Materials Selection of Thermoplastic Matrices of Natural Fibre Composites for Cyclist Helmet Using an Integration of DMAIC Approach in Six Sigma Method Together with Grey Relational Analysis Approach

Natural fibre reinforced polymer composite(NFRPC)materials are gaining popularity in the modern world due to their eco-friendliness,lightweight nature,life-cycle superiority,biodegradability,low cost,and noble mechanical properties.Due to the wide variety of materials available that have comparable attributes and satisfy the requirements of the product design specification,material selection has become a crucial component of design for engineers.This paper discusses the study’s findings in choosing the suitable thermoplastic matrices of Natural Fibre Composites for Cyclist Helmet utilising the DMAIC,and GRA approaches.The results are based on integrating two decision methods implemented utilising two distinct decision-making approaches:qualitative and quantitative.This study suggested thermoplastic polyethylene as a particularly ideal matrix in composite cyclist helmets during the selection process for the best thermoplastic matrices material using the 6σtechnique,with the decision based on the highest performance,the lightest weight,and the most environmentally friendly criteria.The DMAIC and GRA approach significantly influenced the material selection process by offering different tools for each phase.In the future study,selection technique may have been more exhaustive if more information from other factors had been added.

Materials Selection Method Combined with Different MADM Methods

Materials selection is a multiple attribute decision making(MADM)problem.A lot of MADM methods are applicable to materials selection,and it may produce considerable differences between the results of materials selection.But it is unknown which MADM method is better.So it is desirable to decide reasonable final result of materials selection in consideration of the individual results from different MADM methods.In this paper,materials selection method combined with different MADM methods is proposed.The method is based on final ranks of alternative materials,where the final ranks are determined from the ranks of the alternative materials using different MADM methods.This method is applied to select optimal magnesium alloy material for automobile wheels.This method may be widely used to select optimal material in engineering practice.

Piezoelectric materials selection for sensor applications using finite element and multiple attribute decision-making approaches

This paper examines the selection and performance evaluation of a variety of piezoelectric materials for cantilever-based sensor applications.The finite element analysis method is implemented to evaluate the relative importance of materials properties such as Young's Modulus(E),piezoelectric stress constants(e_(31)),dielectric constant(ε)and Poisson's ratio(ν)for cantilever-based sensor applications.An analytic hierarchy process(AHP)is used to assign weights to the properties that are studied for the sensor structure under study.A technique for order preference by similarity to ideal solution(TOPSIS)is used to rank the performance of the piezoelectric materials in the context of sensor voltage outputs.The ranking achieved by the TOPSIS analysis is in good agreement with the results obtained from finite element method simulation.The numerical simulations show that K_(0.5)Na_(0.5)NbO_(3)-LiSbO_(3)(KNN-LS)materials family is important for sensor application.Young's modulus(E)is most influencing material's property followed by piezoelectric constant(e_(31)),dielectric constant(ε)and Poisson's ratio(ν)for cantilever-based piezoelectric sensor applications.

Decision Support Strategy in Selecting Natural Fiber Materials for Automotive Side-Door Impact Beam Composites

The enforcement on sustainable design and environmental-friendly products has attracted the interest of researchers and engineers in the context of replacing metals and synthetic fibers with natural based fibers,especially in the automotive industry.However,studies on sustainable natural fiber material selection in the automotive industry are limited.Evaluation for the side-door impact beam was conducted by gathering product design specification from literature which amounted to seven criteria and it was forwarded to ten decision makers with automotive engineering and product design background for evaluation.The weightage required for decision-making was obtained using the Analytic Hierarchy Process(AHP)method based on six criteria.Following this,the best natural fiber materials to be used as reinforcement in polymer composites were selected using the VIseKriterijumska Optimizacija I Kompromisno Resenje(VIKOR)method.The results using both the AHP and VIKOR method showed that kenaf was the best natural fiber for the side-door impact beam composites.The result showed the lowest VIKOR value,QA1=0.0000,which was determined to be within the acceptable advantage and acceptable stability conditions.It can be concluded that the application of integrated AHP-VIKOR method resulted in a systematic and justified solution towards the decision-making process.

