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In order to obtain a solution to the question asked, a path leading from deeper levels to the highest level should be proven to be valid. This path spans several predicates involved in providing the high level solution. The process of finding a solution is a searching process, which needs to be conducted efficiently and smartly.

In part design module, there are about 16 main rules that govern the design process. These generic rules are used to evaluate the important parameters like part thickness, type of material, bending tolerance, flanged hole characteristics etc. In process planning module, there are similar rules that guide the entire process. The knowledge representation is based on predicates that contain information about the part state and the list of processes that have to be performed.

This system is illustrated with an industrial example of forming strip in progressive die and the design correlates well with the actual design. Bending tool design is another important issue to be discussed. There are few based on neural network too. The ANN modeling is performed under digital and conditional attributes mode. For the inputs specified, the output of the system is the bending machine code containing information like pressure capacity, maximum stroke, bending speed, motor power, number of cylinders. Esche Esche et al.

The main aim of the system is to develop the process sequence, especially for multi step forming operation, and tool configuration for the whole forming process. In process sequence design, both geometry and formability based decisions will be taken. The process is then analyzed by finite element simulation to check the feasibility of the production. Later tooling for each stage of forming operation will be decided. There are standard steps and rules to evaluate the forming sequence in the system. The system is demonstrated through a sample simulation of round cup made of Aluminium alloy material and compared with experiments.

The radial strain and thickness are also compared with experiments and the results are found to be satisfactory Esche et al. Similar expert system for drawing dies is seen in Lin et al. The efficiency of the system to improve the design quality is demonstrated through inner wheel housing part. Roll forming is a continuous bending operation, in which the ductile sheets are passed through consecutive set of rolls, or stands, each performing only incremental, prescribed bending operation, till the actual cross-section profile is obtained.

This process is particularly suitable for long strips of large quantities, with minimum material handling. Expert systems are available for roll forming pass design that are based on neural network and shape element idea. The example taken for study has four bends and three parallel surfaces connected by slanting sides. The ANN system predicts the output which is the location of the design data. There are almost 63 different locations for the design data that are present with the industrial collaborator.

Classification of the data depends on a number of section parameters, such as the total number of bends, sheet thickness and sheet width prior to forming. The system developed in this project searches the 63 storage locations to find integral shapes. In the case of shape element idea Shen et al. The relationship of space geometry is relatively fixed and the forming steps of these parts are almost same. The validity of the system is presented with few examples like roll forming with symmetrical section, non-symmetrical section, welded pipe, and complex shape of steel window section Shen et al.

The rules are based on information obtained from experienced designers, shop floor engineers, handbooks, journals, monographs and industrial brochures. The materials considered are steel, Al alloys, Zircaloy, welds, and processes like rolling practice, shot peening are modeled. Most of the techniques used are ANN based and few others are based on design rules, specific theories and algorithms.

ANN is found to reproduce the results with maximum accuracy showing its efficiency over rule based systems. The material behavior thus predicted is of academic importance and industrial practice as well. Similarly the KBS for materials management developed by Trethewey et al. Trethewey et al. More details on the KBS for material forming are given in table 2. This welded blank is then formed like un-welded blanks to manufacture automotive components, with appropriate tooling and forming conditions. Applications of TWB include car door inner panel, deck lids, bumper, side frame rails etc.

Some of the advantages of using TWBs in the automotive sector are: 1 weight reduction and hence savings in fuel consumption, 2 distribution of material thickness and properties resulting in part consolidation which results in cost reduction and better quality, stiffness and tolerances, 3 greater flexibility in component design, 4 re-usage of scrap materials to have new stamped products and, 5 improved corrosion resistance and product quality. The forming behavior of TWBs is critically influenced by thickness and material combinations of the blanks welded; weld conditions like weld orientation, weld location, and weld properties in a synergistic fashion.

Designing TWB for a typical application will be successful only by knowing the appropriate thickness, strength combinations, weld line location and profile, number of welds, weld orientation and weld zone properties. Predicting these TWB parameters in advance will be helpful in determining the formability of TWB part in comparison to that of un-welded base materials. In order to fulfill this requirement, one has to perform lot of simulation, experimental trials separately for each of the cases which are time consuming and resource intensive.

