Machining Process Planning Research Articles

Study on Computer-Aided Process Planning of Mold Part Machining

Large molds with very deep shape are used in producing plastic parts such as bumpers and inner panels of automobiles. In order to realize the precise and stable machining of the deep mold, 3-axis milling with inclined cutters is often applied in Japanese manufactures. Some machining regions are difficult to process with a cutter in a single orientation. Such regions must be subdivided into several smaller regions, so that each region can be processed with a single orientation cutter. The authors propose a new algorithm for automatically subdividing a machining region. A machining region is represented as a set of points covering the region. For each point, all accessible directions of a cutter are computed. Points are then classified into some groups based on the distribution pattern of accessible directions using our improved cluster analysis method. The classification result corresponds to the milling region subdivision.

Machining and Measurement Plans for Impeller Manufacturing

AbstractThe hub and blade surfaces of an impeller are conventionally machined by a 5-axis numerically controlled machine. Efficient rough machining process plans are required in impeller manufacturing, especially, for the rough machining area partitioning and machining data verification. The blade surfaces of a machined impeller have to be measured exactly to secure the machining tolerance and surface finish of the impeller. Although a coordinate measurement machine with a rotating/tilting probe is used for this measurement, it is not easy to evaluate all the points on impeller surfaces since the measurement is very time consuming. Thus, this paper presents efficient machining and measurement plans for impeller manufacturing. A hybrid rough cut plan is proposed, first, which combines 3-axis and 5-axis machining on a machine. Second, a measurement path generation method based on a unit measurement region is introduced. A case example for an impeller is shown to demonstrate the effectiveness of proposed mac...

Study on Computer-Aided Process Planning of Mold Part Machining

Large molds with very deep shape are used in producing bumpers and inner panels of automobiles. In order to realize the precise and stable machining of such deep molds, 3-axis milling with inclined cutters is often applied. Some machining regions are difficult to process with a cutter in a single orientation. Such regions must be subdivided into several smaller regions, so that each region can be processed with a single orientation cutter. We propose a new algorithm for automatically subdividing the machining region. A machining region is represented as a set of cubic blocks in our study. For each block, all accessible directions of a cutter are computed. Neighboring blocks with similar cutter accessible directions are then combined, and some groups of blocks with similar cutter accessible directions are obtained. Proposed algorithm is implemented and an experimental process planning assistance program using this technology is demonstrated.

Modeling and optimization of wire electrical discharge machining of γ-TiAl in trim cutting operation

The prediction of optimal machining conditions for required surface finish and dimensional accuracy plays a very important role in process planning of wire electrical discharge machining. The present work deals with the features of trim cutting operation of wire electrical discharge machining of γ-titanium aluminide. A second-order mathematical model, in terms of machining parameters, was developed for surface roughness, dimensional shift and cutting speed using response surface methodology (RSM). The experimental plan was based on the face centered, central composite design (CCD). The residual analysis and experimental results indicate that the proposed models could adequately describe the performance indicators within the limits of the factors that are being investigated. Finally the trim cutting operation has been optimized for a given machining condition by desirability function approach and Pareto optimization algorithm. It was observed that performance of the developed Pareto optimization algorithm is superior compared to desirability function approach.

Study on Computer-Aided Process Planning of Mold Part Machining

Large molds with very deep shape are well used in producing bumpers and inner panels of automobiles. In order to realize the precise and stable machining of such deep molds, 3-axis milling with inclined cutters are often applied. In this paper, we propose a new algorithm for determining the optimal cutting direction in such inclined machining. We introduce a concept of accessible cone as a measure for evaluating the stability and safety in the inclined machining, and we define the optimal cutting direction as the direction whose corresponding accessible cone has the maximum peak angle. The accessible cone for a specific cutting direction can be derived by rendering the silhouette picture of the offset shape of the mold. This computation can be accelerate by using the graphics processing unit (GPU) which is now equipped in most PCs. Proposed algorithm is implemented and an experimental process planning assistance program using this technology is demonstrated.

