Modern Manufacturing Technology Research Articles

Grey-Taguchi approach to optimize the tribological performance of hybrid composites

Modern manufacturing technology is evolving, necessitating the creation of materials with higher wear resistance. This research endeavors to forecast the dry sliding wear performance of AL6061-B4C-RM hybrid composites by employing Taguchi and grey relational analysis approaches. Present study applies the L16 Taguchi approach to optimize the process parameters specific wear, and coefficient of friction (COF) at four different levels. Using multiple responses for optimization for both responses, a single process parameter setting is obtained via grey relational analysis. In AL6061 composites, varying weight proportions of Boron Carbide particles (B4C) were added as reinforcements while keeping the red mud (RM) content constant at 2 %. To recognize the dominant factor, analysis of variance (ANOVA) was implemented, and the outcomes of the assessment showed speed plays a crucial role, followed by the weight percent of reinforcement and the load in determining the specific wear rate and COF. A research trial was completed to confirm the outcomes obtained from the combination of optimal parameters.

STRESS, EQUIVALENT STRAIN AND DEFORMATION FOR MACHINING OPERATION UTILIZING EXPLICIT DYNAMIC ANALYSIS FOR VARIOUS MATERIALS

Subtractive and additive manufacturing methods constitute the two main categories of modern manufacturing technology used in the manufacturing sector. The subtractive method is used for material removal and is performed in (Brass, Aluminum and Teflon), the machining process is achieved using CNC (Computer Numerical Method) turn machine. The main objective of the paper is to perform FEA (Finite Element Analysis) over the three material (Brass, Aluminum and Teflon). The CNC machine is used to perform a machining over the three-material stated above, but within the CNC machine there is no provision for achieving the various values of (stress, strain and deformation) during the machining operation. During the machining process, it’s very difficult to get and accurate value of stress for the tool and raw (test) material, so Ansys FEA analysis method is adopted to predict the parameter like stress, strain and total deformation. The analysis part is achieved by the developing the CAD model using Solid work and FEA analysis using Ansys and thereby the output will be helpful to understand the various stress parameters. The results highlight that Teflon has a higher value for total deformation followed by aluminum and brass. The equivalent strain for the Teflon is higher and followed by brass and aluminum. The stress is acting in axial and compressive direction, so brass have a higher value followed by aluminum and Teflon. This method and analysis procedure can be adopted for understanding the deformation, stress and stain acting on any material, which will be highly useful for material of higher with a higher hardness value.

Machine learning-based design optimization of aperiodic multilayer coatings for enhanced solar reflection

Multilayered coatings are promising and successful for applications in semiconductors, optical mirrors, and energy harvesting technologies. Amongst these, optical mirrors are essential for passive radiative cooling. Building upon the multilayer radiative cooling systems observed in snails and drawing from previous research, this study showcases the efficacy of machine learning algorithms in optimizing and gaining insights into multilayer structures. Due to the constraint of low sky window emissivity in biologically found calcite shells, focusing on solar reflectance becomes crucial to maximize the biological phenomenon found in snails. The manual search of the periodic multilayer design space for calcite with air gaps points to the maximum solar reflectance of ∼89% at 170 nm layer thickness for 20 μm coating. To unlock the full potential of these multilayers, we then employ machine learning-based evolutionary optimization method - a genetic algorithm. The optimized aperiodic coating shows a significant enhancement of solar reflectance to ∼99.8% for a 20 μm coating. Interestingly, the same average layer thickness of 170 nm provides maximum solar reflectance in 20 μm periodic and aperiodic calcite multilayer. Investigation of the spectral reflectance shows that layer thickness is crucial in tuning the solar reflectance. For small coatings, wavelengths with higher solar intensity are prioritized. Increasing the coating thickness allows inclusion of thicker layers to reflect longer wavelengths, leading to increasing trend of average calcite layer thickness. Further work exploring radiative cooling materials shows that calcite and barium sulfate reflect sunlight significantly better than silicon dioxide due to their refractive index contrast. Our findings and insights using bio-inspired design can provide superior solar reflectance utilizing thin coatings with modern manufacturing technology.

