A tool for meaning driven materials selection

There are several tools used in materials selection processes by designers. However, they are mostly engineering based tools, which are dominated by numerical (or technical) material data that is mostly of use in embodiment or detailed design phases of new product development. On the other hand, product designers consider certain aspects such as product personality, user-interaction, meanings, emotions in their material decisions. In this regard, existing tools and methods do not fully support designers in their materials selection processes. This paper describes the development of a new materials selection tool holding the idea of [meaning driven materials selection]. In addition, the paper consists of a study conducted to create data for a dummy application.     

Mahalanobis Taguchi system based criteria selection tool for agriculture crops

Agriculture crop selection cannot be formulated from one criterion but from multiple criteria. A list of criteria for crop selection was identified through literature survey and agricultural experts. The identified criteria were grouped into seven main criteria namely, soil, water, season, input, support, facilities and threats. In this paper, Mahalanobis Taguchi system based tool was developed for identification of useful set of criteria which is a subset of the original criteria, for taking decision on crop selection in a given agriculture land. The combination of Mahalanobis distance and Taguchi method is used for identification of important criteria. Matlab software was used to develop the tool. After entering the values for each main criteria in the tool, it will process the value and identify the useful sub-criteria under each main criteria for selecting the suitable crop in a given agriculture land. Instead of considering all criteria, one can use these useful set of criteria under each main criteria for taking decision on crop selection in agriculture.     

Cutting tool material selection using grey complex proportional assessment method

In today’s metalworking industry, many types of materials, ranging from high carbon steel to ceramics and diamonds, are used as cutting tools. Because of the wide range of conditions and requirements, no single cutting tool material meets all the needs of machining applications. Each tool material has its own properties and characteristics that make it best for a specific machining application. While evaluating a cutting tool material for a machining operation, the applicability is dependant on having the correct combination of its physical properties. Thus, it is extensively important to select the most appropriate cutting tool material with the desired properties for enhanced machining performance. This paper considers an exhaustive list of 19 cutting tool materials whose performance are evaluated based on ten selection criteria. The grey complex proportional assessment (COPRAS-G) method is then applied to solve this cutting tool material selection problem considering grey data in the decision matrix. Synthetic single crystal and polycrystal diamonds emerge out as the best two choices. Oil quenched tool steel (AISI O2) and powder metal tool steel (AISI A11) may also be used as the suitable cutting tool materials. Sialon and sintered reaction bonded silicon nitride are the worst chosen cutting tool materials.     

Decision support tool for used oil regeneration technologies assessment and selection

Regeneration is the most efficient way of managing used oil. It saves money by preventing costly cleanups and liabilities that are associated with mismanagement of used oil, it helps to protect the environment and it produces a technically renewable resource by enabling an indefinite recycling potential. There are a variety of processes and licensors currently offering ways to deal with used oils. Selecting a regeneration technology for used oil involves "cross-matching" key criteria. Therefore, the first prototype of spent oil regeneration (SPORE), a decision support tool, has been developed to help decision-makers to assess the available technologies and select the preferred used oil regeneration options. The analysis is based on technical, economical and environmental criteria. These criteria are ranked to determine their relative importance for a particular used oil regeneration project. The multi-criteria decision analysis (MCDA) is the core of the SPORE using the PROMETHEE II algorithm.     

Lithic tool making by Amazonian palaeoindians: a case-study on materials selection

Abstracts are not published in this journal This revised version was published online in November 2006 with corrections to the Cover Date.     

Lean tool selection in a die casting unit: a fuzzy-based decision support heuristic

Lean manufacturing philosophy asks for elimination of wastes hidden in the manufacturing system by focusing on product value stream and eliminating non-value adding activities through continuous improvement efforts. Value stream mapping methodology is subjected to principles of continuous improvement in order to improve the productivity of the process and quality of the product. It provides various tools for data collection and analysis, and identifies the wastes occurring in different stages of manufacturing process. The role of value stream mapping is very important in the identification and subsequently reduction of the wastes. To select the detailed mapping tools for the identification of waste at micro level is a complex decision making problem. In this paper, a case study related to a die casting unit has been taken. A hierarchy related to the decision problem has been developed to select the value stream mapping tools. Here, a fuzzy logic based multi-preference, multi-criteria, and multi-person decision making heuristic has been developed to solve a problem pertaining to above case study. The proposed methodology enjoys logical support from existing decision making tools and pertinently maps the inside details of the underlying problem.     