Generating intermediate models for process planning

The paper presents a procedure for generating intermediate models, occurring in between machining steps, by sequentially subtracting the intended machining volumes from the rough part model. In the case of high-volume production, a detailed description of intermediate models is essential for various downstream applications. Usually, process engineers manually create approximate two-dimensional drawings and/or three-dimensional solid models to represent intermediate models. However, the manual creation of intermediate models is not ideal because these models cannot be automatically updated as modifications are made in the machining process plan or in the product model. To overcome this shortcoming, a procedure is proposed to support the automatic generation of intermediate models. The proposed procedure is based on common two-dimensional geometric algorithms instead of expensive three-dimensional geometric algorithms.

Study on Computer-Aided Process Planning of Mold Part Machining

In this paper, an algorithm for projecting the machining remained area in a prior machining to the cutter location surface for the next machining is proposed. This projection is useful for automating some process planning task of mold machining, such as the determination of the milling region in the following machining operations. In our proposed algorithm, the milling simulation and the inverse offsetting of the result shape of the simulation is required. Most milling simulation programs use discrete Z-map models for representing the workpiece shape. In this paper, an algorithm for inverse offsetting the Z-map model is discussed. Our algorithm computes dense zig-zag curves covering the Z-map model, then it generates the swept volume of the inversed cutter moving along the curves to obtain their Minkowski sum shape. Proposed algorithm is implemented and an experimental process planning assistance program using this technology is demonstrated.

Optimal tool selection for 2.5D milling, part 2: a Voronoi mountain approach for generalized pocket geometries

Cutter selection is a critical subtask of machining process planning. In part 2 of this two-part series, we develop a robust approach for the selection of an optimal sequence of milling cutters. We rely on the Voronoi mountain construction algorithm developed in part 1 to support generalized pocket geometries containing multiple obstruction regions and contours consisting of circular arc and linear segments. Open edges are supported in both the original pocket contours as well as the intermediate edges that bound removal regions for subsequent tools in the optimal sequence. Exact tool-path lengths are computed for the multiple tool sequences rather than relying on area-based estimates of machining time and cost. An externally supplied table of machining technology data is required to drive the tool selection algorithm.

ONTOLOGICAL MACHINING PROCESS DATA MODELLING FOR POWERTRAIN PRODUCTION IN EXTENDED ENTERPRISE

As a new CAx concept, Digital Factory points out digital design of product & virtual planning of production. In this context, feature technology builds up the entire CAx process chain in Digital Factory, and facilitates STEP based engineering data generation and exchange. The collaborative computer-aided machining process planning of a transfer line is an element in process chain of extended enterprises Automotive Engineering. In practice, it is an approach engaged by both OEMs and their long-term machine suppliers. Application of a CAD-based Digital Factory planning system to design a virtual transfer line needs planning standard data and exchange with supplier. This paper highlights STEP-referenced ontological data modeling approach in the standardized data exchange for collaborative machining process planning, discusses the aspects of its prospective application advantages in machining process planning by OEMs and suppliers for Powertrain production in Automotive Industry.

Optimal tool selection for 2.5D milling, part 1: a solid-modeling approach for construction of the Voronoi mountain

Cutter selection is a critical subtask of machining process planning. In this two-part series, we develop a robust approach for the selection of an optimal set of milling cutters for a 2.5D generalized pocket. In the first article (part 1), we present a solid modeling approach for the construction of the Voronoi mountain for the pocket geometry, which is a 3D extension of the Voronoi diagram. The major contributions of this work include: (1) the development of a robust and systematic procedure for construction of the Voronoi mountain for a multiply-connected curvilinear polygon; and (b) an extension of the Voronoi mountain concept to handle open edges.