Planetary Gearbox Design and Development using Additive Manufacturing

Modern manufacturing technology is dominated by additive manufacturing due to its simplicity, precision, time and material savings, and flexibility in design. The purpose of this study is to propose and print a 3D model of planetary gear trains since these gear trains are more efficient than conventional gear trains and have many advantages. A planet gear trains must have collinear input and output axes in order to achieve compact space requirements. Using even spacing between the planets in a gear train allows both static and dynamic forces to be balanced. It is also possible for multiple planets to produce high torques. Finally, it is capable of providing a wide range of speeds. The printed model performed exceptionally well when it came to performance, and it can be used for both educational and industrial purposes, exhibiting the ability to withstand a high level of stress. The current paper demonstrated the process of 3D printing and the challenges involved in printing planetary gear trains, as well as possible solutions to these challenges. Finally, tolerances were reported as 0.5 mm on the final CAD model, and 0.15 mm on the last updated results.

The meaning of knowledge transfer in educating engineers for modern manufacturing technologies

The development of modern manufacturing technologies such as additive manufacturing and other laser-based manufacturing technologies have increased their usage, especially in engineering education. Education must correspond to different applications of these technologies to ensure the quality of their usage in the manufacturing industry. Laser-based technologies have an impact on society through companies and industry in the manufacturing sector as they are employing graduated students, who are more aware of the possibilities of the technologies. Traditional manufacturing industries are facing the era of digital transformation. There are vast differences between industries and different industry players as well as their ability to keep up with the transformation process. The pace of the process can depend on many different items that are related to the company organization and leadership, technology base and to the abilities to modify these. The pace can also depend on the processes of cooperation and development between other ecosystem players e.g., supply chain partners or development drivers such as customer industries. This study concentrates on one of the key issues, namely learning and adoption of new skills to tackle the change. Universities have a major role in the knowledge transfer from education to working life. To develop this, there is a need to identify the factors in university training or pedagogical choices that can speed up the process of transformation. The aim of this research is to identify the factors for knowledge transfer through arranging efficient education for laser-based manufacturing technologies, especially in engineering education. It was concluded that by educating engineers with sufficient knowledge about the technologies, the manufacturing industry can utilize the knowledge and implement laser-based technologies better in their operations.

Preliminary study of knowledge transfer aspect in the creation of a framework for education and training of digital manufacturing technologies

Digitalization is changing the industry. As this change accelerates its speed, it also requires a transformation process where knowledge transfer between industry and research institutes play a significant role. There is a need to be more fluent, flexible, and efficient in order to get the latest research results into industrial implementation as quickly as possible. The challenge in knowledge transfer is that its speed in the current stage is too slow compared to the speed of development and changes required by digitalization of traditional manufacturing industries. The motivation for this study is the gap in knowledge transfer. One emerging digital transformation is the establishment of modern digital manufacturing technologies, e.g., additive manufacturing (AM). There are different approaches to supporting the industry in this transformation. Knowledge transfer can happen, for example, through education (e.g., master students) and industrial training, but also the fluent transfer of the latest research results from research institutes to companies is needed. University education needs to support the requirements of the manufacturing industry by providing future experts with skills to smooth the transformation process and bring novel technology applications, such as AM, to industrial-scale use. The article discusses how university education can support future competence-building in the industry. Similar needs are also present in industrial training, which universities often provide. Both education and training need to be improved from fundamental approaches to explain how this new knowledge should be created, i.e., how knowledge transfer happens most efficiently. The outcome of this article is the basis of the framework for education and training of digital manufacturing technologies by using modern learning methods and tools. More detailed pedagogical and knowledge transfer models can be developed and applied when this framework is created.

Spherical dome: design, digital twin manufacturing, and testing of a glubam structure

ABSTRACT Domes are common architectural elements in real life, with spherical or sphere-like shapes being the most common. Nonetheless, there has been limited research on incorporating modern design and manufacturing technologies into spherical domes, especially those made of bio-based materials. This paper presents a comprehensive study of the design pipeline for spherical domes. In terms of spherical tessellation for dome design, a set of innovative algorithms and programs is made available as open-source resources, comprising both manual and numerical approaches, where the numerical approaches are validated by existing results while also expanding these results, and new manual approaches are proposed and benchmarked as alternatives to conventional methods with similar or better performance, condition-wise. A proof-of-concept prototype is constructed according to the proposed pipeline to demonstrate its feasibility for later experiments. A preliminary loading test was conducted, validating its satisfactory mechanical behavior. In summary, a comprehensive design pipeline for spherical domes is proposed and validated through a prototype project, during which various innovations are made, from spherical tessellation to parametrical design, analysis, and digital twin manufacturing.