A comparison of tool management strategies and part selection rules for a flexible manufacturing system

An important element in the successful operation of flexible manufacturing systems (FMS) is the management of the tooling component. This paper reports on one aspect of tool management for FMS operations. Four tool allocation and scheduling strategies are compared in the presence of three part selection rules through a simulation study of a five-machine FMS with an automated tool handling system. The tool allocation strategies are similar to those used in industry while the part selection rules are synthesized from the literature on FMS scheduling under tooling constraints. The use of different tooling strategies produces significantly different outcomes in FMS performance.     

Differential evolution-based feature selection and parameter optimisation for extreme learning machine in tool wear estimation

Cutting tool wear degrades the product quality in manufacturing processes. Hence, real-time online estimation of tool wear is important for suggesting a tool replacement before the wear limit is reached, in order to protect the workpiece and the CNC machine from damage and breakdown. In this study, using both statistical features and wavelet features extracted from sensor signals, an adaptive evolutionary extreme learning machine (ELM) learning paradigm is developed for tool wear estimation in high-speed milling process. In the proposed method, a discrete differential evolution (DE) algorithm is used to select input features for the ELM, and a continuous DE algorithm is used for parameter optimisation of the mixed kernel function for the ELM. The experimental results indicate that the proposed adaptive evolutionary ELM-based tool wear estimation model can effectively estimate the tool wear in high-speed milling process. Empirical comparisons show that the proposed model performs better than existing approaches in estimating the tool wear.     

An enterprise modelling CASE tool and data schema requirements for the selection of software support

This paper describes a prototype CASE tool which has been implemented to support the selection of software applications. It is argued that information structures are invariably linked to the operation of the enterprise and can, therefore, be used as a powerful basis both for the suitability assessment and selection of candidate software applications. A structured methodology for the modelling of the enterprise operation and development of information requirements is first outlined to establish the application domain of the CASE tool. The functionality of the CASE tool is subsequently described, showing how aspects of the enterprise are captured in terms of organization, functionality, resource and information. It will be demonstrated how the information structures, captured by the CASE tool, are subsequently developed into a schema and used for the evaluation of a suitable software application.     

An integrated approach for machine tool selection using fuzzy analytical hierarchy process and grey relational analysis

Selecting a proper machine tool is one of the important decisions a company has to make. Companies which fail to do so face many problems which negatively affect the firm's productivity, flexibility, precision and its responsiveness capabilities. Selection of a machine tool involves a lot of criteria to be simultaneously studied and so it requires a multi-criterion decision making (MCDM) method to solve it. Also the subjectivity involved in such decisions ask for the use of theories such as fuzzy and grey which are very effective in handling subjective inputs. This paper integrates the fuzzy analytic hierarchy process (AHP) and grey relational analysis approaches for the selection of a machine tool from a given set of alternatives. Fuzzy AHP is used to calculate the priority weights of the criteria. Subsequently grey relational analysis (GRA) is employed to rank the alternatives. A well known problem existing in literature has been picked up for the numerical illustration. The results obtained in this paper are better when compared with that existing in literature.     

Optimizing production smoothing decisions via batch selection for mixed-model just-in-time manufacturing systems with arbitrary setup and processing times

This paper is concerned with the production smoothing problem that arises in the context of just-in-time manufacturing systems. The production smoothing problem can be solved by employing a two-phase solution methodology, where optimal batch sizes for the products and a sequence for these batches are specified in the first and second phases, respectively. In this paper, we focus on the problem of selecting optimal batch sizes for the products. We propose a dynamic programming (DP) algorithm for the exact solution of the problem. Our computational experiments demonstrate that the DP approach requires significant computational effort, rendering its use in a real environment impractical. We develop three meta-heuristics for the near-optimal solution of the problem, namely strategic oscillation, scatter search and path relinking. The efficiency and efficacy of the methods are tested via a computational study. The computational results show that the meta-heuristic methods considered in this paper provide near-optimal solutions for the problem within several minutes. In particular, the path relinking method can be used for the planning of mixed-model manufacturing systems in real time with its negligible computational requirement and high solution quality.     

