An integrated system to design machine layouts for modular special purpose machines

This thesis introduces the development of an integrated system for the design of layouts for special purpose machines (SPMs). SPMs are capable of performing several machining operations (such as drilling, milling, and tapping) at the same time. They consist of elements that can be arranged in different layouts. Whilst this is a unique feature that makes SPMs modular, a high level of knowledge and experience is required to rearrange the SPM elements in different configurations, and also to select appropriate SPM elements when product demand changes and new layouts are required. In this research, an integrated system for SPM layout design was developed by considering the following components: an expert system tool, an assembly modelling approach for SPM layouts, an artificial intelligence tool, and a CAD design environment. SolidWorks was used as the 3D CAD environment. VisiRule was used as the expert system tool to make decisions about the selection of SPM elements. An assembly modelling approach was developed with an SPM database using a linked list structure and assembly relationships graph. A case-based reasoning (CBR) approach was developed and applied to automate the selection of SPM layouts. These components were integrated using application programing interface (API) features and Visual Basic programming language. The outcome of the application of the novel approach that was developed in this thesis is reducing the steps for the assembly process of the SPM elements and reducing the time for designing SPM layouts. As a result, only one step is required to assemble any two SPM elements and the time for the selection process of SPM layouts is reduced by approximately 75% compared to the traditional processes. The integrated system developed in this thesis will help engineers in design and manufacturing fields to design SPM layouts in a more time-effective manner

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Internet-enabled fixture design system using case-based reasoning technology

Master'sMASTER OF ENGINEERIN

Technical feasibility analysis of utilizing special purpose machine tools

Special purpose machine tools (SPMs) are primarily used for performing drilling-related operations and are widely used in mass production including automotive component manufacturing. Utilization of SPM is considerably widespread; however, this technology is relatively new and expensive. The important problems facing manufacturing industries wishing to utilize this technology is feasibility analysis to decide whether a SPM can be utilised for production of the given part and if it is feasible which SPM components would be appropriate. Since the cost of utilizing SPM is high, feasibility analysis must be performed before any investment on detailed design. This paper proposes a technical feasibility analysis method which assists in deciding whether SPM is applicable for machining a given part to achieve the highest productivity. The method is based on the framework which consists of relations between the desired part properties to the characteristics of the SPM components. These relations are captured as rules and constraints in an intelligent system which is implemented in Visual Basic. Applying the proposed method to a number of industrial parts shows that it is a very useful tool in deciding when SPMs should be utilized

A Hybrid Intelligent System for Stamping Process Planning in Progressive Die Design

This paper presents an intelligent, hybrid system for stamping process planning in progressive die design. The system combines the flexibility of blackboard architecture with case-based reasoning. The hybrid system has the advantage that it can use past knowledge and experience for case-based reasoning when it exists, and other reasoning approaches when it doesn’t exist. A prototype system has been implemented in CLIPS and interfaced with Solid Edge CAD system. An example is included to demonstrate the approach.Singapore-MIT Alliance (SMA

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

Development of a decision support system for decision-based part/fixture assignment and fixture flow control.

Doctoral Degree. University of KwaZulu-Natal, Durban.An intense competition in a dynamic situation has increased the requirements that must be considered in the current manufacturing systems. Among those factors, fixtures are one of the major problematic components. The cost of fixture design and manufacture contributes to 10-20% of production costs. Manufacturing firms usually use traditional methods for part/fixture assignment works. These methods are highly resource consuming and cumbersome to enumerate the available fixtures and stabilise the number of fixtures required in a system. The aim of this study was to research and develop a Decision Support System (DSS), which was useful to perform a decision-based part/fixture assignment and fixture flow control during planned production periods. The DSS was designed to assist its users to reuse/adapt the retrieved fixtures or manufacture new fixtures depending upon the state of the retrieved fixtures and the similarities between the current and retrieved cases. This DSS combined Case-Based Reasoning (CBR), fuzzy set theory, the Analytic Hierarchy Process (AHP) and Discrete-Event Simulation (DES) techniques. The Artificial Intelligence (AI) component of the DSS immensely used a fuzzy CBR system combined with the fuzzy AHP and guiding rules from general domain knowledge. The fuzzy CBR was used to represent the uncertain and imprecise values of case attributes. The fuzzy AHP was applied to elicit domain knowledge from experts to prioritise case attributes. New part orders and training samples were represented as new and prior cases respectively using an Object-Oriented (OO) method for case retrieval and decision proposal. Popular fuzzy ranking and similarity measuring approaches were utilised in the case retrieval process. A DES model was implemented to analyse the performances of the proposed solutions by the fuzzy CBR subsystem. Three scenarios were generated by this subsystem as solution alternatives that were the proposed numbers of fixtures. The performances of these scenarios were evaluated using the DES model and the best alternative was identified. The novelty of this study employed the combination of fuzzy CBR and DES methods since such kinds of combinations have not been addressed yet. A numerical example was illustrated to present the soundness of the proposed methodological approach.Please refer to the PDF for author's keywords

Development of a decision support system for decision-based part/fixture assignment and fixture flow control = Ukusungulwa kohlelo lokuxhaswa kwezinqumo mayelana nokwabiwa kwezingxenye ezakhiwayo kanye nokuhanjiswa kwazo.