ARTIFICIAL NEURAL NETWORKS BASED GEARS MATERIAL SELECTION HYBRID INTELLIGENT SYSTEM

An artificial neural networks(ANNs) based gear material selection hybrid intelligent system is established by analyzing the individual advantages and weakness of expert system (ES) and ANNs and the applications in material select of them. The system mainly consists of tow parts: ES and ANNs. By being trained with much data samples, the back propagation (BP) ANN gets the knowledge of gear materials selection, and is able to inference according to user input. The system realizes the complementing of ANNs and ES. Using this system, engineers without materials selection experience can conveniently deal with gear materials selection.

Decision making tools for optimal material selection:A review

The present work reviews different decision making tools(material comparing and choosing tools)used for selecting the best material considering different parameters.In this review work,the authors have tried to address the following important enquiries:1)the engineering applications addressed by the different material choosing and ranking methods;2)the predominantly used decision making tools addressing the optimal material selection for the engineering applications;3)merits and demerits of decision making tools used;4)the dominantly used criteria or objectives considered while selecting a suitable alternative material;5)overview of DEA on material selection field.The authors have surveyed literatures from different regions of the globe and considered literatures since 1988.The present review not only stresses the importance of material selection in the early design stage of the product development but also aids the design and material engineers to apply different decision making tools systematically.

Sine Trigonometry Operational Laws for Complex Neutrosophic Sets and Their Aggregation Operators in Material Selection

In this paper,sine trigonometry operational laws(ST-OLs)have been extended to neutrosophic sets(NSs)and the operations and functionality of these laws are studied.Then,extending these ST-OLs to complex neutrosophic sets(CNSs)forms the core of thiswork.Some of themathematical properties are proved based on ST-OLs.Fundamental operations and the distance measures between complex neutrosophic numbers(CNNs)based on the ST-OLs are discussed with numerical illustrations.Further the arithmetic and geometric aggregation operators are established and their properties are verified with numerical data.The general properties of the developed sine trigonometry weighted averaging/geometric aggregation operators for CNNs(ST-WAAO-CNN&ST-WGAO-CNN)are proved.A decision making technique based on these operators has been developed with the help of unsupervised criteria weighting approach called Entropy-ST-OLs-CNDM(complex neutrosophic decision making)method.A case study for material selection has been chosen to demonstrate the ST-OLs of CNDM method.To check the validity of the proposed method,entropy based complex neutrosophic CODAS approach with ST-OLs has been executed numerically and a comparative analysis with the discussion of their outcomes has been conducted.The proposed approach proves to be salient and effective for decision making with complex information.

Concurrent Material Selection of Natural Fibre Filament for Fused Deposition Modeling Using Integration of Analytic Hierarchy Process/Analytic Network Process

The employment of natural fibres in fused deposition modeling has raised much attention from researchers in finding a suitable formulation for the natural fibre composite filaments.Moreover,selection of suitable natural fibres for fused deposition modeling should be performed before the development of the composites.It could not be performed without identifying selection criteria that comprehend both materials and fused deposition modeling process requirements.Therefore,in this study,integration of the Analytic Hierarchy Process(AHP)/Analytic Network Process(ANP)has been introduced in selecting the natural fibres based in different clusters of selection concurrently.The selection process has been performed based on the interdependency among the selection criteria.Pairwise comparison matrices are constructed based on AHP’s hierarchical model and super matrices are constructed based on the ANP’s network model.As a result,flax fibre has ranked at the top of the selection by scored 19.5%from the overall evaluation.Flax fibre has excellent material properties and been found in various natural fibre composite applications.Further investigation is needed to study the compatibility of this fibre to be reinforced with a thermoplastic polymer matrix to develop a resultant natural fibre composite filament for fused deposition modeling.