Dynamic performance measures for tools with multi-state wear processes and their applications for tool design and selection

Traditional models to evaluate the reliability and performance of tools are binary models, working (success) or failure, to classify the state of the tool. Most machine tools degrade with time and thus a multi-state discrete classification is more realistic for the continuous degradation of the tool. We propose a non-homogeneous continuous-time Markov process model for tool degradation, because the length of time the machine tool stays in a certain state depends not only on the current state, but also on how long the tool has been in the current state. The traditional reliability and life performance measures focus on the mean time between failure or the failure rate. The performance measures must capture the total experience of the manufacturer over the target life of the tool and the impact of its degradation on the quality of the products to the downstream customers. We propose several new measures for tool performance. These measures can be used to evaluate different tool designs or we can use them to select the best tool for a certain application based on the economic/disutility functions.     

基于FAHP及FCA的机床设备优选方法研究

针对零件加工机床设备优选的问题,分析了影响机床设备选择的约束因素,建立了一种两级结构的多目标优选模型,包括加工时间T、加工质量Q、加工成本C、资源消耗R、环境影响E在内的5个优化目标;针对传统层次分析法(AHP)在构建机床设备优选判断矩阵时没有考虑评价工程师主观模糊性和指标属性的模糊性,导致机床设备优选的可信度和准确性降低的问题,提出并设计了基于三角模糊数的模糊层次分析法(FAHP)及模糊综合评价(FCA)用于机床设备优选的模型算法.在评价指标标准化阶段,采用隶属度函数进行数据标准化处理.结合某航空制造企业叶片榫头铣削用机床设备优选的案例,验证了该方法的可行性和有效性.     

Material selection for the tool holder working under hard milling conditions using different multi criteria decision making methods

Nowadays machining of materials in their hardened state, also called hard machining, is a challenge in production of tools and molds. It has some advantages such as lower process time and lower manufacturing cost when compared to conventional machining. In machining of hard workpiece materials, however, very high stresses act on the tool holder through the cutting tool. These stresses necessitate the tool holder to have some specific properties. Especially in hard milling, the tool holder should have high stiffness and should be able to dissipate the energy generated during interrupted cutting. Material cost of the tool holder is also important since lower costs provide a competitive advantage for manufacturers. The material selection for the tool holder should be conducted considering aforementioned requirements. To tackle the difficulty of the material selection with specific properties from a large number of alternatives, multi-criteria decision-making (MCDM) methods have been used. In this paper a decision model including extended PROMETHEE II (EXPROM2) (preference ranking organization method for enrichment evaluation), TOPSIS (technique for order performance by similarity to ideal solution) and VIKOR (VIšekriterijumsko KOmpromisno Rangiranje) methods were used for the selection of the best material for the tool holder used in hard milling. The criteria weighting was performed by compromised weighting method composed of AHP (analytic hierarchy process) and Entropy methods. The candidate materials were ranked by using these methods and the results obtained by each method were compared. It was confirmed that MCDM methods can be used for the solution of real time material selection problems. Tungsten carbide–cobalt and Fe–5Cr–Mo–V aircraft steel were found as the best materials for the tool holder production. The obtained results are found to be rather satisfactory and can be used in design stage of hard machining operations.     

EDM of Ti-6Al-4V: Electrode and polarity selection for minimum tool wear rate and overcut

Titanium and its alloys especially Ti-6Al-4V are being widely used numerous application areas. In addition to have iconic properties, Ti-6Al-4V is considered as challenging material in machining perspective that is why it has captured global research focus. In this research, electric discharge machining of Ti-6Al-4V has been carried out by employing four different types of electrode materials (graphite, aluminum, copper, and brass) assigned with two alternate polarities (positive and negative). Selection of the most appropriate tool material and electrode polarity is the important aspect needed to be explored for this alloy. In addition to polarity, discharge current, and pulse time ratio have been considered as process variables owing to have their direct influence in electric discharge machining. Taguchi L9 array has been employed for each of the four electrodes with positive polarity and similarly L9 with negative polarity. Thus, a total number of 72 experiments have been conducted. Tool wear rate and overcut (OC) around the machined surfaces are the response characteristics to be investigated in order to achieve minimum amounts of both of these two responses. Selection of the most suitable tool with common tool polarity has been carried out meeting the decision criteria of minimum tool wear and minimum OC.     

Optimizing Electromagnetic Flowmeter Coils

Design principles are considered for optimal coils for electromagnetic flowmeters for general industrial use.     

Optimizing systematically renewed structures

Based on earlier work, a renewal model for structural failures with subsequent systematic reconstruction is developed. It is shown that simple objective functions can be obtained by making use of some asymptotic results of renewal theory. Complete objective functions must include benefits and expected failure cost which both must be discounted appropriately. Some techniques in optimization for locally stationary and locally non-stationary failure models are developed. Those are illustrated at simple examples.