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In this paper, fatigue life of coil springs was\ud investigated as failure due to high cyclic loading during\ud their service life is more common. For this purpose,\ud two exclusive rear coil compression spring geometries\ud were investigated using material type - siliconchromium\ud steel SAE-AISI 9254 for this application. For\ud these design models, the effect of changing component\ud geometry using varying pitch arrangements of coils were\ud examined to understand their structural durability and\ud ability to withstand cyclic loading conditions. A finite\ud element model based on industry standard designs\ud was created to simulate real-life spring performance.\ud In this study, the varied pitch designs were subjected\ud to lower stresses in static analysis and have longer life\ud estimates in the fatigue analysis. This confirms that\ud a varied pitch can improve the performance of a coil\ud spring. The result shows that design-1 incorporated a\ud pitch design that reduced pitch at the centre coils of the\ud spring performed better.

Working together harmoniously in an engineering team is crucial to achieve success and is essential to the team members’ wellbeing. This study examines what types of personalities hinder the harmony across engineering teams and the outcome of this study is crucial to recognise how to maintain harmony across engineering teams with diverse personalities. Consequently, the data is collected from experienced engineering team leaders as well as an experienced psychologist. This study shows that in the engineering industry personalities are looked at from a different perspective and their characteristics are perceived from a business point of-view as opposed to the literature in which judgments are made upon the personality description.

Currently, extensive signal analysis is performed in order to evaluate structural health of turbomachinery blades. This approach is affected by constraints of time and the availability of qualified personnel. Thus, new approaches to blade dynamics identification that provide faster and more accurate results are sought after. Generally, modal analysis is employed in acquiring dynamic properties of a vibrating turbomachinery blade and is widely adopted in condition monitoring of blades. The analysis provides useful information on the different modes of vibration and natural frequencies by exploring different shapes that can be taken up during vibration since all mode shapes have their corresponding natural frequencies. Experimental modal testing and finite element analysis are the traditional methods used to evaluate mode shapes with limited application to real live scenario to facilitate a robust condition monitoring scheme. For a real time mode shape evaluation, rapid evaluation and low computational cost is required and traditional techniques are unsuitable. In this study, artificial neural network is developed to evaluate the mode shape of a lab scale rotating blade assembly by using result from finite element modal analysis as training data. The network performance evaluation shows that artificial neural network (ANN) is capable of mapping the correlation between natural frequencies and mode shapes. This is achieved without the need of extensive signal analysis. The approach offers advantage from the perspective that the network is able to classify mode shapes and can be employed in real time including simplicity in implementation and accuracy of the prediction. The work paves the way for further development of robust condition monitoring system that incorporates real time mode shape evaluation.

In the last few years, additive manufacturing (AM) technologies has enabled engineers to design and develop several products with complex geometries, which could not be easily built by conventional manufacturing methods. Furthermore, AM application has been extended from solely making prototypes to develop end-use components in different industry sectors such as civic, aerospace, medical and etc. One of the earliest additive manufacturing methods known as metal printing allows us to produce more performative components in industries, however due to some limitations, this method claims different strategies of design for additive manufacturing (DFAM). In this research, a construction component, namely multi branch node, which is used in space frame (SF), is chosen as a platform in order to implement the design strategy on its surface using a rationalization algorithm. At first, a multi branch node is simply sketched using the dimensional information of SF's converging bars into a specific node. Then, a topology optimization analysis is performed by reducing the aforementioned volume from the specific section of the node to achieve the minimum strain energy. Finally, a rationalization algorithm namely, mesh-matching technique is implemented on a topologically-optimized node in order to make it more compatible with the metal printing and additionally provide safety and structural advantages compared to the original node.

This paper proposes a design method for devices composed of long fiber-reinforced composites (FRC) and additive manufactured (AM) parts. Both FRC and AM processes have similar application characteristics: suitable for small production volumes, additive in nature, and capable of being highly automated. On the other hand, the classes have distinct characteristics. FRCcomponents tend to be large and of simple shapes, while AM components tend to be small with highly complex geometry. Their combination has the potential for significant synergies, while mitigating their individual limitations. A decision guide is proposed, in the form of a series of questions, to guide the designer to determine if their application is a good candidate for FRC+AM. The decision guide is reformulated into a proposed design process that guides the designer to advantageously benefit from AM and FRC characteristics. The tools are illustrated with an example of a composite pressure vessel with integrated pressure reducer.

Voxel-based multimaterial jetting additive manufacturing allows fabrication of digital materials (DMs) at the meso-scale (∼1 mm) by controlling the deposition patterns of soft elastomeric and rigid glassy polymers at the voxel-scale (∼90 μm). The digital materials can then be used to create heterogeneous functionally graded material (FGM) structures at the macro-scale (∼10 mm) programmed to behave in a predefined manner. This offers huge potential for design and fabrication of novel and complex bespoke mechanical structures.\ud \ud This paper presents a complete design and manufacturing workflow that simultaneously integrates material design, structural design, and product fabrication of FGM structures based on digital materials. This is enabled by a regression analysis of the experimental data on mechanical performance of the DMs i.e., Young’s modulus, tensile strength and elongation at break. This allows us to express the material behavior simply as a function of the microstructural descriptors (in this case, just volume fraction) without having to understand the underlying microstructural mechanics while simultaneously connecting it to the process parameters.\ud \ud Our proposed design and manufacturing approach is then demonstrated and validated in two series of design exercises to devise complex FGM structures. First, we design, computationally predict and experimentally validate the behavior of prescribed designs of FGM tensile structures with different material gradients. Second, we present a design automation approach for optimal FGM structures. The comparison between the simulations and the experiments with the FGM structures shows that the presented design and fabrication workflow based on our modeling approach for DMs at meso-scale can be effectively used to design and predict the performance of FGMs at macro-scale.