A.M.M. Sharif Ullah

Applying linguistic criteria in FMS selection: fuzzy‐set‐AHP approach

This paper presents a computational framework that combines both fuzzy sets and analytical hierarchy process (AHP) for selecting the best‐ranked flexible manufacturing system from a number of feasible alternatives. Fuzzy sets are employed to recognize the selection criteria as linguistic variables rather than numerical ones, which, in turn, makes the framework quite user‐friendly. AHP is used to determine the due weight of the selection criteria, in accordance with their relative importance. In total, 14 criteria are considered, grouping them into flexibility, cost, productivity, and risk. The criteria under the first three groups are independent (i.e. their own fuzzy sets evaluate them) and the criteria under risk are indirectly evaluated by using the fuzzy sets of the criteria under flexibility. The proposed framework is implemented by developing an expert system called FmsExpert, using Borland C++. The performance of this system is also demonstrated by using an example.

A human-assisted knowledge extraction method for machining operations

Abstract This paper deals with a human-assisted knowledge extraction method to extract “if…then…” rules from a small set of machining data. The presented method utilizes both probabilistic reasoning and fuzzy logical reasoning to benefit from the machining data and from the judgment and preference of a machinist. Using the extracted rules, one can determine the values of operational parameters (feed, cutting velocity, etc.) to ensure the desired machining performance (keep surface roughness within the stipulated range (e.g., moderate)). Applying the presented method in a real-life machining knowledge extraction situation and comparing it with the inductive learning based knowledge extraction method (i.e., ID3), the usefulness of the method is demonstrated. As the concept of manufacturing automation is shifting toward “how to support humans by computers”, the presented method provides some valuable hints to the developers of futuristic computer integrated manufacturing systems.

Manufacturing process performance prediction by integrating crisp and granular information

This study deals with the integration of crisp and granular information for predicting the performance of a manufacturing process. Supporting and computing a set of two If-Then rules is considered the central idea for this integration. In these rules, the antecedent part deals with the recommended ranges of the control variables of the process, while the consequent part deals with the acceptable ranges of the performance measures of the process. The rules specify that if the control variables are kept within their recommended ranges, then it is likely or unlikely to get the performance measures within their acceptable ranges. The rules are supported by using the following conditional probabilities: the probability of getting the performance measures acceptable given that the control variables are within their recommended ranges (which should be likely), and the probability of getting performance measures acceptable given that the control variables are not within their recommended ranges (which should be unlikely). The remarkable thing is that both acceptable ranges and recommended ranges are subjectively defined concepts. So are likelihood perceptions such as “likely” and “unlikely.” Therefore, all of them can be defined by using some kind of fuzzy-granular information. The usefulness of this new approach is demonstrated by solving a machining decision-making problem (select cutting conditions and inserts satisfying subjectively defined surface finish requirement in terms of roughness and fractal dimension of machined surface). Further study should be directed toward understanding these rules in the context of predictive process planning.

Sustainability analysis of rapid prototyping: material/resource and process perspectives

Sustainability of rapid prototyping (RP) depends on both model-building materials (wooden-materials, photo-resins, etc.) and model-building processes (additive processes – SLA, SLS, etc.; and subtractive processes – e.g., wood-sawing). In this study, a sustainability index is developed for RP processes, and this index incorporates such sustainability factors as volumetric quantity of model-building material, CO2 footprint and resource depletion of primary production of model-building material, energy consumption and CO2 emission of the model-building process. In addition, physical models have been created from the same 3D CAD data by using both SLA-based RP technology (additive process) and wooden-material-based RP technology (subtractive process). The subtractive process uses a specially designed CNC machine tool that removes the wooden-material using a circular-saw controlled by a 3D CAD model. The model-building process has been repeated for different scales of the same 3D CAD model. Using the experimental results, the sustainability index of the two RP technologies has been compared. The results help determine the critical size of a physical model of a given 3D CAD model and RP technology ensuring sustainability. In addition, the results show new avenues for improving the respective RP technologies in terms of sustainable manufacturing requirements.