Doctoral Degree. University of KwaZulu-Natal, Durban.ABSTRACT: An intense competition in a dynamic situation has increased the requirements that must be considered in the current manufacturing systems. Among those factors, fixtures are one of the major problematic components. The cost of fixture design and manufacture contributes to 10-20% of production costs. Manufacturing firms usually use traditional methods for part/fixture assignment works. These methods are highly resource consuming and cumbersome to enumerate the available fixtures and stabilise the number of fixtures required in a system. The aim of this study was to research and develop a Decision Support System (DSS), which was useful to perform a decision-based part/fixture assignment and fixture flow control during planned production periods. The DSS was designed to assist its users to reuse/adapt the retrieved fixtures or manufacture new fixtures depending upon the state of the retrieved fixtures and the similarities between the current and retrieved cases. This DSS combined Case-Based Reasoning (CBR), fuzzy set theory, the Analytic Hierarchy Process (AHP) and Discrete-Event Simulation (DES) techniques. The Artificial Intelligence (AI) component of the DSS immensely used a fuzzy CBR system combined with the fuzzy AHP and guiding rules from general domain knowledge. The fuzzy CBR was used to represent the uncertain and imprecise values of case attributes. The fuzzy AHP was applied to elicit domain knowledge from experts to prioritise case attributes. New part orders and training samples were represented as new and prior cases respectively using an Object-Oriented (OO) method for case retrieval and decision proposal. Popular fuzzy ranking and similarity measuring approaches were utilised in the case retrieval process. A DES model was implemented to analyse the performances of the proposed solutions by the fuzzy CBR subsystem. Three scenarios were generated by this subsystem as solution alternatives that were the proposed numbers of fixtures. The performances of these scenarios were evaluated using the DES model and the best alternative was identified. The novelty of this study employed the combination of fuzzy CBR and DES methods since such kinds of combinations have not been addressed yet. A numerical example was illustrated to present the soundness of the proposed methodological approach. Keywords: Decision support systems, case-based reasoning, analytic hierarchy process, fuzzy set theory, object-oriented methods, discrete-event simulation, fixtures. IQOQA LOCWANINGO : Ukuncintisana okunezinhlelo eziguquguqukayo kulesi sikhathi samanje sekwenze ukuthi kube nezidingo ezintsha ezinhlelweni zokukhiqiza. Phakathi kwakho konke lokhu izingxenye (fixtures) zingezinye zezinto ezidala izinkinga. Intengo yokwakha uhlaka lwengxenye kanye nokuyikhiqiza kubiza amaphesenti ayi-10 kuya kwangama-20 entengo yokukhiqiza. Amafemu akhiqizayo avamise ukusebenzisa izindlela ezindala zomsebenzi wokwaba izingxenye. Lezi zindlela zidla kakhulu izinsizangqangi futhi kuthatha isikhathi eside ukubala izingxenye ezikhona nokuqinisekisa ukuthi kunesibalo esanele kulokho okumele kube yikho ohlelweni lokusebenza. Inhloso yalolu cwaningo bekungukucwaninga nokusungula i-Decision Support System (DSS) ebe lusizo ekwenzeni umsebenzi wokuthatha izinqumo ngokwabiwa kwezingxenye kanye nokuhanjiswa kwazo ngezikhathi ezimiselwe ukukhiqiza. I-DSS yakhelwa ukusiza labo abayisebenzisayo ukuze basebenzise noma bazisebenzise lapho zingakaze zisetshenziswe khona lezo zingxenye ezibuyisiwe, noma kwakhiwe ezintsha kuya ngokuthi zibuyiswe zinjani lezi ezibuyisiwe nokuthi ziyafana yini nalezo ezintsha. I-DSS isebenzise amasu ahlanganise i-Case-Based Reasoning (CBR), injulalwazi echazwa ngokuthi i-fuzzy, ne-Analytic Hierarchy Process (AHP) ne-Discrete-Event Simulation (DES). I-Artificial Intelligence (AI) eyingxenye ye-DSS isebenzise kakhulu uhlelo lwe-fuzzy CBR luhlangene ne-fuzzy AHP kulandelwa imithetho yolwazi olumayelana nohlobo lomsebenzi. I-CBR isetshenziswe ukumelela lezo zimo zamanani ezingaqondakali nezingaphelele kulezo zingxenye. I-AHP e-fuzzy yasetshenziswa ukuze kutholakale ulwazi kochwepheshe olubeka phambili lezo zingxenye. Ama-oda ezingxenye ezintsha kanye namasampuli asetshenziselwa ukuqeqesha avezwe njengamasha kanye nabekade evele ekhona ngokulandelana kusetshenziswa indlela eyaziwa ngokuthi yi-Object-Oriented (OO) method lapho kubuyiswa izinto noma kunezinqumo eziphakanyiswayo. Izindlela ezijwayelekile zokulandelanisa nokufanisa zisetshenziswe ohlelweni lokubuyisa izinto. Kusetshenziswe isu eliyi-DES ukuhlaziya ukusebenza kwezisombululo eziphakanyiswe yindlela ye-CBR e-fuzzy. Le ndlela iphinde yaveza izimo ezintathu eziphakanyiswe ukuba zibe yisisombululo esibalweni sezingxenye ezihlongozwayo. Ukusebenza kwalezi zimo kuhlungwe ngokusebenzisa indlela ye-DES kwase kuvela inqubo engcono. Ukungajwayeleki kwalolu cwaningo kusebenzise ingxube yezindlela ze-fuzzy CBR ne-DES ngoba lolu hlobo lwengxube belungakaze lusetshenziswe. Kusetshenziswe isibonelo sezibalo ekwethuleni ukusebenza kwale nqubo yokusebenza ehlongozwayo