Material Selection of a Natural Fibre Reinforced Polymer Composites using an Analytical Approach

Material selection has become a critical part of design for engineers,due to availability of diverse choice of materials that have similar properties and meet the product design specification.Implementation of statistical analysis alone makes it difficult to identify the ideal composition of the final composite.An integrated approach between statistical model and micromechanical model is desired.In this paper,resultant natural fibre and polymer matrix from previous study is used to estimate the mechanical properties such as density,Young’s modulus and tensile strength.Four levels of fibre loading are used to compare the optimum natural fibre reinforced polymer composite(NFRPC).The result from this analytical approach revealed that kenaf/polystyrene(PS)with 40%fibre loading is the ideal composite in automotive component application.It was found that the ideal composite score is 1.156 g/cm^(3),24.2 GPa and 413.4 MPa for density,Young’s modulus and tensile strength,respectively.A suggestion to increase the properties on Young’s modulus are also presented.This work proves that the statistical model is well incorporated with the analytical approach to choose the correct composite to use in automotive application.

Selected Natural Materials in History

1 Technology of natural materials Early man used conveniently shaped stones as tools. ＂Workshop＂ areas have been found with large numbers of stones, some showing signs of being worked. However, organic materials like wood will decay under normal wet conditions in the presence of oxygen, so we won＇t find the same sort of evidence for wooden tools. It is safe to assume that early man used sticks as probes and clubs, and maybe even for making some sort of nestlike protection against the elements and predators, since we see chimpanzees and other animals doing this sort of thing. So wood, and ahnost certainly other plant materials such as fibrous leaves, and bone and other materials gleaned from dead animals, would be used from the earliest times. We need to know this in order to establish the idea that Man can be expected to have a long history of the use and manipulation of natural materials. This needs skills in choosing materials for certain uses on the basis of their mechanical properties, whether those properties are to do with the ease of shaping the material or the effectiveness of that material in use. Occasionally the material was chosen simply because it was readily available. If we find that a particular material was always used for a certain job, it＇s reasonable to deduce that Man was exerting materials selection criteria through experience.

Materialized Views Selection Problem in Decision Supporting Systems: Issues and Challenges

The data warehouse is the most widely used database structure in many decision support systems around the world. This is the reason why a lot of research has been conducted in the literature over the last two decades on their design, refreshment and optimization. The manipulation of hypercubes (cubes) of data is a frequently used operation in the design of multidimensional data warehouses, due to their better adaptation to OLAP (On-Line Analytical Processing). However, the updating of these hypercubes is a very complicated process due mainly to the mass and complexity of the data presented. The purpose of this paper is to present the state of the art of works based on multidimensional modeling using the hypercube as a unit of presentation of data stores. It starts with the base of this process which is the choice of the views (cubes) forming our data warehouse base. The objective of this work is to describe the state of the art of research works dealing with the selection of materialized views in decision support systems.

EFFECT OF STRUCTURE OF FUNCTIONAL POLYMER ACTIVE MATERIALS ON PROPERTIES OF GADOLINIUM ION SELECTIVE ELECTRODE

In this paper,the functional polymeric active materials were prepared by the grafting copolymerization and their structure and properties were studied.The results show that the structure and properties of these ac- tive materials have the relative large effects on the properties of gadolinium ion selective electrodes.

The role of Cr content on the corrosion resistance of carbon steel and low-Cr steels in the CO_(2)-saturated brine

The selection of appropriate materials for the transportation pipelines is of vital importance to ensure the safety operation in Carbon Capture,Utilisation and Storage(CCUS).To clarify the effects of Cr content in steel on the resistance against general and localised corrosion,electrochemistry methods combined with pH measurements and various surface analysis techniques were implemented on X65,1Cr,3Cr and 5Cr steel samples in a CO_(2)-saturated solution at 60°C and pH 6.6 during 192 h of immersion.Additionally,thermodynamic and kinetic analyses of the formation of the corrosion products on carbon steel and low-Cr steels were performed.The results show that the general corrosion resistance increased with rising Cr content without the presence of significant corrosion products formation.However,with the formation and development of the corrosion products,the general corrosion resistance reduced with the increase in Cr content.The formation of the compact crystalline FeCO3 on X65 and 1Cr steel surfaces offered superior general corrosion protection,while cannot provide enough localised corrosion protection.By contrast,the double-structural corrosion product layers on 3Cr and 5Cr steels notably suppressed the localised corrosion,but providing poor protection against general corrosion over long immersion periods.This study reveals the contributions of Cr content on general and localised corrosion resistance at various periods,providing references for material selection and evaluation in CO_(2) environments relevant for CCUS.

