Design Of Mechanical Components Research Articles

Editorial Board Members’ Collection Series: Improving Structural Integrity of Metals: From Bulk to Surface

Metals and alloys continue to play a crucial role in the design and construction of load-bearing structures and mechanical components [...]

A Machine Learning Approach for Mechanical Component Design Based on Topology Optimization Considering the Restrictions of Additive Manufacturing

Additive manufacturing (AM) and topology optimization (TO) emerge as vital processes in modern industries, with broad adoption driven by reduced expenses and the desire for lightweight and complex designs. However, iterative topology optimization can be inefficient and time-consuming for individual products with a large set of parameters. To address this shortcoming, machine learning (ML), primarily neural networks, is considered a viable tool to enhance topology optimization and streamline AM processes. In this work, a machine learning (ML) model that generates a parameterized optimized topology is presented, capable of eliminating the conventional iterative steps of TO, which shortens the development cycle and decreases overall development costs. The ML algorithm used, a conditional generative adversarial network (cGAN) known as Pix2Pix-GAN, is adopted to train using a variety of training data pairs consisting of color-coded images and is applied to an example of cantilever optimization, significantly enhancing model accuracy and operational efficiency. The analysis of training data numbers in relation to the model’s accuracy shows that as data volume increases, the accuracy of the model improves. Various ML models are developed and validated in this study; however, some artefacts are still present in the generated designs. Structures that are free from these artefacts achieve 91% reliability successfully. On the other hand, the images generated with artefacts may still serve as suitable design templates with minimal adjustments. Furthermore, this research also assesses compliance with two manufacturing constraints: the limitations on build space and passive elements (voids). Incorporating manufacturing constraints into model design ensures that the generated designs are not only optimized for performance but also feasible for production. By adhering to these constraints, the models can deliver superior performance in future use while maintaining practicality in real-world applications.

The analysis of scaled mechanical dynamic systems

A new approach to scaled experimentation has appeared in the open literature bringing into existence a countably infinite number of similitude rules connecting multiple scaled experiments. The simplest rule (the zeroth-order rule) captures all what is possible with dimensional analysis but higher-order rules appear to necessitate investigations at multiple scales. The scaling theory finite similitude can however, be repurposed for the analysis of scaled models making it possible to relate models of two different sizes whilst automatically accounting for all scale effects present. The new approach to scaling analysis gives rise to additional systems of equations that are required to be solved and it is this aspect that is the main focus of this paper. It is shown through application of the new scaling-analysis approach to mechanical systems built from discrete elements (e.g., springs, lumped masses, dampers) how scale effects are directly represented. Scaling analysis under the finite-similitude framework is shown to be effective for connecting up scaled models but additionally dovetails with experimental approaches involving scaled experiments. Through application to mechanical systems the new formulation is shown to have practical value but also reveals how system-level scale effects can be handled efficiently. The approach provides a framework for the design and analysis of mechanical components that are required to operate over a range of sizes.

Overview of the ASME Boiler and Pressure Vessels, U stamps Certification for Pressure Vessels

The American Society of Mechanical Engineers (ASME) Code and Sections are important for the design, manufacturing, and maintenance of mechanical systems and components. This paper will present importance of ASME U stamp certification which is crucial for ensuring Boiler and Pressure Vessels quality, safety, and compliance with industry standards. ASME U stamp certification which is accepted across the more than 100 nations. This paper also covers ASME U stamp requirements, Certificate duration, Certification processes, and application of U stamp and importance of Single Certification Mark for U stamp

Autonomous Agricultural Drone with Sprinkling System

The quadcopter is one of the most widely used varieties of Unmanned Aerial Vehicles, or UAVs. This paper aims to build a drone with autonomous navigation capabilities. A sprinkling system was installed over the quadrotor to spray the desired fertilizers in the given area. The process of creating a quadrotor using a mechatronic design methodology—which considers the design of mechanical, electrical, software, and control components simultaneously—is explained in the article. The simulation findings for pitch and roll altitude control are also presented in the study. The investigation's findings include suggestions for choosing motors, propellers, batteries, and electrical speed controls wisely so that anyone with a desire might construct one by themselves.