An intelligent knowledge based cost modelling system for innovative product development

This research work aims to develop an intelligent knowledge-based system for product cost modelling and design for automation at an early design stage of the product development cycle, that would enable designers/manufacturing planners to make more accurate estimates of the product cost. Consequently, a quicker response to customers’ expectations. The main objectives of the research are to: (1) develop a prototype system that assists an inexperienced designer to estimate the manufacturing cost of the product, (2) advise designers on how to eliminate design and manufacturing related conflicts that may arise during the product development process, (3) recommend the most economic assembly technique for the product in order to consider this technique during the design process and provide design improvement suggestions to simplify the assembly operations (i.e. to provide an opportunity for designers to design for assembly (DFA)), (4) apply a fuzzy logic approach to certain cases, and (5) evaluate the developed prototype system through five case studies. The developed system for cost modelling comprises of a CAD solid modelling system, a material selection module, knowledge-based system (KBS), process optimisation module, design for assembly module, cost estimation technique module, and a user interface. In addition, the system encompasses two types of databases, permanent (static) and temporary (dynamic). These databases are categorised into five separate groups of database, Feature database, Material database, Machinability database, Machine database, and Mould database. The system development process has passed through four major steps: firstly, constructing the knowledge-based and process optimisation system, secondly developing a design for assembly module. Thirdly, integrating the KBS with both material selection database and a CAD system. Finally, developing and implementing a ii fuzzy logic approach to generate reliable estimation of cost and to handle the uncertainty in cost estimation model that cannot be addressed by traditional analytical methods. The developed system has, besides estimating the total cost of a product, the capability to: (1) select a material as well as the machining processes, their sequence and machining parameters based on a set of design and production parameters that the user provides to the system, and (2) recommend the most economic assembly technique for a product and provide design improvement suggestion, in the early stages of the design process, based on a design feasibility technique. It provides recommendations when a design cannot be manufactured with the available manufacturing resources and capabilities. In addition, a feature-by-feature cost estimation report was generated using the system to highlight the features of high manufacturing cost. The system can be applied without the need for detailed design information, so that it can be implemented at an early design stage and consequently cost redesign, and longer lead-time can be avoided. One of the tangible advantages of this system is that it warns users of features that are costly and difficult to manufacture. In addition, the system is developed in such a way that, users can modify the product design at any stage of the design processes. This research dealt with cost modelling of both machined components and injection moulded components. The developed cost effective design environment was evaluated on real products, including a scientific calculator, a telephone handset, and two machined components. Conclusions drawn from the system indicated that the developed prototype system could help companies reducing product cost and lead time by estimating the total product cost throughout the entire product development cycle including assembly cost. Case studies demonstrated that designing a product using the developed system is more cost effective than using traditional systems. The cost estimated for a number of products used in the case studies was almost 10 to 15% less than cost estimated by the traditional system since the latter does not take into consideration process optimisation, design alternatives, nor design for assembly issue