Shunmugam M S

Flat pattern development of sheet metal components is a prerequisite for sheet metal fabrication and it also facilitates development of process plans. Commercial softwares for flat pattern development are too expensive and they often require a 3-D model of the component. In fact, most of the component drawings exist as orthographic projections in the majority of industries. A system that can automatically generate a flat pattern using the orthographic projections as the input can be very useful. Such a system is proposed in this paper and it first extracts the features automatically from the orthographic projections and then generates the 3-D wireframe model of the central plane of the component. Using a variant of attributed adjacency graph, the data from the 3-D wireframe model are used for automatic flat pattern development incorporating bending allowances. © 2002 Elsevier Science Ltd. All rights reserved.

To automate planning activities in a computer integrated manufacturing environment, an integrated system of feature recognition and reasoning is essential. An attempt is made in the present work to develop such a system for 3D sheet metal components. Though certain part-modellers use feature-based methodology, they lack the information required for manufacturing and entire feature information is lost when converted to a neutral format such as STEP AP-203. The proposed feature recognition identifies manufacturing features in a generic manner, while feature reasoning gives the information required for manufacturing. Taking 3D model data in STEP AP-203 format as input to the feature recognition system, the central plane of the component is first generated. Further processing of faces is carried out and various features with similar manufacturing attributes are identified using a set of rules based on the topology, geometry and Boolean logic. Different types of manufacturing features such as cut, stretched, drawn and bent features as well as composite features are effectively identified irrespective of their shape. The system proposed here was tested with components taken from industry and examples available in the published literature. The proposed feature recognition system serves as input to the feature reasoning system dealt with in Part II of this work (Kannan, T.R. and Shunmugam, M.S., Processing of 3D Sheet metal components in STEP AP-203 format. Part II: feature reasoning system. Int. J. Prod. Res., 2009 (in press)).

Both feature recognition and reasoning are needed for automating manufacturing planning activities for 3D sheet metal components. The feature reasoning system proposed here generates manufacturing information (e.g. flat pattern, locations of various features, internal cuts and blank profiles, type of tools required, operation sequence, bending sequence, etc.) for the features recognized in Part I of this paper. The issues related to flat pattern developments as experienced while using commercial software have been addressed in this work. A tool selection approach that uses standard tools for single and multiple blow features in punch presses is suggested. A zigzag method for operation sequencing and a virtual bend sequencing system are included. The system has been tested with various components and the results are compatible with industrial practices.

Collision detection is a computationally intensive task within process planning for sheet metal bending. An efficient collision detection algorithm can greatly improve the speed of the process planning. In this work, relevant features are extracted first from STL format of the part and tool models. Next, the collision detection strategies for the sheet metal bending problem are investigated considering bounding volume hierarchies involving oriented-bounding box (OBB) and axis-aligned bounding box (AABB) methods. The approaches are explained using two example parts. By analysing the data for 10 different sheet metal parts, it is demonstrated that although OBB hierarchy is more efficient in terms of minimising the number of collision tests between part and tool models, AABB hierarchy is superior in terms of computation time. The collision detection method based on AABB can be integrated with the sheet metal bend planning to realise CAD–CAM integration.

Process planning for sheet metal bending involves the determination of a near-optimal bend sequence for a given part. The problem is complex since the search space of possible solutions is factorial with respect to the number of bends. In this paper, a two-stage algorithm is described that allows for the quick identification of a near-optimal bend sequence for a given part and set of tools. In the first stage, a Bend Feasibility Matrix is constructed to map the entire search space by taking a geometric approach to the problem. The matrix helps to quickly establish whether the part can be manufactured using the given set of tools. The second stage uses best-first search (graph) algorithm to identify the bend sequence. During search, infeasible sequences are never evaluated and expensive collision tests are not done since the necessary computations are already done in the first stage. Performance of the proposed algorithm is compared with that of genetic algorithm and it is demonstrated that the best-first search algorithm is better than genetic algorithm (GA) to solve the bend sequencing problem.

Bending is one of the vital operations to obtain a three-dimensional (3D) shape in a sheet metal component. While planning for its manufacture, it is important to select proper tools, tool stages and sequence for collision-free bending at every stage. Collinear bends can be performed in a single operation. Tools and tool stages can be reused in a sequence depending on length of tool stage, intermediate shape of the component and availability of tools. When a component has many bends and different tools and tool stages are available, number of alternative bend sequences is very large. Evaluation of all such sequences to obtain a collision-free sequence is very tedious. In the present work, bending is carried out virtually for each sequence and an elitist genetic algorithm is used to determine a near optimal bend sequence for which number of tools, tool stages and handling requirements are minimal. The proposed planner for bend sequencing is developed as a part of an integrated manufacturing planning system for sheet metal components.