Service Takt: A collaboration mechanism for service-oriented manufacturing

Industrial Internet and modern manufacturing technologies have enabled distributed manufacturing resources to be virtualized as services and dynamically organized into a virtual ally to meet market opportunities. When services orchestrate together, they are expected to coordinate in cycle time, cost as well as information, etc. to obtain optimal performance. However, the distribution, diversity, and uncertain factors often make services face unpredictable disturbances and hinder them to collaborate effectively. In this paper, a mechanism of Service Takt is proposed for services collaboration. By analyzing the typical characteristics of service-oriented manufacturing, the attributes of Service Takt and related collaboration elements are elaborated, and then the calculating processes and feasibility of Service Takt are illustrated with three application cases. By taking into account the managerial requirements as constraints in service collaboration, the adaptability of the proposed mechanism is enhanced to suit various manufacturing scenarios. Based on the interaction between the static and dynamic Service Takts, a closed-loop management model of the manufacturing system is constructed. The experimental results show that the overall efficiency can be improved when services execute in a sound rhythm through this mechanism.

Tile Surface Defect Detection Based on Improved Faster R-CNN

With the increasing population, there is a high demand for housing, commercial buildings, and lifestyle facilities. The development of modern manufacturing technology has led to higher quality control requirements for tiles. This article presents a ceramic tile defect detection method based on Faster R-CNN, primarily focused on detecting surface defects or cracks on tiles. The method replaces VGG16 with the ResNet-101 network as a new backbone and utilizes depth-wise separable convolution to address efficiency issues resulting from the backbone replacement. Finally, Soft-NMS is employed to optimize regression boxes and prevent missed detections. Experimental results demonstrate that the improved algorithm achieves a mAP of 70.4%, a 13.2% enhancement over the original algorithm, highlighting the effectiveness and feasibility of the proposed approach.

Preface

The 7th International Conference on Mechanical, Aeronautical and Automotive Engineering (ICMAA 2023) was held virtually from February 24-26, 2023. On behalf of the Organizing Committee of this Conference, it is my great pleasure and honor to introduce the proceedings of ICMAA 2023.The proceedings include a variety of advanced research topics which were presented in the conference. Full papers submissions were blind peer-reviewed by at least two experts from the International Program Committee in order to ensure high quality standard of research. The papers are evaluated on the basis of their originality, innovation, technical value, and applicability. The selection process followed strict criteria and consequently only 28 full papers were selected and accepted for publishing.ICMAA2023 Conference Committee has chosen Zoom as the virtual platform and invited authors to give oral presentations based on their research works, followed by a short Q&A part. Before the Conference, all the authors are to prepare and submit videos as backups in case of unexpected technical issues. In the Conference, oral sessions were well organized with participation of scientists from all over the world. They actively discussed about Modern manufacturing system and technology, Mechanical structure design and strength analysis, Friction and material life analysis, Power machinery and system simulation and etc.The success of the Conference is in a large way attributed to the expertise and experience of the thematic coordinators in promoting and coordinating the overall organization and smooth running of the Conference. Project leader(s) in each branch has put much time and effort into soliciting papers from relevant researchers for presentation at the conference. The chairperson of each session has helped ensure a lively discussion during the Q & A and kept to the time allocation. On behalf of the Conferences Committee, I would like to express our sincere thanks to them for their fervent participation. We would also like to express our sincere thanks to all the reviewers.We hope to meet most if not all of you at the next ICMAA to have an exciting academic and practical discussions/exchanges. At the meantime, do keep safe until then.List of Organizing Committees are available in this Pdf.