Fuzzy Monte Carlo Simulation using point-cloud-based probability-possibility transformation

Fuzzy Monte Carlo Simulation (FMCS) uses both the probability density function (pdf) and possibility distributions (e.g., fuzzy numbers) to model the uncertainty/imprecision associated with the input parameters and, then, to simulate the uncertainty/imprecision associated with the output parameters. A probability–possibility transformation is needed to transfer the information of a fuzzy number into its equivalent pdf, while performing the simulation. This study deals with an approach of FMCS that uses a point-cloud-based probability–possibility transformation. Let x(t), t = 0,1,…, n, be a set of points that represents some random states of an uncertain/imprecise quantity. The collection of points (x(t), x(t+i)), t = 0,…, n–i, i∈ {1,2,…} is called point-cloud, providing a visual/computational representation of variability, modality, and ranges associated with the quantity. This study identifies the pdf and possibility distribution (fuzzy number) underlying a given point-cloud. Using these distributions, the relationships between the triangular fuzzy number and unimodal pdf (normal/uniform distributions) are identified. Two numerical examples are described elucidating the effectiveness of the proposed transformation. The first example deals with the issue of monitoring a FMCS process. The other example deals with the issue of making a decision by using FMCS.

Logical interaction between domain knowledge and human cognition in design

The formal interaction between domain knowledge and human cognition in design is described by using some logical operations (induction, deduction, extension). When the extracted domain knowledge is not computable using deduction, a designer can critically think to modify the knowledge until a deductive agreement is discovered. In such a critical thinking process, the designer by nature uses commonsense (i.e., general knowledge) and his or her familiarity to the problem. A logical operation called extension is developed to formalise the above mentioned critical thinking process. Real-life examples are also shown to provide more insight into the presented logical operations.

Supplier evaluation with GD-based multi criteria decision making

Inherent complexity and uncertainty in a business environment necessitates the consideration of conflicting multi criteria in the supplier evaluation process. In multi criteria supplier evaluation process, there are many decision situations in which the information cannot be assessed precisely in a quantitative form but may be in a qualitative one. This research aims to develop a computing tool which can evaluate the supplier by taking the opinion of expert as a linguistic value in a fuzzy form and incorporating the uncertainty measure. The use of linguistic labels makes expert judgement more reliable and informative for decision making. To address the uncertainty during decision making, Alpha-cut which is a fuzzy algebra, shall be used to get a crisp range from a fuzzy range. To enlighten the effects of uncertainty, objective of this research is to perform a sensitivity analysis using the range of truth-value using alpha-cut and see how the decision is affected.

On the Effective Teaching of CAD/CAM at the Undergraduate Level

AbstractDue to the growing need for the graduates having solid foundation in computer aided manufacturing, undergraduate programs for manufacturing/mechanical/industrial engineering across the globe offers introductory courses in CAD/CAM. In this paper, a method is presented for integrating various resources available in an engineering college and in the Internet for teaching the fundamentals of CAD/CAM (parametric curve based geometric modeling, machining parameter selection, and cutter location determination). The presented method helps students attain the educational objectives set by the ABET. It is also argued that the presented method is more effective than a teaching method wherein a commercial CAD/CAM package is used.

DNA based computing for understanding complex shapes.

This study deals with a computing method called DNA based computing (DBC) that takes inspiration from the Central Dogma of Molecular Biology. The proposed DBC uses a set of user-defined rules to create a DNA-like sequence from a given piece of problem-relevant information (e.g., image data) in a dry-media (i.e., in an ordinary computer). It then uses another set of user-defined rules to create an mRNA-like sequence from the DNA. Finally, it uses the genetic code to translate the mRNA (or directly the DNA) to a protein-like sequence (a sequence of amino acids). The informational characteristics of the protein (entropy, absence, presence, abundance of some selected amino acids, and relationships among their likelihoods) can be used to solve problems (e.g., to understand complex shapes from their image data). Two case studies ((1) fractal geometry generated shape of a fern-leaf and (2) machining experiment generated shape of the worn-zones of a cutting tool) are presented elucidating the shape understanding ability of the proposed DBC in the presence of a great deal of variability in the image data of the respective shapes. The implication of the proposed DBC from the context of Internet-aided manufacturing system is also described. Further study can be carried out in solving other complex computational problems by using the proposed DBC and its derivatives.

On Some Eco-Indicators of Cutting Tools

This study deals with some eco-indicators of cutting tools. Eco-indicators of cutting tools are classified into three categories, namely, material-, process-, and geometry-related eco-indicators. Material-related eco-indicators consist of density, price, embodied energy, CO2 footprint, NOX, SOX, water usage, material processing energy, and recycle fraction of tool materials. Process-related eco-indicators consist of material removal rate, cutting velocity, feed rate, spindle speed, and surface coating. Geometry-related eco-indictors consist of special geometric features of cutting tool that make the tool’s performance robust in terms of process-related eco-indicators. The general definitions and representations of these indicators are described. Giving examples of cutting tools made of tungsten carbide and HSS, it is shown that further research is needed to develop an ideal cutting tool that is equally preferable in terms of material-, process-, and geometry-related eco-indicators.© 2011 ASME