Smart design of a high-performance seat frame for a luxury car

The design of mechanical components in recent years has been experiencing a series of innovative processes which, in addition to the classic methodologies, also support topological and structural optimization considering the product life cycle (LCA). These innovative aspects make it possible to reduce the weight of the components and the impact on the environment, both during production and during operation. The purpose of this work is to create a tool for the “intelligent design” of automotive components through optimization algorithms called “Smart Design”. The final intent is to create a multidisciplinary methodology for mechanical design that combines topological optimization techniques and product life cycle analysis. The single studies, from the creation of the CAD and the finite element analysis, up to the topological optimization and the LCA analysis, are managed by a single code generated in the Matlab® environment. The work is carried out in collaboration with Ferrari S.p.a.

Optimization of reliability and sensitivity analysis for flange structures

The study delves into the reliability-driven optimization design of the flanges, drawing from reliability design theory, advanced optimization methodologies, and comprehensive sensitivity analysis. Flanges, integral to mechanical systems, demand precise design. Reliability design theory emphasizes consistent operation under diverse conditions. A novel approach to the flange-specific reliability sensitivity analysis method is central to this research. It promises to revolutionize flange design. By understanding the first two moments of core random parameters, we expedite reliability-driven optimization designs for mechanical connections. The programs enhance our approach, streamlining the pursuit of reliability-driven designs and providing rapid, accurate sensitivity analysis data for mechanical connections. This data offers critical insights into parameter influence, enabling targeted design adjustments. In summary, this research represents a significant advancement in mechanical engineering. By integrating reliability design theory with cutting-edge methodologies, it promises to enhance the quality and reliability of mechanical connections, meeting the rigorous demands of contemporary engineering applications. This holistic approach will play a pivotal role in the future of mechanical component design.

Synergistic lubrication effects of Sn–Ag–Cu and MXene–Ti3C2 to improve tribological properties of M50 bearing steel with microporous channels

Purpose This paper aims to study the synergistic lubrication effects of Sn–Ag–Cu and MXene–Ti3C2 to improve the tribological properties of M50 bearing steel with microporous channels. Design/methodology/approach M50 matrix self-lubricating composites (MMSC) were designed and prepared by filling Sn–Ag–Cu and MXene–Ti3C2 in the microporous channels of M50 bearing steel. The tribology performance testing of as-prepared samples was executed with a multifunction tribometer. The optimum hole size and lubricant content, as well as self-lubricating mechanism of MMSC, were studied. Findings The tribological properties of MMSC are strongly dependent on the synergistic lubrication effect of MXene–Ti3C2 and Sn–Ag–Cu. When the hole size of microchannel is 1 mm and the content of MXene–Ti3C2 in mixed lubricant is 4 wt.%, MMSC shows the lowest friction coefficient and wear rate. The Sn–Ag–Cu and MXene–Ti3C2 are extruded from the microporous channels and spread to the friction interface, and a relatively complete lubricating film is formed at the friction interface. Meanwhile, the synergistic lubrication of Sn–Ag–Cu and MXene–Ti3C2 can improve the stability of the lubricating film, thus the excellent tribological property of MMSC is obtained. Originality/value The results help in deep understanding of the synergistic lubrication effects of Sn–Ag–Cu and MXene–Ti3C2 on the tribological properties of M50 bearing steel. This work also provides a useful reference for the tribological design of mechanical components by combining surface texture with solid lubrication. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-12-2023-0381/

Experimental Study on Mechanical Characteristics of Aluminium Metal Matrix Composite Reinforced with Titanium Oxide (TiO2) and Graphite (Gr) Particles Processed by Stir Casting Method

Abstract: In recent trend Aluminium based metal matrix composites are most used in mechanical and automobile component design applications because of their excellent mechanical, Tribological and its physical properties. Also, Aluminium metal matrix composites are used for a variety of applications such as military, aerospace, electrical industries, and automotive purposes. The present work concentrates on the experimental study on the effects of TiO2 and Gr on aluminium alloy. During this investigation, Titanium oxide (TiO2) and Graphite (Gr) particles was reinforced with aluminium and it was prepared using stir casting technique with various weight percentage combinations of TiO2 particles 2% by wt., 4% by wt., and 6% by wt. and 3% of Gr constant for all trials. Mechanical parameters were estimated by selecting the standard test methods.