DEVELOPMENT OF A NEW MASTER´S DEGREE PROGRAM BASED ON WORK LIFE REQUIREMENTS

Lapland University of Applied Sciences (Lapland UAS) started developing a new master´s degree program titled “Master of Renewing Industry” on topics related specially to twin transition and modern manufacturing technologies in spring 2021. Planning of the curriculum was based on the requirements of companies in the Northern Finland area as the contents and learning outcomes were derived from the demands of work life. The topics were constructed around modern manufacturing technologies used in the workplace taking digitalization, sustainability, and carbon neutrality into account. One major component in the curriculum is the master´s thesis where students select a development task from their work based on the themes of the degree program. Besides the professional element, the support of writing skills and written communication is continuous in the thesis process and plays an important role in student´s professional development as it increases their research abilities. As an example, from the study contents, a course titled “Industrial Production and Operations” is presented, which started in the beginning of the program in early 2022. Every study course has a designated teacher who plans, organizes, and is responsible for the successful delivery of the course, including the use of visiting lecturers and experts. Some of these visiting lectures are given by experts in the industry as the key point of all the courses is to strengthen work life orientation. This master's degree program supports lifelong learning of technical personnel in the workplace and enhances their potential to adopt the best principles of sustainability and twin transition.<p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0050/a.php" alt="Hit counter" /></p>

Accelerated Production of Biopharmaceuticals via Microwave-Assisted Freeze-Drying (MFD).

Recently, attention has been drawn to microwave-assisted freeze-drying (MFD), as it drastically reduces the typically long drying times of biopharmaceuticals in conventional freeze-drying (CFD). Nevertheless, previously described prototype machines lack important attributes such as in-chamber freezing and stoppering, not allowing for the performance of representative vial freeze-drying processes. In this study, we present a new technical MFD setup, designed with GMP processes in mind. It is based on a standard lyophilizer equipped with flat semiconductor microwave modules. The idea was to enable the retrofitting of standard freeze-dryers with a microwave option, which would reduce the hurdles of implementation. We aimed to collect process data with respect to the speed, settings, and controllability of the MFD processes. Moreover, we studied the performance of six monoclonal antibody (mAb) formulations in terms of quality after drying and stability after storage for 6 months. We found drying processes to be drastically shortened and well controllable and observed no signs of plasma discharge. The characterization of the lyophilizates revealed an elegant cake appearance and remarkably good stability in the mAb after MFD. Furthermore, overall storage stability was good, even when residual moisture was increased due to high concentrations of glass-forming excipients. A direct comparison of stability data following MFD and CFD demonstrated similar stability profiles. We conclude that the new machine design is highly advantageous, enabling the fast-drying of excipient-dominated, low-concentrated mAb formulations in compliance with modern manufacturing technology.

Breaking Material Symmetry to Control Mechanical Performance in 3D Printed Objects

Additive manufacturing is a modern manufacturing technology allowing the material structuring at a fine scale. This structuring affects the performance of printed parts. In this study, the quantification of the material arrangement in 3D printed ceramic on the mechanical performance is tackled. The experimental layout considers two main printing parameters, namely, part orientation and printing angle, where 12 different printing configurations are studied. These configurations differ in terms of filament arrangement in the building direction, and within the plane of construction. Material characterisation is undertaken through tensile testing, which are performed for vertical, lateral and longitudinal orientations, and combined with a printing angle of 0°, 15°, 30°, and 45°. In addition, Scanning Electron Microscopy is considered to study how the material symmetry affects the fractured patterns. This analysis is completed with optical imaging and is used to monitor the deformation sequences up to the rupture point. The experimental results show a wide variety of deformation mechanisms that are triggered by the studied printing configurations. This study concludes on the interpretation of the observed trends in terms of mechanical load transfer, which is related to the lack of material connectivity, and the relative orientation of the filaments with respect to the loading directions. This study also concludes on the possibility to tune the tensile performance of 3D printed ceramic material by adjusting both the part orientation and the printing angle.

