187,280 research outputs found
PIM : planning in manufacturing using skeletal plans and features
In order to create a production plan from product model data, a human expert thinks in a special terminology with respect to the given work piece and its production plan: He recognizes certain features and associates fragments of a production plan. By combining these skeletal plans he generates the complete production plan. We present a set of representation formalisms suitable for the modelling of this approach. When an expert\u27s knowledge has been represented using these formalisms, the generation of a production plan can be achieved by a sequence of abstraction, selection and refinement. This is demonstrated in the CAPP-system PIM, which is currently developed as a prototype. The close modelling of the knowledge of the concrete expert (or the accumulated know-how of a concrete factory) facilitate the development of planning systems which are especially tailored to the concrete manufacturing environment and optimally use the expert\u27s knowledge and should also lead to improved acceptance of the system
Automatic feature extraction from conventional CAD model to support feature-based design approach for the sheet metal stamping industries
Despite the continuing improvement in computer aided design (CAD) systems and improvements in computer aided manufacturing (CAM), the process planning activity has still not been completely integrated into the CAD/CAM cycle. Particularly in sheet metal stamping industries human interpretation of CAD data is required to extract the geometry and technological information of a component. As a result most CAD systems are used as advanced drafting and drawing management tools by designers. Thus the responsibility for interpreting the design data required for extracting the manufacturing part features still resides with the process planner. Which has increase possibilities of entering errors with design data. A need, therefore, exists to develop expert system for automatic features extraction from a CAD database. An application software was developed for automatic feature extraction from conventional CAD model database to impliment feature-based design approach for the sheet metal stamping industries
A knowledge based decision support system for tool changeover in CNCs
This paper describes an application of an adaptive planning system for automatic tool changers in flexible manufacturing systems. The conventional models of predictive control usually cannot adapt to a real time dynamic environment. The proposed adaptive control model is capable of self adjusting to changing environments. The algorithm is based on a decision logic, which is constructed by breaking up knowledge and converting them into mathematical form in order to cover all possible conditions that can exist during the implementation phase. Expert thoughts and knowledge from decision logic are stored in the decision tree, which consists of circular nodes, arcs and decision nodes. The suggested system is capable of accepting further rules, new nodes and branches to the tree when additional attributes are needed. This whole knowledge is encoded in the form of production rules and each rule represents a small chunk of knowledge relating to the given domain of tool replacement. A number of related rules collectively respond to highly useful conclusions.The system uses VP Expert development shell, contains an inference engine and, a user interface. The originality of the proposed strategy lies in that a knowledge-based expert system is developed to identify and analyze the current conditions and then readjust the output that reflects the real-time environment. Compared with the various classical models, the approach can synthesize and analyze as many variables as possible to adequately and reliably identify the real-time conditions. Simulation results demonstrate the effectiveness and practicality of this tool-change planning and control strategy
Proof obligations as a support tool for efficient process management in the field of production planning and scheduling
Production planning and scheduling is one of the most important business processes that significantly influence the performance of manufacturing companies. There are many information systems supporting production planning and scheduling and some of them are based on very sophisticated planning algorithms. Despite this fact, many companies still face serious problems even while using professional software tools for production planning and scheduling. Obviously, a lot of other changes in form of process innovations are required. This paper deals with the problem of process management in the field of production planning and scheduling. Our study explains reasons for low performance of advanced technologies and provides solution in form of system model of key factors affecting the efficiency of planning software. Research part is based on the study conducted within Czech manufacturing companies in form of questionnaire-based investigation combined with interviews. Proposed solution is extended to the abstract mathematical model based on proof obligations which prove or disprove the correctness of intended algorithms. Our study provides basic example of such an abstract model and describes its functionality and influence to proper production planning and scheduling. It will be processed to the form of complex expert system based on Event B method in the future
An integrated computer-aided modular fixture design system for machining semi-circular parts
Productivity is one of the most important factors in manufacturing processes because of the high level of market competition. In this regard, modular fixtures (MFs) play an important role in practically improving productivity in flexible manufacturing systems (FMSs) due to this technology using highly productive computer numerical control (CNC) machines. MFs consist of devices called jigs and fixtures for accurately holding the workpiece during different machining operations. The design process is complex, and traditional methods of MF design were not sufficiently productive.
