2.2 Traditional Approaches of Setup Planning
2.2.1 Decision Tree, decision Table and GT based Approaches
Decision trees and decision tables are useful decision making tools. A decision tree is a way to represent information and knowledge. Conditions (IF) are set as branches of the tree and predetermined actions (THEN) can be found at the junction/node of each branch. The condition specified on each branch must be satisfied in order to traverse that branch. If the condition specified on a branch is true, then that branch can be traversed to reach the next node and this process is continued until a terminal point on the tree is reached. If the condition specified on a branch is false, then another branch may be taken. Figure 2.1 shows the structure of a decision tree.
Figure 2.1. Structure of a decision tree
The algorithm for implementing a decision tree may be written in any of the procedural programming languages such as FORTRAN, PASCAL, C, etc. GT codes and special descriptive languages are used for representing the part description. GT uses similarities between parts to classify them into part families. In the context of machining process planning, a part family consists of a set of parts that have similar machining requirements. An appropriate classification and coding system is to be used for the entire range of parts produced in a shop. All the existing parts are coded following the adopted scheme for coding. Each part family is then represented by a family matrix. The next step is to prepare a standard process plan that can be used by the entire part family. The standard process plans are then stored in a database and indexed by family matrices. Example of some commercially available GT coding systems are Opitz coding, KK-3 coding, MICLASS coding, DCLASS coding, etc [Chang et al., 1998].
An example of the application of decision tree in generative CAPP is EXCAP (Expert Computer-Aided Process-Planning) [Davis and Darbyshire, 1984]. EXCAP generates process plans for machining symmetric rotational components. It uses a decision tree to represent possible operation sequences and backward chaining logic for decision making. Knowledge is represented by production rules. M-GEPPS is another CAPP system which uses decision tree for process planning of rotational parts [Wysk et al. 1988]. KK-3 GT coding is used for providing part information as input to the system. Eight different types of lathe machine operations, viz. cut-off,
facing, turning, drilling, boring, reaming, thread cutting and tapping can be planned by M-GEPPS.
Chitta et al. [1990] developed a decision support system for process planning of both rotational and prismatic parts. The system is capable of producing process plans for parts using different machining operations such as turning, drilling, reaming, boring, slotting, milling, thread cutting, etc. Opitz GT codes are used for providing input information of the parts, machine tools and cutting tools. Opitz is a commercially available part classification and coding system [Chang et al., 1998].
The decision support system is designed to perform coding and classification of parts, generate a list of probable machining operations required, select machine tools and cutting tools, optimize cutting parameters and provide alternative solutions in case of machine breakdown.
Interfacing CAD system with a GT coding system is a common practice for generating feature information for process planning. Nadir et al. [1993] presented an automatic GT coding and classification system for machining rotational parts based on a commercial solid modeller. The system allows a CAD system to be interfaced to a CAPP system through GT coding. Lau and Jiang [1998] used a STEP (Standard for the Exchange of Product Model Data) compliant neutral file to connect dissimilar CAD packages to CAPP through a GT coding scheme. Unlike other coding schemes which have a rigid digit length structure, it has a flexible digit length capability that makes it possible to include all the detail of the features in the codes. The features recognition process works in parallel with the GT coding process. The final step is the generation of process plans for machining of the component. This is achieved through a program which is able to interpret the GT codes and generate an optimized process plan for machining of the component. A similar approach is developed by Lau et al. [2005] for integrating CAD and CAPP which also contributes to research related to GT-based automated process planning.
The benefit of this approach is that the product designs with dissimilar formats from various CAD systems can be interconnected and automatically coded for multiple manufacturing purposes. An expert system development tool, CLIPS (C Language Integrated Production System) is used for developing the process planning function.
The features of the part are sequenced in order to minimise the number of setups and tool changes.
Joshi et al. [1994] describes the development of a generic GT shell for process planning. The system is based on a PC based database management system. Variant GT based approach is used to retrieve an existing process plan and generative techniques are used to modify the plan to suit the new part. It is very flexible and customized GT codes can be developed for different parts
Jiang et al. [1997] presented an automated procedure for milling of prismatic components using a GT coding scheme. The methodology enables the component coding scheme to be integrated with an expert system for the selection of machining operations, sequencing the machining operations, and the selection of cutting tools.
By integrating the proposed GT code and process planning knowledge, the machining facilities can be optimized.
Decision table is another tool used to represent process information. It organises the conditions (IF), actions (THEN) and decision rules in a tabular form. Conditions and actions are placed in rows of the decision table, while decision rules are placed in the columns. When all the conditions in a decision table are met, a decision is taken. The algorithm for implementing the decision table may be written in either some specially developed language or any of the procedural programming languages such as FORTRAN, PASCAL, C, etc. Table 2.1 is a sample decision table.
Table 2.1. A sample decision table
Conditions Rules
Condition-1 Yes No Yes Yes
Condition-2 No No Yes Yes
Condition-3 Yes Yes No No
Actions X X
Action-1 X X
Action-2 X X
CUTTECH (Cutting Technology), a system for selection of cutting tools, speeds, and feeds for machining operations uses both decision tables and algorithms [Barkocy and Zdeblick, 1984]. The knowledge-base comprises machining rules and machine tool and cutting tool data. Rules are applied in descending order of importance to sort a list of tools from the most to the least preferred tools.
HI-MAPP (Hierarchical and Intelligent Manufacturing Automated Process Planner), an automated process planning system uses both decision tables and knowledge-based rules for decision making [Berenji and Khoshnevis, 1986]. The part is represented by a set of form features such as hole, slot, groove, etc. The plan generation process starts with an initial feasible plan given as input. It is then expanded incrementally by including the detail. HI-MAPP considers minimization of total machining time.
Kim et al. [1996] developed an automatic setup planning method for machining of prismatic parts considering machining of reference faces as well as features concurrently. Decision table is used to determine the machining sequence of reference faces based on part dimension, degree of surface roughness, fixture type and cutting tool. Reference faces and features which can be machined in the same setup are searched and machined together so that number of setups is minimized.
Decision trees, decision tables, and GT codes, often used in traditional CAPP systems, work effectively only for simple decision making processes. The main limitation with the decision trees and decision tables is that they are relatively static in terms of representing the process planning knowledge. These are primarily methods to represent knowledge and are coded line by line in the program. Any modification to the current knowledge would require rewriting of the original program. They lack the ability to automatically acquire knowledge and need longer response time. Moreover, GT code based input is not suited for automated process planning systems, since coding is a manual process. It is both time consuming and prone to error. Another major disadvantage of GT coding based systems is the cost involved in creating and maintaining databases for the part families. Table 2.2 shows some traditional efforts of setup planning highlighting the key points.
Table 2.2. Major Setup planning approaches using decision tree, decision table and group technology (GT)
References Type of part Main constraints considered Davis and Darbyshire [1984]
Wysk et al. [1988]
Chitta et al. [1990]
Barkocy and Zdeblick [1984]
Berenji and Khoshnevis [1986]
Kim et al. [1996]
Nadir et al. [1993]
Joshi et al. [1994]
Jiang et al. [1997]
Lau and Jiang [1998]
Lau et al. [2005]
Rotational Rotational
Both rotational and prismatic
Both rotational and prismatic
Prismatic Prismatic
Rotational Prismatic Prismatic Prismatic Prismatic
Precedence relation, resources Precedence relation, process parameters
Process parameters, machine tool and cutting tool capacities
Process parameters, resources Precedence relations, machining time Surface roughness, part geometry,TAD
Topology, part geometry
Part geometry, precedence relations Precedence relations, cutting tool Part geometry, precedence relations Part geometry, flexibility