The data required for the expert system development is obtained through simulations only. The proposed expert system design for TWB forming is shown in Fig. This expert system is expected to involve three different phases. All the three phases have a design mode of operation where an initial expert system is created and put in place.

The created expert system is then operated in use and update mode. In Phase 1, while the expert system is designed, a range of material properties and TWB conditions are defined within which ANN models are developed to predict the results as discussed in the earlier sections. The same phase while operated in the usage mode, the user selects base material properties and TWB conditions within the chosen range for application and prediction of formability. In this phase, user can select different material models viz.

There is no single strain hardening law and yield theory that can predict the forming behavior of TWBs made of varied sheet materials accurately. Hence in the design mode, ANN models will be developed to predict the forming behavior using different material models. As a result, in the usage mode of the expert system, the user can opt for desired material models to predict the forming characteristics.

Phase 2 involves selecting the forming behavior to be predicted for chosen base material and weld conditions.

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In the design mode, tensile behavior, formability characteristics, deep drawability of welded blanks will be simulated by standard formability tests. Different category of industrial sheet parts will be simulated and expert system will be developed to predict their forming behavior. The global tensile behavior of TWB viz. Formability properties like forming limit curve, percentage thinning, dome height at failure, failure location will be predicted by Limit Dome Height LDH test and in-plane stretching tests using different limit strain criteria say M-K analysis, thickness gradient based necking criterion, effective strain rate criterion, semi empirical approach etc.

Cup deep drawability response like draw depth, weld line movement, punch force, failure location, earing and draw-in profile can be predicted.

Sheet Metal Forming Processes Die Design Book And Cdrom Combo

Also it is planned to develop ANN model and expert system for predicting the formability of application or industry specific sheet parts made of welded blanks. In the usage mode, the user selects the type of test results that is required to be predicted. In phase 3 the training, testing, usage and updating the ANN predictions with simulation results will be performed.

In the design mode operation, various ANNs are created and validated for predicting the forming behavior enumerated in Phase 2 for various combination of material properties and TWB conditions and constitutive behavior enumerated in Phase 1. In the usage mode, the user predicts the required forming behavior for an initially chosen material, TWB condition and constitutive behavior. If the forming behavior predicted is not indicative of a good stamped product, the user changes the above said conditions till he gets satisfactory results.

In the absence of this expert system, the user will have to run time consuming and resource intensive simulation for this iterative stage. In this way, the expert system also learns form the application cases, enhancing the range and success rate of predictions. In this chapter, some representative expert system prediction like the stress-strain behavior, draw-in profile during cup deep drawing, and forming limit curve are presented.

The tools required for tensile test, deep drawing test, Limit dome height test simulation and modeling details can be obtained from Veera Babu et al. The various ANN parameters like number of hidden layers, neurons, and transfer functions are optimized based on many trials to predict the outputs within the normalized error limit of 10 Various network structures with one and two hidden layers with varying number of neurons in each layer are examined. Finally the architecture which yielded better performance is used for modeling.

In all the cases, a feed forward back propagation algorithm is selected to train the network in Matlab programming environment. Here the scaled conjugate gradient algorithm is used to minimize the error. The comparison between ANN predicted true stress-strain behavior and simulation results are shown in Fig. It is also found Siva Krishna, that the FLCs predicted from other failure criteria — effective strain rate, major strain rate based necking criteria, both the original and modified ones Fig.

A slight deviation in the plane strain and stretching modes of deformation is seen in both the intermediate TWB conditions. The suitability of the system is problem specific. A sheet forming engineer who wants to develop an expert system for some industrial TWB sheet part can just make it as part of. This way the expert system is also expanded, becomes more efficient in solving realistic TWB forming conditions. The relations between TWB inputs and outputs are non-linear in nature and hence it is complex to explicitly state rules for making expert system.

But these complex relationships can be easily handled by ANN. In fact, it is not mandatory that the user should know about the input-output relations in TWB. Since this expert system is ANN based, it can potentially become a learning system as the problem solved by the system can also become a part of training examples for customers. The ANN learning and fixing optimum architecture takes time and are problem specific, which can be sorted out by practice. The expert system developed in this work is applicable within the range of input and base material properties specified by Veera Babu et al.