Associating a Design Model and its Machining Process Plan

AbstractOne of the major problems with the conventional process of creating products is the poor association between product design and manufacturing engineering. Although, a product is represented as a 3D CAD model, in-process models (IPMs) of a process plan are usually 2D drawings that have little association with the product model. The lack of association requires an excessive amount of effort for managing changes. The concept of digital product creation has been proposed to integrate all the activities involved in the process of creating products. This paper proposes a methodology based on digital manufacturing technology that keeps a proper association between a product model and its machining process plan. For the associativity, the method generates IPMs by removing machining volumes from the stock model (rough part). This paper classifies machining process planning into two different stages: initial process planning and detailed process planning. The first stage focuses on developing a reasonable i...

A Hybrid Feature Recognizer for Machining Process Planning Systems

We describe a hybrid feature recognition method for machining features that integrates three distinct feature recognition methods: graph matching, cell-based maximal volume decomposition, and negative feature decomposition using convex decomposition. Each of these methods has strengths and limitations, which are evaluated separately. We integrate these methods in a sequential workflow, such that each method recognizes features according to its strengths, and successively simplifies the part model for the following methods. We identify two anomalous cases in the application of maximal volume decomposition, and their cure by introducing limiting halfspaces. Feature volumes recognized by all three methods are then combined into a unified hierarchical feature representation, which captures feature interaction information, including geometry-based machining precedence relations.

POWERTRAIN MACHINING FEATURE IDENTIFICATION

Presented in this paper is a procedure to identify machining features of powertrain components. Machining feature recognition is one of the most important steps for machining process planning. In the case of powertrain components, the first step is to compare a machined model (finished part model) and the corresponding rough part model to identify the volume which should be removed from the rough part model. In regard to the comparison, the most intuitive idea is to use a 3D BOOLEAN operation. Although this approach looks fine, it might not take advantage of the inherent attributes of powertrain component machining. This paper focuses on two important attributes of powertrain machining: (1) a machined model and the corresponding rough part model are very similar and have many identical faces and (2) a rough part model always contains the machined model. Based on these two attributes, we develop an efficient procedure for identifying powertrain machining features. Since the proposed procedure employs well-known 2D geometric algorithms instead of 3D BOOLEAN operations, it is very efficient and robust.

A surface based approach to recognition of geometric features for quality freeform surface machining

The paper presents a surface-based approach for geometric feature recognition for the purpose of automating the process planning of freeform surface machining. The proposed approach consists of the following four steps for recognition of the geometric features: conversion and preprocessing of the surface geometry data, subdivision of NURBS surface, reconstruction of surface orientation areas, and recognition of geometric features. The proposed scheme assumes that the input geometry data form is based on an IGES CAD model and the surface model can be represented in the form of trimmed NURBS surfaces. The connectivity relations of the product surfaces are analyzed and each surface is subdivided into orientation regions based on the surface normal vector over a certain point density grid, and then all the connected regions with the same orientation type are grouped into surface orientation areas. After that, the geometric feature will be recognized through the analysis of area connectivity and relationship. The paper describes the developed algorithms on surface orientation region subdivision, surface orientation area reconstruction, and geometric feature recognition. The verified feasibility study of the developed method is also presented.

Parametric Modeling of Part Family Machining Process Plans From Independently Generated Product Data Sets

Parametric modeling has become a widely accepted mechanism for generating data set variants for product families. These data sets include geometric models and feature-based process plans. They are created by specifying values for parameters within feasible ranges that are specified as constraints in their definition. These ranges denote the extent or envelope of the product family. Increasingly, with globalization the inverse problem is becoming important: Given independently generated product data sets that on observation belong to the same product family, create a parametric model for that family. This problem is also of relevance to large companies where independent design teams may work on product variants without much collaboration only to later attempt consolidation to optimize the design of manufacturing processes and systems. In this paper we present a methodology for generating a parametric representation of the machining process plan for a part family through merging product data sets generated independently from members of the family. We assume that these data sets are feature-based machining process plans with relationships such as precedences between the machining steps for each feature captured using graphs. Since there are numerous ways in which these data sets can be merged, we formulate this as an optimization problem and solve using the A* algorithm. The parameter ranges generated by this approach will be used in the design of tools, fixtures, material handling automation and machine tools for machining the given part family.

