 20 kHz, a tool head amplitude of 30 μm, and a processing time of 360 s, the surface roughness (Ra) of the straight section reduced to 0.165 5 μm, while the surface roughness (Ra) of the curved section reduced to 0.371 8 μm.

A lightweight parallel attention residual network for tile defect recognition

In modern industrial production, permanent magnet motors are an indispensable part of industrial manufacturing. The quality of the magnetic tiles directly affects the working performance of the permanent magnet motors, making the detection of defects on the surface of magnetic tiles critically important. However, due to the small size of defects on the tile image and the reflectivity of the defective surface, the details of image characteristics are not prominently acquired.These problems bring a lot of difficulties for the recognition of magnetic tile defects. In this paper, a magnetic tile defect detection method is proposed for the probAlems of unclear image features and small defects. First, the image is processed using linear variation to enhance the image detail features. Then, by introducing the inverted bottleneck block structure in MobileNetV2, the Attention Parallel Residual Convolution Block (APR) is proposed, and the Lightweight Parallel Attention Residual Network (LPAR-Net) is built. In APR Block, 7 × 7 convolution is introduced so that the model can extract spatial features from a larger range, and weighted fusion of input images by residual structure. In addition, in this paper, CBAM is improved, split into two parts and inserted into APR Block. Finally, the mainstream image classification models and the LPAR-Net proposed in this paper are used for comparison, respectively. The experimental results show that the method achieves 93.63% accuracy on the adopted dataset, which is better than the existing mainstream image classification network models DenseNet, MobileNet, ConvNext and so on. In addition, this paper introduces a strip steel surface defect dataset and compares it with the above image classification model, which verifies that the detection method proposed in this paper still has strong recognition capability.

Virtual warehousing through digitalized inventory and on-demand manufacturing: A case study

Novel digital on-demand manufacturing technologies provide a significant opportunity to support development of virtual warehousing and in turn improve supply chain performance. However, the implementation of virtual warehouse comes with a set of challenges, especially where the objective is to virtually warehouse standard or legacy parts that have been developed and verified initially for conventional (non-digital) manufacturing. In this paper, we explore the key elements required for successful implementation of a virtual warehouse for legacy parts based on a combination of part digitalization, on-demand manufacturing, and part validation. Our proposed framework for adoption of virtual warehouse includes development of a digital inventory which includes supply chain and manufacturability data, identification, and selection of suitable parts for on-demand manufacturing, selection of on-demand manufacturing technology, fit-for-purpose validation of the parts. Our framework is exemplified through a case study, and we conclude that the building of an effective virtual warehouse requires several enablers, including availability of digital data about technical and supply chain characteristics of parts, but also a suitable part identification tool. This part identification tool needs to be flexible to include comparison with reference parts already produced by different on-demand manufacturing technologies.

Effect of Hot Isostatic Pressing and Solution Heat Treatment on the Microstructure and Mechanical Properties of Ti-6Al-4V Alloy Manufactured by Selective Laser Melting

A powder-bed-based additive manufacturing process called electron beam melting (EBM) is defined by high temperature gradients during solidification, which produces an extremely fine microstructure compared to the traditional cast material. However, porosity and segregation defects are still present on a smaller scale which may lead to a reduction in mechanical properties. It is important to have a better knowledge of the influence of post-fabrication treatments on the microstructure and mechanical characteristics before the use of additive manufacturing parts in specific applications. In this study, the effects of solution heat treatment (SHT) and hot isostatic pressing (HIP) on the microstructure and mechanical properties of Ti-6Al-4V alloy fabricated by the EBM process have been investigated. The SHT and HIP treatments can significantly improve the ductility of EBM Ti-6Al-4V due to the coarsening of α laths and the formation of β grains.

Reactive formation of C3N4 as a by-product of AISI 1070 parts produced by laser powder bed fusion in N2 atmosphere

AbstractIn metal additive manufacturing (AM), inert gases are traditionally used to achieve a controlled atmosphere and mitigate the effects of residual reactive gases. However, the interaction between gases and laser processes, particularly in reactive laser powder bed fusion (RL-PBF) technology, offers the possibility of opening up new avenues for material synthesis. In this experimental work, the authors observed the presence of C3N4 in the residual powder during the manufacture of AISI 1070 steel parts by L-PBF, indicating a reactive process occurred during parts production. This investigation revealed the formation in the working chamber of a waste product containing C3N4 carbon nitride, due to the reaction between the carbon released from the steel and the nitrogen in the chamber. Remarkably, despite carbon depletion, the final product of AISI 1070 steel complies with the specifications of use. Hence, the L-PBF machine was modified to allow black powder sampling from various locations in the chamber. Authors attempted to enhance the production of the C3N4 material by increasing the SED up to 7143 J/mm2 to sublimate a pure graphite rod and concurrently manufacture parts in AISI 1070, in a nitrogen atmosphere. The results obtained at higher SED values showed that in both cases (graphite rod or AISI 1070 steel) a C3N4 compound in the black powder is formed in the investigated atmosphere by reaction of nitrogen atoms with the carbon atoms vaporized by the laser beam. Thus, the study highlights the novel achievement of synthesizing carbon nitride as a high-value by-product while producing functional AISI 1070 steel parts via L-PBF through reaction with nitrogen atmosphere.

Tribological Performance of Additive Manufactured PLA-Based Parts.

Polymer additive manufacturing has advanced from prototyping to producing essential parts with improved precision and versatility. Despite challenges like surface finish and wear resistance, new materials and metallic reinforcements in polymers have expanded its applications, enabling stronger, more durable parts for demanding industries like aerospace and structural engineering. This research investigates the tribological behaviour of FFF surfaces by integrating copper and aluminium reinforcement particles into a PLA (polylactic acid) matrix. Pin-on-disc tests were conducted to evaluate friction coefficients and wear rates. Statistical analysis was performed to study the correlation of the main process variables. The results confirmed that reinforced materials offer interesting characteristics despite their complex use, with the roughness of the fabricated parts increasing by more than 300%. This leads to an increase in the coefficient of friction, which is related to the variation in the material's mechanical properties, as the hardness increases by more than 75% for materials reinforced with Al. Despite this, their performance is more stable, and the volume of material lost due to wear is reduced by half. These results highlight the potential of reinforced polymers to improve the performance and durability of components manufactured through additive processes.

Mass reduction method for topology optimisation of a Ti6Al4V part for additive manufacturing

Abstract Additive manufacturing and topology optimization provide new possibilities to produce complex parts. They can be used separately but with joint applications as a mutually reinforcing solution in component development tasks. The results obtained using the design software can be refined even further depending on the specific goal set. This paper deals with mass reduction with stiffness-based topology optimization of a structural component. The effect of different design spaces, load cases, and design parameters were examined. Then, the new part was validated with FEA simulation. After the validation, the part was prepared for 3D metal printing. Based on the research results, we present a methodology that can be used as a solution considering the software’s limitations and the development of the specific component. Applying the methodology developed in the research makes it possible to achieve mass minimization on other parts with a similar method.