Computer-aided design (CAD) software has rapidly improved as a result of the development of computer technology, and has provided huge opportunities for modular fixture designers to use its 3D modelling capabilities to develop more automated systems. Computer-aided fixture design (CAFD) systems have become automated by the use of artificial intelligence (AI) technology. This study will investigate the further improvement of automated CAFD systems by using AI tools. In this research, an integrated CAFD is developed by considering four main requirements:
· a 3D model of the workpiece,
· an expert system,
· assembly automation of MFs,
· an efficient feature library.
The 3D model is an important factor that can provide the appropriate specification of the workpiece; SolidWorks is used the CAD environment for undertaking the 3D modelling in this study. The expert system is applied as a tool to make right decisions about the CAFD planning process, including locating and clamping methods and their related element selection. This helps achieve a feasible fixture design layout. SolidWorks API and Visual Basic programming language are employed for the automating and simulation of the assembly process of MFs. A feature library of modular fixture elements is constructed as a means to simplify the fixture design process
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
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
Numerical modelling and simulation in sheet metal forming
The application of numerical modelling and simulation in manufacturing technologies is looking back over about a 20–30 years history. In recent years, the role of modelling and simulation in engineering and in manufacturing industry has been continuously increasing. It is well known that during manufacturing processes simultaneous the effect of many different parameters can be observed. This is the reason why in former years, detailed analysis of manufacturing processes could have been done only by time-consuming and expensive trial-and-error methods. Due to the recent developments in the methods of modelling and simulation, as well as in computational facilities, modelling and simulation has become an everyday tool in engineering practice. Besides the aforementioned facts, the emerging role of modelling and simulation can also be explained by the growing globalisation and competition of the world market requiring shorter lead times and more cost effective solutions. In spite the enormous development of hardware and software facilities, the exclusive use of numerical modelling still seems to be very time- and cost consuming, and there is still often a high scepticism about the results among industrialists. Therefore, the purpose of this paper is to overview the present situation of numerical modelling and simulation in sheet metal forming, mainly from the viewpoint of scientific research and industrial applications
Multi crteria decision making and its applications : a literature review
This paper presents current techniques used in Multi Criteria Decision Making (MCDM) and their applications. Two basic approaches for MCDM, namely Artificial Intelligence MCDM (AIMCDM) and Classical MCDM (CMCDM) are discussed and investigated. Recent articles from international journals related to MCDM are collected and analyzed to find which approach is more common than the other in MCDM. Also, which area these techniques are applied to. Those articles are appearing in journals for the year 2008 only. This paper provides evidence that currently, both AIMCDM and CMCDM are equally common in MCDM
Recent Achievements in Numerical Simulation in Sheet Metal Forming Processes
Purpose of this paper: During the recent 10-15 years, Computer Aided Process Planning and Die Design evolved as one of the most
important engineering tools in sheet metal forming, particularly in the automotive industry. This emerging role is strongly emphasized by
the rapid development of Finite Element Modelling, as well. The purpose of this paper is to give a general overview about the recent
achievements in this very important field of sheet metal forming and to introduce some special results in this development activity.
Design/methodology/approach: Concerning the CAE activities in sheet metal forming, there are two main approaches: one of them may
be regarded as knowledge based process planning, whilst the other as simulation based process planning. The author attempts to integrate
these two separate developments in knowledge and simulation based approach by linking commercial CAD and FEM systems.
Findings: Applying the above approach a more powerful and efficient process planning and die design solution can be achieved radically
reducing the time and cost of product development cycle and improving product quality.
Research limitations: Due to the different modelling approaches in CAD and FEM systems, the biggest challenge is to enhance the
robustness of data exchange capabilities between various systems to provide an even more streamlined information flow.
Practical implications: The proposed integrated solutions have great practical importance to improve the global competitiveness of sheet
metal forming in the very important segment of industry.
Originality/value: The concept described in this paper may have specific value both for process planning and die design engineers
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