Machining process planning of prismatic parts using case-based reasoning and past process knowledge

A process planner for three-dimension prismatic parts is developed in this paper by utilizing case-based techniques. A three-dimension prismatic part is represented by a set of primary features (such as holes, pockets, slots, etc). The subplan candidates for individual features of a part are first generated by the proposed system via a backward inference planner based on the specifications of cutting tools available in a factory. The system then combines all subplans into the final process plan for a given part based on the merging information. The merging information is the information regarding the manufacturing environment of a factory (i.e. machine layout, transfer line, etc), and plays a key role in the process planning. Generally, the merging information is contained in old plans, and will be extracted by the system using case-based techniques. This way, the proposed system can generate a practical process plan for a given part based on case histories provided by the factory itself. The proposed process planner is composed of five major components: feature indexer, retriever, modifier, simulator, and repairer. It is implemented on a Sun workstation using the ACIS geometric modeler and C++ .

Recognition of machining features for cast then machined parts

Mechanical parts are typically manufactured using multiple manufacturing processes. Primary processes such as casting realize the primary shape of the part, while secondary processes such as machining generate more detailed shape of the part. This paper presents a feature recognition method to support machining process planning for cast-then-machined parts. From the part model including the specification of machined faces, we generate the starting workpiece for machining, which represents the casting output in sufficient detail to support machining process planning. The starting workpiece is generated by identifying faces to be made by casting followed by machining, then offsetting the part through these faces by a uniform machining thickness to obtain cast faces, and combining the halfspaces induced by machined faces and the halfspaces induced by their bounding cast faces to enclose removal volumes. Machining features are then recognized from the removal volumes using a volume decomposition method called Alternating Sum of Volumes with Partitioning.

Indexing and retrieval in machining process planning using case-based reasoning

To reduce the manufacturing time of a product, one effective way to develop a machining process plan for a new part is to retrieve a relevant case of process planning similar to a new desired part and then adapt the retrieved case to meet the new requirements. This paper proposes a mechanism for retrieval of process planning cases. The core of the retrieval mechanism contains: (1) a feature-based representation of a part and cutting processes; (2) indexing of a part; (3) a feature hierarchical structure based on cutting processes; and (4) a similarity metric used to measure the similarity between a new desired part and any old part in the case base. The application domain here is for axisymmetric part machining. A prototype based on the retrieval mechanism is implemented on a Sun workstation using the ACIS 3D-Toolkit from Spatial Technology Inc.

A hybrid approach for machining process optimization using multiple experts data

A hybrid analytical hierarchy approach, based on the expert ranking technique for machining process optimization and planning, is demonstrated in this article. The proposed approach enables the transfer of data, information, and knowledge between the different domains related to manufacturing processes. It also combines the characteristics of both the practical experiences of industrialists with the output of a mathematical analysis model, to obtain more accurate knowledge that can be used for weight ranking, and accordingly process optimization. This approach helps designers as well as manufacturing planners to make decisions concerning the selections of tools, machines, or machining processes based on different MULTI-expert views.

Tolerance chart optimization for quality and cost

This paper introduces a mathematical model for tolerance chart balancing during machining process planning. The criteria considered in this study are based on the combined effects of manufacturing cost and quality loss, under the constraints of process capability limits, design functionality restrictions, and product quality requirements. Manufacturing cost is expressed in geometrical decreasing functions, which represent tolerances to be assigned. Process variability is expressed in quadratic loss functions, which represent the deviation between part measurement and the target value. Application of this model minimizes the total cost of manufacturing activities and quality issues relating to machining process planning, particularly in the early stages of planning.