Selective Laser Melting-Sintering Technology: From Dental Co-Cr Alloys to Dental Ceramic Materials

The general term of CAD/CAM technology (i.e., Computer-Aided Design/Compute-Aided Manufacturing) comprises several aspects, such as subtractive manufacturing processes, like milling (soft and hard milling), and additive manufacturing processes, like Selective Laser Melting (SLM), which refers to metallic materials, or Selective Laser Sintering (SLS), which refers to glasses/glass-ceramics/ceramic, or polymeric, or related composite materials produced via powder metallurgy technique. In biomaterials fabrications, the first step in SLM or SLS technology is the digital design of the prosthetic restoration, whereby the patient's individual anatomical and morphological features are precisely described. Afterwards laser-aided melting or sintering is repeated (layer-by-layer) until the complete restoration item is fabricated. A wide range of dental materials can be produced by SLM or SLS technology, e.g., metals and alloys, thermoplastic polymers, glasses/ceramics, waxes, and thermoplastic composites. Thus, it is a promising technology for producing a variety of dental restorations, such as metal-ceramic restorations, all-ceramic restorations, maxillofacial prostheses, functional skeletons, individual scaffolds for tissue engineering, etc. SLM technology is already widely applied for fabricating metal objects for dental (e.g., Co-Cr alloy) and orthopedic prostheses. As a subsequence, in the last decade, researchers' interest has been shifted to SLS of ceramic powders, such as SiO2, Al2O3, SiO2/Al2O3, and ZrO2/Y2O3. This article comprehensively reviews the SLS process and its prospects for producing glasses/glass-ceramic/ceramic materials for biomedical/dental applications. The experimental results clearly show that this very modern additive manufacturing technology does not jeopardize the properties of the ceramic biomaterials' properties.

Prediction of melt pool shape in additive manufacturing based on machine learning methods

Directed Energy Deposition (DED), one type of additive manufacturing (AM) as novel modern manufacturing technology, is widely employed to fabricate materials layer by layer through digital models. It is well known that the shape of the melt pool is influenced by the manufacturing process parameters during the manufacturing process, which can affect the performance of the part. Therefore, three machine learning models, support vector regression (SVR), extreme gradient boosting (XGBoost), and back propagation neural network (BPNN), were selected and constructed in this study to predict melt pool shape and to maximize prediction performance by integrating various algorithms and parameters. Meanwhile, the geometry of the melt pool, such as the height, width, and depth of 210 single-channel deposition layers, were measured as training and testing datasets, which can be supplied the basic data for training the prediction models and testing the prediction performance of models. In addition to the training and test datasets, a new dataset with 36 samples was created to validate the performance predictions made by the three machine learning models. The results demonstrate that, based on the test dataset, the RBF kernel-based SVR model predicts the melt pool height with an accuracy of ∼ 93 %, while the XGboost model predicts the melt pool width and depth with accuracy rates of ∼ 97 % and ∼ 96.3 %, respectively. In the new dataset, the BPNN model based on Adam's method achieves a prediction accuracy of ∼ 93.7 % for melt pool height, but the XGboost model achieves ∼ 96.6 % and ∼ 97.8 % for melt pool width and depth, correspondingly. Thus, in addition to the training and testing datasets, the enhanced machine learning models exhibited remarkable prediction accuracy, excellent generalization, and robustness in the new dataset. As a result, the application of machine learning has greatly improved the possibility of controlling the DED process more intelligently and stably.

Multi-dimensional numerical analysis of flow instabilities in 3D-shaped honeycomb absorbers

Considered a valid alternative to other receiver types, open volumetric receivers for central tower power plants have been developed and researched for multiple decades. 3D-Shaped absorber geometries, made possible by modern manufacturing technologies, particularly offer the promise of high thermal efficiencies. Yet, volumetric absorbers carry the inherent risk of flow instabilities, which can lead to a total failure of the receiver due to local overheating. Therefore, a numerical, multi-dimensional methodology for the flow stability analysis of volumetric absorbers is proposed. The first stage of the methodology applies an 1D LTNE continuum model to quantify the general pressure loss characteristic of an absorber and identifies critical pressure drop levels with potential for flow instability. These levels are tested as part of the second analysis stage using a discrete 3D CFD model against different irradiation and flow perturbations. If the absorber is able to compensate the disturbances, its flow behavior is deemed stable. Otherwise, the absorber geometry is considered susceptible to local overheating due to flow instability.The new methodology has been validated against two reference absorbers with known stable resp. unstable flow behavior. Further, the new approach was applied to analyze the flow stability of two 3D-shaped honeycomb absorber geometries. It was shown that the geometric features of 3D-shaped absorbers can directly improve the flow stability, but do not prevent instabilities automatically. Therefore, a flow stability analysis should be included in design/optimization processes of new volumetric absorbers alongside a maximization of the thermal efficiency.