Notch structural stress theory: Part II predicting total fatigue lives of notched structures

Notch fatigue problems are among the most common topics for the reliability analysis and structural design of mechanical engineering components. In previous work, we proposed the Notch Structural Stress (NSS) method based on fracture mechanics for reflecting the notch effect on fatigue crack propagation lives. However, like other regular fatigue assessing procedures, NSS also cannot accurately predict the fatigue life of notched structures with slower stress gradients due to the inconsistent slope of their S-N curves. This paper presents the Theory of Notch Structural Stress (TNSS) for predicting total fatigue lives of notched structures, which overcomes the life prediction limitations caused by slope differences in S-N curves and constructs a life-predicting frame suitable for different notch types even including plain and sharp notches. Compared with the often-used stress-concentration-assessing procedures, TNSS provides a more effective consolidation of the S-N data from different notch types, mostly since it takes both Initiation and Propagation state resistance into account at the same time.

An evaluation of the sources and extent of error in the position of implants placed using 3D-printed stereolithographic surgical guides.

Introduction/Background. Errors can be introduced in implant placement when using stereolithographically manufactured fully limiting surgical guides due to various factors, such as discrepancies in designing and manufacturing of surgical guides (intrinsic error), built-in variations in design of mechanical components of guide systems and implant dimensions (inherent error) or due to placement error by the operator (operator error). Aim. The aim of this study was to evaluate influence of various factors on implant position when using these guides. Materials and Methods. Prosthetically-driven implant positions were planned with pre-operative cone beam computed tomography (CBCT) data and digital scans using a computer aided designing (CAD) software. Static guided implant surgery was performed under local anaesthesia using fully limiting mucosa-supported surgical guide. Pre-operative and post-operative CBCTs were superimposed using surgical sleeve as a reference to evaluate the placement error by operator (N=12). To evaluate intrinsic error, standard tessellation language (STL) files of the virtual design of the surgical guides and the STL file obtained after scanning the 3D printed stereolithographic surgical guides were superimposed. Inherent error was calculated using a geometric model. Results. Intrinsic error was observed to be a major contributing factor in angular deviation of implant, linear deviation observed at implant shoulder as well as at implant apex. Operator error was observed to be a major contributing factor for mean vertical deviation observed at apex of implant. Conclusion. This study showed that accuracy of implant placement was largely influenced by discrepancies introduced during designing and manufacturing of stereolithographic surgical guides as compared to other sources.

An evaluation of the sources and extent of error in the position of implants placed using 3D-printed stereolithographic surgical guides.

Introduction/Background. Errors can be introduced in implant placement when using stereolithographically manufactured fully limiting surgical guides due to various factors, such as discrepancies in designing and manufacturing of surgical guides (intrinsic error), built-in variations in design of mechanical components of guide systems and implant dimensions (inherent error) or due to placement error by the operator (operator error). Aim. The aim of this study was to evaluate influence of various factors on implant position when using these guides. Materials and Methods. Prosthetically-driven implant positions were planned with pre-operative cone beam computed tomography (CBCT) data and digital scans using a computer aided designing (CAD) software. Static guided implant surgery was performed under local anaesthesia using fully limiting mucosa-supported surgical guide. Pre-operative and post-operative CBCTs were superimposed using surgical sleeve as a reference to evaluate the placement error by operator (N=12). To evaluate intrinsic error, standard tessellation language (STL) files of the virtual design of the surgical guides and the STL file obtained after scanning the 3D printed stereolithographic surgical guides were superimposed. Inherent error was calculated using a geometric model. Results. Intrinsic error was observed to be a major contributing factor in angular deviation of implant, linear deviation observed at implant shoulder as well as at implant apex. Operator error was observed to be a major contributing factor for mean vertical deviation observed at apex of implant. Conclusion. This study showed that accuracy of implant placement was largely influenced by discrepancies introduced during designing and manufacturing of stereolithographic surgical guides as compared to other sources.

