Material Selection and Design for Assembly Applied in the Development of an Energy Generating Device

Structural performance of prefabricated glass fibre concrete floor panel versus compressed fibre cement floor panel for an optimised volumetric module- A case study

Foremost step in the development of any electromechanical product is its design, and conceptual design is the most ambiguous and creative phase of design. There exist only a few computational tools that aid designers at conceptual design stage, and mostly designers rely on personal experience or experience of co-workers to generate quality designs. The proposed framework aims at generating robust computerized conceptual designs by incorporating Modularity, Design for Assembly (DFA) and Design for Variety (DFV) principles at the conceptual stage. Conceptual design alternatives obtained from the proposed framework are ranked based on minimum assembly time, and are composed of modules in a way that future changes in customer needs are satisfied only by replacing certain modules. The framework involves searching a design repository of components by using functional-basis and pre-defined graph grammar rules, to generate all possible conceptual design alternatives. These design alternatives are ranked and filtered using a DFA index, and top two alternatives are selected. Selected designs are modularized and filtered using a DFV index to obtain the best design alternative. This paper provides a detailed discussion of the proposed framework, and its working is illustrated through the design of a mounting system for holding a Variable Message Sign (VMS).

An intelligent method for selecting optimal materials and its application

The aim of this paper is to propose the methods to select reasonable normalization method in TOPSIS and decide best optimal material combined with individual results from TOPSIS with some popular normalization methods. In this paper, to evaluate performance of normalization method, entropy-based and variation coefficient-based performance scores are introduced. To decide final result of materials selection combined with individual results from TOPSIS with different normalization methods, final rank index of alternative material is proposed. To verify the effectiveness of the proposed methods, TOPSIS with some popular normalization methods is applied to select optimal tribological coating material. As a result, it is desirable to select the normalization method with highest entropy-based and variation coefficient-based performance scores. In order to select best optimal material using TOPSIS with some popular normalization methods, the method to decide final result of materials selection is proposed by using final indices of alternative materials. The proposed methods may be widely used to solve the materials selection problems in engineering practice.

Ceramic matrix composites have been recommended for space applications. Accordingly, in this paper, a material selection method for the forebody of a space transportation system is demonstrated. The methodology is based on mass-model coupled aerothermodynamic design of a highly-integrated forebody-inlet system that uses the multidisciplinary optimization capability of the TIPSO (Two-steps Improved PSO) algorithm. The design optimization and hence material parameters are evolved using the newly developed SHWAMIDOF-FI tool. This paper focuses on validating the selection of carbon composite material by optimizing the configuration parameters for integrating a cone-derived forebody into planar wedge surfaces and an inlet-isolator assembly, so as to form a mixed internal-external compression system. Surface temperature, thermal conductivity, tensile strength and emissivity are used as primary parameters for selection of a forebody material. The optimization results validate that a carbon fibre reinforced carbon and silicon carbide (C/C-SiC) dual matrix composite is best suited for the application

Compliance with the rules and regulations of competition “Student Formula Car Racing” that conducted annually by the ‘Society of Automotive Engineers’ (SAE) India, the car frame must be designed and built with supreme priority. The major task posed is to design and fabricate a light weighed vehicle chassis frame without compensating the safety. This paper boards various methods of material selection, technical design optimization and Finite Element Analysis using ANSYS. The basic design is based on the anthropological study data of the specified human (95th percentile male) al-lowing fast ‘way-in’ and ‘way-out’ access from the car. According to the rules book specification on material selection, AISI 4130 chromoly steel was the first time identified for the frame design. Resulting in the final design of the vehicle frame, various analyses were done using ANSYS and the successive results are plotted and discussed. The entire design optimization and simulation analysis are based on the 2019 Formula SAE rules book. Keywords: finite element analysis, AISI 4130 chromoly steel, frame construction, Society of Automotive Engineers.