Determination of the best materials for development and designing product using a multi-criteria decision-making

Purpose–Material selection,driven by wide and often conflicting objectives,is an important,sometimes difficult problem in material engineering.In this context,multi-criteria decision-making(MCDM)methodologies are effective.An approach of MCDM is needed to cater to criteria of material assortment simultaneously.More firms are now concerned about increasing their productivity using mathematical tools.To occupy a gap in the previous literature this research recommends an integrated MCDM and mathematical Bi-objective model for the selection of material.In addition,by using the Technique for Order Preference by Similarity to Ideal Solution(TOPSIS),the inherent ambiguities of decision-makers in paired evaluations are considered in this research.It goes on to construct a mathematical bi-objective model for determining the best item to purchase.Design/methodology/approach–The entropy perspective is implemented in this paper to evaluate the weight parameters,while the TOPSIS technique is used to determine the best and worst intermediate pipe materials for automotive exhaust system.The intermediate pipes are used to join the components of the exhaust systems.The materials usually used to manufacture intermediate pipe are SUS 436LM,SUS 430,SUS 304,SUS 436L,SUH 409 L,SUS 441 L and SUS 439L.These seven materials are evaluated based on tensile strength(TS),hardness(H),elongation(E),yield strength(YS)and cost(C).A hybrid methodology combining entropy-based criteria weighting,with the TOPSIS for alternative ranking,is pursued to identify the optimal design material for an engineered application in this paper.This study aims to help while filling the information gap in selecting the most suitable material for use in the exhaust intermediate pipes.After that,the authors searched for and considered eight materials and evaluated them on the following five criteria:(1)TS,(2)YS,(3)H,(4)E and(5)C.The first two criteria have been chosen because they can have a lot of influence on the behavior of the exhaust intermediate pipes,on their performance and on the cost.In this structure,the weights of the criteria are calculated objectively through the entropy method in order to have an unbiased assessment.This essentially measures the quantity of information each criterion contribution,indicating the relative importance of these criteria better.Subsequently,the materials were ranked using the TOPSIS method in terms of their relative performance by measuring each material from an ideal solution to determine the best alternative.The results show that SUS 309,SUS 432L and SUS 436 LM are the first three materials that the exhaust intermediate pipe optimal design should consider.Findings–The material matrix of the decision presented in Table 3 was normalized through Equation 5,as shown in Table 5,and the matrix was multiplied with weighting criteriaß_j.The obtained weighted normalized matrix V_ij is presented in Table 6.However,the ideal,worst and best value was ascertained by employing Equation 7.This study is based on the selection of material for the development of intermediate pipe using MCDM,and it involves four basic stages,i.e.method of translation criteria,screening process,method of ranking and search for methods.The selection was done through the TOPSIS method,and the criteria weight was obtained by the entropy method.The result showed that the top three materials are SUS 309,SUS 432L and SUS 436 LM,respectively.For the future work,it is suggested to select more alternatives and criteria.The comparison can also be done by using different MCDM techniques like and Choice Expressing Reality(ELECTRE),Decision-Making Trial and Evaluation Laboratory(DEMATEL)and Preference Ranking Organization Method for Enrichment Evaluation(PROMETHEE).Originality/value–The results provide important conclusions for material selection in this targeted application,verifying the employment of mutual entropy-TOPSIS methodology for a series of difficult engineering decisions in material engineering concepts that combine superior capacity with better performance as well as cost-efficiency in various engineering design.

Recent advances on mixed matrix membranes for CO_2 separation

Recent advances on mixed matrix membrane for CO2 separation are reviewed in this paper. To improve CO2 separation performance of polymer membranes, mixed matrix membranes (MMMs) are developed. The concept of MMM is illustrated distinctly. Suitable polymer and inorganic or organic fillers for MMMs are summarized. Possible interface morphologies between polymer and filler, and the effect of interface morphologies on gas transport properties of MMMs are summarized. The methods to improve compatibility between polymer and filler are introduced. There are eight methods including silane coupling, Grignard treatment, incorporation of additive, grafting, in situ polymerization, polydopamine coating, particle fusion approach and polymer functionalization. To achieve higher productivity for industrial application, mixed matrix composite membranes are developed. The recent development on hollow fiber and flat mixed matrix composite membrane is reviewed in detail. Last, the future trend of MMM is forecasted.