Accounting for Machine Learning Prediction Errors in Design

Abstract Machine learning is gaining prominence in mechanical design, offering cost-effective surrogate models to replace computationally expensive models. Nevertheless, concerns persist regarding the accuracy of these models, especially when applied to safety-critical products. To address this challenge, this study investigates methods to account for model prediction errors by incorporating epistemic uncertainty within surrogate models while managing aleatory uncertainty in input variables. The paper clarifies key aspects of modeling coupled epistemic and aleatory uncertainty when using surrogate models derived from noise-free training data. Specifically, the study concentrates on quantifying the impacts of coupled uncertainty in mechanical design through the development of numerical methods based on the concept of the most probable point. This method is particularly relevant for mechanical component design, where failure prevention holds paramount importance, and the probability of failure is low. It is applicable to design problems characterized by probability distributions governing aleatory and epistemic uncertainties in model inputs and predictions. The proposed method is demonstrated using shaft and beam designs as two illustrative examples. The results demonstrate the method's effectiveness in quantifying and mitigating the influence of coupled uncertainty in the design process.

Analysis of the influence of fillet geometry on stress concentration in keyway by the finite element method

Machines play a pivotal role in the modern world, as they streamline various activities by virtue of their capacity to convert energy, such as transforming electrical energy into mechanical energy. In this context, the mechanical shaft assumes significant importance, as it enables the transmission of power to a wide range of applications. Being a solid component, the shaft is subjected to mechanical stresses, and the keyway groove, present in the shaft, is a feature that exacerbates stress concentrations in its geometry, which is undesirable as it increases the risk of failure due to rupture. Therefore, this research aimed to investigate the influence of the keyway groove geometry on stress concentration in shafts under bending loads, using simulations based on the finite element method. The study compares elliptical keyways with circular keyways and assesses how variations in parameters impact stress concentration, employing the Ansys tool for the analysis. The results demonstrate that slight modifications in the keyway groove geometry can significantly reduce stress concentrations, thereby contributing to the optimized design of mechanical components and providing valuable insights for engineers working in this field.

Investigation of phase transition, tribological behavior and wear mechanisms of WC-enhanced biphase eutectic high entropy alloy by fast hot pressing sintering

The wear resistance of materials is crucial in mechanical component design, but there has been limited research on the tribological behavior of WC-reinforced high entropy alloy(HEA) to date. Therefore, this study used fast hot pressing sintering to fabricate WC/AlCoCrFeNi2.1 metal matrix composites (MMCs). It was observed that: 1. The HEAs matrix had a biphase eutectic structure with FCC and BCC phases, while the interfacial layer consisted of M7C3 and M23C6 (M=Cr, W). 2. MMCs showed proportional increases in microhardness, nanoindentation and wear resistance with reinforced particle addition. 3. During frictional processes, an oxide film formed providing material protection, enhancing stability and reducing friction-induced losses. Adhesive wear, abrasive wear, and three-body abrasion were identified as primary types of observed wear.

Deformation and Shear Stress Analysis of Torsional Rod Used in Automobile in Various Materials Using Solidworks and Ansys: A Review

Abstract: Deformation and shear stress analysis are pivotal considerations in the design and performance evaluation of mechanical components, particularly in the automobile industry. The behaviour of torsional rods, critical load-bearing elements in many automotive systems, necessitates meticulous assessment to ensure optimal functionality and longevity. This review delves into the comprehensive study of deformation and shear stress analysis of torsional rods employed in automobiles. The investigation harnesses the capabilities of two industry-standard simulation tools, SolidWorks and ANSYS, to explore a spectrum of materials commonly used in automotive engineering. The juxtaposition of SolidWorks and ANSYS serves as a comprehensive approach to examining the behaviour of torsional rods across various materials, offering valuable insights for both researchers and practitioners. Through illustrative case studies and comparative analyses, this review sheds light on the unique attributes of different materials and simulation platforms, contributing to the advancement of automotive component design and analysis methodologies. Based on previous research the objective of this review paper is “Deformation and Static Stress Analysis of Torsional Rod Using Various Materials” Based on previous research the objective of this review paper is “Deformation and Shear Stress Analysis of Torsional Rod Using Various Materials”

Noncontact and rapid measurement of elastic properties of metals with a dual-mode bulk-wave EMAT

