topography method is a well-known non-contacting 3-D measurement method. Recently, the automatic 3-D measurement by topography has been required, since the method was frequently applied to the engineering and medical fields. The 3-D measurement using projection topography is very attractive because of its high measuring speed and high sensitivity. In this paper, using two-wavelength method of projection topography was tested to measuring object with problems. The experimental results prove that the proposed scheme is capable of finding absolute fringe orders, so that the problems can be effectively overcome so as to treat large step discontinuities in measured objects.

The purpose of this paper is to generate realistic production scheduling in the supply chain. The scheduling model determines the best schedule using operation sequences and machine and strongly satisfies the due dates of customer order. The model is NP-hard in the strong sense in general. And, real system can be happened various kinds of uncertain factors such as queuing, breakdowns and repairing time in the manufacturing supply chain. To solve this problem a hybrid approach involving a genetic algorithm (GA) and computer simulation is proposed. Such an approach has not been treated in the literature. The GA is employed in order to quickly generate feasible production and delivery schedules. The simulation is used to minimize the maximum completion time for the production and delivery plan with last sequence with fixed schedules from the GA model. More realistic production and delivery schedules with an optimal completion time by performing the iterative hybrid approach can be obtained. This proposed approach generates: (1) selecting the best machine for each operation, (2) deciding the sequence of operation to product and route to deliver, (3) minimizing the makespan for each order. The results of computational experiments for a simple example of the supply chain are given and discussed to validate the proposed approach. It has been shown that the hybrid approach is powerful for complex production delivery scheduling in the manufacturing supply chain.

Mechanical parts are often grouped into part families based on the similarity of their shapes, to support efficient manufacturing process planning and design modification. This paper presents a similarity assessment technique to support part family classification for machined parts. It exploits the multiple feature decompositions obtained by the feature recognition method using convex decomposition. Convex decomposition provides a hierarchical volumetric representation of a part, organized in an outside-in hierarchy. It provides local accessibility directions, which supports abstract and qualitative similarity assessment. It is converted to a Form Feature Decomposition (FFD), which represents a part using form features intrinsic to the shape of the part. This supports abstract and qualitative similarity assessment using positive feature volumes. FFD is converted to Negative Feature Decomposition (NFD), which represents a part as a base component and negative machining features. This supports a detailed, quantitative similarity assessment technique that measures the similarity between machined parts and associated machining processes implied by two parts’ NFDs. Features of the NFD are organized into branch groups to capture the NFD hierarchy and feature interrelations. Branch groups of two parts’ NFDs are matched to obtain pairs, and then features within each pair of branch groups are compared, exploiting feature type, size, machining direction, and other information relevant to machining processes.

Moire topography method is a well-known non-contacting 3-D measurement method. Recently, the automatic 3-D measurement by moire topography has been required since the method was frequently applied to the engineering and medical fields. 3-D measurement using projection moire topography is very attractive because of its high measuring speed and high sensitivity. In this paper, using two-wavelength methods of projection moire topography was tested to a measuring object with 2pi -ambiguity problems. Experimental results prove that the proposed scheme is capable of finding absolute fringe orders, so that the 2pi -ambiguity problems can be effectively overcome so as to treat large step discontinuities in measured objects.

Abstract FAPPS (Feature-based Automatic Process Planning System) is developed as a comprehensive metal cutting process planning system operated in PC environments. It can recognize the machining features automatically from a given 3D part design model, and then generates operation sheets, divided process drawings, NC codes, and inspection sheet. It consists of the following modules: tolerance input module for menu-driven input of tolerances, feature recognition module for automatic recognition of pre-defined machining features and compound features, process planning module for rule based determination of machining processes, divided process drawing module for automatic generation of divided process drawings, operation planning module for rule based generation of specific operation plans, and measurement planning module for automatic generation of CMM measurement plans. The CMM measurement planning in FAPPS uses both geometric information and tolerance information from CAD files in order to determine measurement surfaces, number and positions of measurement points, and measurement sequences for inspecting machined parts. The measurement plan is represented in DMIS format for automated measurements using CMM’s. The measurement planning module that is realized in FAPPS is explained in this paper with the developed algorithms. Fuzzy logic calculation is used to determine the number of measurement points and geometric consideration for selecting measurement positions is performed.

An automobile body assembly is a complex system consisting of hundreds of compliant sheet metal parts. A number of locating schemes are used throughout the assembly and inspection processes. This paper presents a methodology to represent the assembly and inspection processes of an automobile body for tolerance analysis. The proposed representation methodology consistently describes dimensional variations with respect to various locating schemes that change through the assembly process.

In order to improve machining quality of parts, tolerance information should be easily exchanged among product designers, process planners, operators and inspectors. The tolerance information exchange is not yet accomplished since common formats for tolerance information management are being under development. So far the tolerance is recorded as text information in the drawings of commercial 3D CAD systems, which requires human interpretation for its use in cutting process planning and inspection planning processes.

Abstract This paper presents a feature-based method to support machining sequence planning. Precedence relations among machining operations are systematically generated based on geometric information, tolerance specifications, and machining expertise. The feature recognition method using Alternating Sum of Volumes With Partitioning (ASVP) Decomposition is applied to obtain a Form Feature Decomposition (FFD) of a part model. Form features are classified into a taxonomy of atomic machining features, to which machining process information has been associated. Geometry-based precedence relations between features are systematically generated using the face dependency information obtained by ASVP Decomposition and the features’ associated machining process information. Multiple sets of precedence relations are generated as alternative precedence trees, based on the feature types and machining process considerations. These precedence trees are further enhanced with precedence relations from tolerance specifications and machining expertise. Machining sequence planning is performed for each of these precedence trees, applying a matrix-based method to reduce the search space while minimizing the number of tool changes. The precedence trees may then be evaluated based on machining cost and other criteria. The precedence reasoning module and operation sequence planning module are currently being implemented within a comprehensive Computer-Aided Process Planning system.

For the rational scheduling of production in descrete machining shop, standard time needs to be estimated correctly. The estimation depends on the accuracy of cutting condition. Machinists usually make modifications to recommended cutting conditions suggested by process planner in order to satisfy requirements for individual operation. The systematization of the modification procedure by using neural network methodology is proposed in this paper. Also, other key functions of the operation planning system for prismatic components which includes the module for cutting condition are briefly described. The results of operation planning are demonstrated with an example part.