Study on Chip Formation Mechanism of Single Crystal Copper Using Molecular Dynamics Simulations

Nano-cutting is an important development direction of the modern manufacturing technology. However, the research on the mechanism underlying nano-cutting lags far behind the practical application, which restricts the development of this advanced manufacturing technology. The chip formation process is the basic process of nano-cutting, and it is of key importance for the mechanism research of nano-cutting. In this paper, the nano-tensile behavior of single crystal copper was studied based on the molecular dynamics simulations. The toughness and brittleness characteristics of the copper at different temperatures were analyzed. Then, the molecular dynamics simulations of nano-cutting for single crystal copper with different toughness and brittleness were studied. The crystal structure, cutting force, stress–strain distribution and atomic motion characteristics were systematically investigated. The nano-chip formation mechanism of single crystal copper was revealed. The results show that the chip is formed through two ways, namely the shear and extrusion. The material near the free surface of the workpiece undergoes continuous shear slip and periodic long-distance slippage along the primary shear direction, forming the block chip in which the FCC and HCP structures are orderly distributed. The material near the tool-chip interface is extruded by the tool, block chip and stagnation zone to form the flowing chip with amorphous structure. As the temperature increases, the occurrence frequency of long-distance slippage in the block chip increases, while the slippage degree decreases. Besides, with the increase in temperature, the thickness of block chip formed by shear slip decreases, while the thickness of flowing chip formed by extrusion increases.

In-Situ Inspection for Robotic Polishing of Complex Optics

Abstract: With rapid development of modern optical manufacturing technologies, industrial robot polishing has a wide range of application scenarios and broad development potential in the field of optical manufacturing. The integration of in-situ inspection is a key to improving the reliability and efficiency of precision manufacturing. Deflectometry is a promising in-situ measuring method due to its large dynamic range and structural flexibility. The measurement principles, calibration methods, phase retrieval, surface reconstruction, scope extension etc are presented systematically. The key problems of height-slope ambiguity and position-angle uncertainty are analyzed in details. High-precision measurement of complex optical elements is realized, which is of great significance to the intelligent manufacturing of key optical components.

Applications of periodic structures and metamaterials for antenna design

Nowadays, wireless telecommunication is playing an increasing role in almost every field. With the spread of IoT and 5G technologies, all electronic devices will communicate over a network and millions of devices can be connected to each other over such network. Due to this, the number of base stations will also increase significantly. As a result, even more antennas will be installed both indoors and outdoors. It follows that the use of smaller and more compact antennas and simpler and more reliable parameter tuning are of great importance to service providers as well as to equipment manufacturers, both in terms of design and antenna selection. Metamaterials are sub-wavelength structures that act as a homogeneous material to create electromagnetic properties that would not be possible with conventional materials. With the help of today's modern design and manufacturing technologies, it is possible to implement and manufacture such structures. In this article, the theoretical background and application possibilities of periodic structures and materials are presented, followed by the tuning of a split ring resonator and its application on a patch antenna.

An application of statistical quality tools for process robustness and sustainability of titanium nitride coating on a machine component of a fishing net weaving machine

Improvements of machine component quality and lifetime are important issues in modern manufacturing technology for cleaner production. This work aims to investigate the effect of sputtering process factors on wear resistance and hardness of TiN-coated fishing net weaving machine component called ‘hook’ and to determine the appropriate operating conditions for achieving process robustness. The hardness and weight loss obtained after dry sliding test against nylon fiber of TiN sputtered hooks were described by the second-order models of process factors including DC current, operating pressure and Ar to N2 flow rate ratio. From the statistical results, the optimal operating condition was achieved at a DC current of 0.41 A, operating pressure of 0.58 Pa and Ar/N2 of 1.5, leading to a high hardness of 27.6121 GPa and a low weight loss of 0.77 mg. Moreover, confirmation experiments based on Monte Carlo simulation verified that the two responses obtained at the optimal operating condition were not sensitive to the variations of process factors, demonstrating the sustainability of process quality improvement.