ABSTRACT The elastic mechanical behaviour of materials is very important to the design and service safety evaluation of mechanical load components. In order to accurately measure the elastic mechanical behaviour of metal structures with a non-destructive testing way, a dual-mode bulk-wave electromagnetic acoustic transducer (EMAT) is developed for both ultrasonic longitudinal wave and shear wave measurement. The elastic modulus and Poisson’s ratio of metals are directly obtained with the velocities of the longitudinal and shear waves. In order to improve the efficiency of electromagnetic acoustic transducer, the electromagnetic acoustic parameters were characterised and optimised by numerical simulation. The results show that the developed dual-mode EMAT can successfully excite and receive longitudinal and shear waves, and the elastic modulus and Poisson’s ratio of different materials (copper, aluminium and steel) obtained through experiments using the developed dual-mode EMAT are very close to the reference values.

Uncertainty for fatigue life of low carbon alloy steel based on improved bootstrap method

AbstractFatigue testing is a crucial technique for assessing the fatigue performance of materials and components. However, the testing process often involves numerous uncertainties, such as variations in part roughness and size, and the collected data typically constitutes small samples. This paper proposes an improved bootstrap method with cycle of uncertainty to address the limitations of traditional bootstrap methods in estimating uncertainties in small sample sizes. The proposed method was applied to estimate the effect of surface roughness and common normal size on fatigue tests, resulting in improved estimation accuracy. The research contributes to the development of improved methods for fatigue life estimation, which has practical implications for mechanical component design and optimization.

Development of an optimally designed real-time automatic citrus fruit grading–sorting​ machine leveraging computer vision-based adaptive deep learning model

Conventional automation approaches for postharvest operations are plagued by time and data inefficiency seldom leading to suboptimal solutions. Automatic machines often require highly skilled software professionals for calibration and reconfiguration thus making the technology prone to high costs. Contemporary sensors and smart devices capable of handling deep learning image analytics have been employed in the present study for the development of an automatic machine that performs postharvest operations, like—washing, vision-based sorting and weight-based grading of citrus fruits with much reduced human effort while achieving excellent performance for the designated tasks. Accuracy of performance was ensured by the optimal design of mechanical components carried out by kinematic synthesis and dimensional analysis. The machine was equipped with an effective custom lightweight CNN model “SortNet” that was designed and tuned to carry out vision-based classification of citrus fruits into “accept” and “reject” based on surface characteristics. SortNet was less complex and took less computational time while exhibiting comparable accuracy with respect to existing state-of-the-art pre-trained deep learning models. An embedded system operated by a single-board computer was used in the weight grading section for segregating fruits based on three weight categories. Evaluation, realization and transferability of the above said strategy was demonstrated by the real hardware with physical actuators working in real-time to serve as proof-of-concept for a sustainable solution to postharvest automation of citrus fruits.

Physical background of significant increase in mechanical properties and fatigue strength of groove-rolled Cu-Ni-Si alloy with discontinuous precipitates

Prolonged aging of Cu-Ni-Si alloy induced the formation of discontinuous precipitates (DPs) of fiber-shaped Ni2Si intermetallic compounds, rendering good conductivity and relatively inferior strength. To improve the strength of Cu-Ni-Si alloy with DPs, groove-rolling technique was utilized to plastically deform the aged Cu-6Ni-1.5Si specimen. As the rate of area reduction increased from 20% to 50% and 80%, the diameter and interspacing of Ni2Si fibers decreased. Both tensile strength and fatigue limit stress, which are essential for the design of electrical and electronic mechanical components, of Cu-6Ni-1.5Si specimen increased significantly with groove-rolling. It was suggested that an extra strengthening was achieved by Ni2Si fiber reinforcement in addition to work hardening. The increase in yield strength by fiber reinforcement was estimated by Orowan strengthening principle, assuming ideal fiber arrangement. The estimated yield strength based on Orowan mechanism was found to be much greater than the experimentally measured value. This was because actual morphology and arrangement of fibers were largely different from the ideal state, necessitating an error factor, k, which varied with the true strain, εt, due to groove-rolling. The k showed initial steep increase and subsequent mild increase to εt, followed by decreasing trend after εt ≒ 0.7. The physical background of enhanced fatigue strength of rolled specimen was discussed based on the detailed microstructural observation on crack initiation and propagation behavior.