Tool Materials Rapid Selection Based on Initial Wear

Based on a large amount of literature about tool wear research,873 tool wear curves are taken as samples,and statistical analysis is carried out to select the most suitable tool from all the tool materials suggested by the tool manufacturers. Statistical relationships between the initial wear and uniform wear periods are obtained. The results show that there is qualitative relationship between wear rate during initial wear period （WRIWP） and wear rate in uniform wear period （WRUWP） to certain extent. On this basis,a tool material rapid selection method based on the initial wear is put forward,and suitable tool materials for machining titanium alloy are selected. The experimental results indicate that this method is effective and useful. The new tool materials rapid selection can be used to select suitable cutting tool materials quickly before carrying out systematic machinability tests with the most suitable tool materials. The technology can be applied to doing the initial selection of cutting tool materials in either the machinability research or the workshop production.

Selective ensemble modeling based on nonlinear frequency spectral feature extraction for predicting load parameter in ball mills

Strong mechanical vibration and acoustical signals of grinding process contain useful information related to load parameters in ball mills. It is a challenge to extract latent features and construct soft sensor model with high dimensional frequency spectra of these signals. This paper aims to develop a selective ensemble modeling approach based on nonlinear latent frequency spectral feature extraction for accurate measurement of material to ball volume ratio. Latent features are first extracted from different vibrations and acoustic spectral segments by kernel partial least squares. Algorithms of bootstrap and least squares support vector machines are employed to produce candidate sub-models using these latent features as inputs. Ensemble sub-models are selected based on genetic algorithm optimization toolbox. Partial least squares regression is used to combine these sub-models to eliminate collinearity among their prediction outputs. Results indicate that the proposed modeling approach has better prediction performance than previous ones.

Possible Ways to Make Interpretation Teaching More Effective

Interpretation course aims to help students understand interpreting theories and master interpreting skills.Limited class hours make it difficult to achieve the expected goals.This paper therefore focuses on interpretation teaching and discusses the possible ways to make teaching more effective in the limited class hours.

Optimum alternate material selection methodology for an aircraft skin

Weight penalty has been a challenge for design engineers of aerospace vehicles.Today’s high-efficiency combat aircraft undergoes intense stress and strain during flying missions,which require stronger and stiffer materials to retain structural integrity.Though metallic materials have been successfully used for the construction of aircraft structures and components,metals still have a low strength-to-weight ratio.This paper aims to develop an alternate optimised material selection methodology to replace the metallic skin of a medium-sized military aircraft.The search for the optimum material will result in reduced aircraft weight which will be benefitted by extra payload on the aircraft.The selection methodology is comprised of finding design pressure limits on the aircraft skin,and comparison of properties(strength,elastic modulus,shear modulus,etc.)and performance(safety factor,deflection,and stress)of the existing metallic skin with alternate optimised material.The comparison was made under aerodynamic pressure,bending force,and twisting moment.Carbon Fibre Reinforced Polymer/Epoxy(CFRP)Uni-Directional(UD)prepreg(elastic modolus is 209 GPa)was selected as an alternate optimum material to replace the aluminium alloy skin of the aircraft studied.The selected alternate optimum material resulted in the reduction of aircraft skin weight by 30%.

Digital twin-driven green material optimal selection and evolution in product iterative design

In recent years,green concepts have been integrated into the product iterative design in the manufacturing field to address global competition and sustainability issues.However,previous efforts for green material optimal selection disregarded the interaction and fusion among physical entities,virtual models,and users,resulting in distortions and inaccuracies among user,physical entity,and virtual model such as inconsistency among the expected value,predicted simulation value,and actual performance value of evaluation indices.Therefore,this study proposes a digital twin-driven green material optimal selection and evolution method for product iterative design.Firstly,a novel framework is proposed.Subsequently,an analysis is carried out from six perspectives:the digital twin model construction for green material optimal selection,evolution mechanism of the digital twin model,multi-objective prediction and optimization,algorithm design,decision-making,and product function verification.Finally,taking the material selection of a shared bicycle frame as an example,the proposed method was verified by the prediction and iterative optimization of the carbon emission index.