A novel method for materials selection in mechanical design: Combination of non-linear normalization and a modified digital logic method

This article deals with initial material selection and design method of long fibre-reinforced thermoplastic (LFT) based on impact resistance requirements. In this study, effects of material properties and impact condition, including total fibre thickness, fibre aspect ratio and impactor diameter, on the minimum penetration energy of LFT plates are analysed based on experiments and validated numerical models. Mathematical expression is established between them. Applicability of the original model for LFT plate with different fibre aspect ratios subjected to different impactors is discussed. The minimum penetration energy is used as the bridge between energy absorption characteristics and material parameters, impact condition. Finally, an updated theoretical model for energy absorption analysis is proposed. We conclude that the updated semi-empirical model is a valuable tool for a first stage material selection and design.

: RFI attenuating materials and construction methods for portable and permanent shelters are discussed and found to be inadequate for high levels of attenuation, even though satisfactory performance may be obtained for lower levels of attenuation. Measurement techniques and test fixtures are developed to permit rapid evaluation of shielding effectiveness of about 100 samples of various materials, including base metals, composites, screens and foils. Test results are presented that permit selection of suitable materials for each level of attenuation desired. Representative samples of commercial enclosures are tested for seam integrity, access opening and door leakage, and overall construction methods. Weaknesses of all the standard assembly methods are discussed in detail. Conceptual designs are developed that apply the materials and construction methods defined to each level of attenuation and type of enclosure. Detailed drawings and specifications are presented for each configuration. A comparison is made between each class of enclosure for weight, volume, erection time and cost for each level of attenuation. Discussions are presented of trade-offs and simplications for each attenuation level and type of shelter.

Definition of a multimaterials design method

Integrated shape and material selection for single and multi-performance criteria

The aim of this paper is to develop a new method for the material selection problem, which is important for the product design. Material selection problem is actually a multi-attribute decision making (MADM) problem which contains many influence factors. The new material selection method is an extension of projection method with evaluation attribution values expressed with triangular fuzzy numbers. Coefficient of variation method is used to determine weights of evaluation attribute. A grinding wheel abrasive material selection problem is used to illustrate the effectiveness and practicability of the proposed method.

An evolutionary approach in the combinatorial selection and optimization of catalytic materials

The optimization of the assembly phase, in complex products, is a challenging phase and it need to be handled in the early phase of product development (i.e., conceptual design). Several methods have been developed to assess the assemblability of product at the conceptual design phase, however, the most critical aspect concerns the possibility to derive design guidelines starting from the results of assemblability analysis. In this context, the present work aims at defining a methodology able to retrieve design for assembly and installation guidelines starting from the analysis of a given product architecture at the conceptual design phase (loop-back of the design for assembly method). The developed method makes use of matrices and vectors to provide a list of design actions that affect the product assemblability including a ranking of their impacts on the final design. The methodology was used to retrieve and select design guidelines in the context of aircraft manufacturing. The case study (cabin equipping of commercial aircraft) provides interesting results in the identification and implementation of design guidelines to improve the aircraft architecture at the conceptual level.

Corrosion is one of the most challenging problems that affects the safety and durability of onshore pipelines. Corrosion-resistant steels play a pivotal role in ensuring long lasting function, cost reduction in upkeep, and prevention of major breakdowns. The traditional materials selection methods often depend on hands-on testing, expert experience and the use of a trial-and-error process. The rapid advancement of machine learning and artificial intelligence (AI) driven techniques is revolutionizing materials discovery, property prediction, and material design by minimizing human intervention and accelerating scientific progress. In the advent of the digital revolution, Artificial Intelligence (AI) has emerged as a pivotal tool in various domains, including materials design and discovery. This paper aims to develop and facilitate how AI is used to interpret Ashby’s chart and merge databases of materials together to select materials for corrosion resistant steels in onshore oil and gas pipelines. This paper proposes an AI Assisted material selection framework specifically designed to identify optimal corrosion resistant steels for onshore pipelines oil and gas pipelines by improving forecast accuracy, reducing uncertainty, enabling informed material selection, and strengthening longterm pipeline integrity in corrosive operating environments. By leveraging machine learning (ML) techniques such as Gradient Boosting Machines (GBM), Principal Component Analysis (PCA) for dimensionality reduction, and Neural Networks. The framework intelligently analyzes vast datasets encompassing operational parameters, environmental conditions, and historical corrosion rates.