Veera Babu et al. Though this is true, the range specified is large enough to include usable TWB conditions. It is worth to study the applicability of the present expert system outside the range and for many new sheet materials including high strength steels. In this chapter, the expert system applications in designing, planning, and manufacturing of sheet parts is discussed.

Emphasis is given for process design, process sequence and planning, strip layout plan, and tool design. The use of expert system in material forming is also highlighted. Finally an expert system that is being developed to predict the TWB forming behavior is presented. In TWB, the expert system can predict the weld line movement for the given input properties, by which the blank holding force can be varied suitably to minimize the weld line movement.

Some of the systems are neural network based that are capable of handling non-linear relationships in a better fashion and are independent of existing design rules. The strength of ANN based system is that any new material, forming process, process parameter, and industrial parts can be included into the model without formalizing new rules, except that one needs to train and test the network whenever it is updated for new prediction work. Also it looks like most of the systems are developed as per industrial requirements, rather than for academic research. In this case, one has to follow some forming limit criteria to predict the limit strains under varied TWB conditions, as depicted earlier in TWB expert system.

In future, expert systems to design and predict, a the sheet formability of new materials like high strength steels, advanced high strength steels; new processes like hydro forming, micro forming etc. For instance, expert system can be developed for tailor welded blanks made of dual phase steel, friction stir welded blanks made of Al alloy sheets, hydro forming of dual phase steel, spring back and bending of high strength steels that are of practical importance and can be used efficiently in industries.

Efficient expert systems that can predict the microstructural, fatigue, and high temperature behavior of many automotive and constructional materials should be developed in future. ANN model developed by Hosseini et al. Hosseini et al. One can also develop hybrid expert systems that integrate different methods of expert system development like ANN and Genetic Algorithm GA to predict the sheet forming behavior. The best example for this is the spring back prediction work done by Liu et al. Liu et al. Licensee IntechOpen. Help us write another book on this subject and reach those readers.

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Login to your personal dashboard for more detailed statistics on your publications. We are IntechOpen, the world's leading publisher of Open Access books. Built by scientists, for scientists. Our readership spans scientists, professors, researchers, librarians, and students, as well as business professionals. Downloaded: Process design The sheet forming process includes operations like cup drawing, stamping, stretching, bending, ironing, spinning etc.

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  • My Notes. Knovel subscription is supported by Knovel Guest Usage. Cookies are used by this site. To decline or learn more, visit our Cookies page. Knovel offers following tools to help you find materials and properties data Material Property Search. Also known as Data Search, find materials and properties information from technical references.

    Visual and interactive search of NIST pure compounds database for chemicals and their properties. Promotional Toolkit. Engineering Data Module Beta. This Reference is not available in your current subscription. Notify your administrator of your interest. It provides an expanded and more comprehensive treatment of sheet metal forming processes, while placing forming processes and die design in the broader context of the techniques of press-working sheet metal.

    Handbook of Metal Forming

    Included are the "hows" and "whys" of product analysis, as well as the techniques for blanking, punching, bending, deep drawing, stretching, material economy, strip design, movement of metal during stamping, and tooling. While concentrating on simple, applicable engineering methods rather than complex numerical techniques, the author uses many illustrations, tables, and charts to enhance comprehension and learning.

    Show less. View More. Back to Table of Contents. New in Manufacturing Engineering. Thus the piece is divided into two parts. In this, the small piece which comes out is called a Blank desired product and the remaining material in the large piece after blanking is called scrap, as shown above.

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    A Punch and a Die is used for this blanking operation. Punching is also an operation just like Blanking. But the main difference is that the Blank piece which was the desired product in blanking is the scrap here in the punching operation. So it is just opposite to Blanking but the process is almost the same.

    Punch and die are also used here just like blanking operation.

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    Piercing is the operation in which very small holes are created in the sheet metal piece without removing any material from the sheet or by removing very little quantity of material shown below in the image. Punch and die both are used in this process also. The punch used in Piercing operation is generally of bullet shape.

    So I hope this article has made a comprehensive explanation of various sheet metal operations in a detailed way.