An expert system for machine tool and cutting tool selection: a modular approach

1989 ◽  
Author(s):  
A. Nagpal ◽  
N. Singh
Keyword(s):  
Expert System ◽  
Machine Tool ◽  
Cutting Tool ◽  
Modular Approach ◽  
Tool Selection

scholarly journals Modularized Cutting Tool Selection Expert System

2014 ◽  
Vol 8 (1) ◽  
pp. 892-898
Author(s):  
Chen Wang ◽  
Wu Zhao ◽  
Ling Chen ◽  
Kai Zhang ◽  
Xin Guo
Keyword(s):  
Expert System ◽  
Knowledge Base ◽  
Feature Recognition ◽  
Cutting Tool ◽  
Tool Material ◽  
Tool Selection ◽  
Type Calculation ◽  
Rule Bases ◽  
Cutting Problem ◽  
Selection Of

This paper is to present a rule-based cutting tool selecting expert system which has knowledge modules and rule bases. Besides, according to different process targets, the selection progress will apply corresponding constraints and rule modules. The logic of tool selection follows a decision-making procedure as an experienced engineers. The strategy of system is to guide the user through several standard steps: information input; feature recognition; selection of machining method; selection of tool material and type; calculation of process parameter and solving cutting problem. This system also has a modularized structure which allows adding new functions and new modules to expand knowledge base and data base. Modules involves in this system are composed of the user interface, knowledge acquisition facility, explanation facility, the knowledge base module, the inference engine and the database module.

scholarly journals Expert System Based on Integrated Fuzzy AHP for Automatic Cutting Tool Selection

2019 ◽  
Vol 9 (20) ◽  
pp. 4308 ◽  
Author(s):  
Xuan Lan Phung ◽  
Hoanh Son Truong ◽  
Ngoc Tam Bui
Keyword(s):  
Expert System ◽  
Cutting Tool ◽  
Fuzzy Ahp ◽  
Cutting Tools ◽  
Multicriteria Decision Making ◽  
Uncertain Information ◽  
Tool Selection ◽  
Power Requirement ◽  
High Productivity ◽  
Workpiece Stability

Cutting tool selection plays an important role in achieving reliable quality and high productivity work, and for controlling the total cost of manufacturing. However, it is complicated for process planners to choose the optimal cutting tool when faced with the choice of multiple cutting tools, multiple conflict criteria, and uncertain information. This paper presents an effective method for automatically selecting a cutting tool based on the machining feature characteristics. The optimal cutting tool type is first selected using a proposed multicriteria decision-making method with integrated fuzzy analytical hierarchy process (AHP). The inputs of this process are the feature dimensions, workpiece stability, feature quality, specific machining type, and tool access direction, which determine the cutting tool type priority after evaluating many criteria, such as the material removal capacity, tool cost, power requirement, and flexibility. Expert judgments on the criteria or attributes are collected to determine their weights. The cutting tool types are ranked in ascending order by priority. Then, the rule-based method is applied to determine other specific characteristics of the cutting tool. Cutting tool data are collected from world-leading cutting tool manufacturer, Sandvik, among others. An expert system is established, and an example is given to describe the method and its effectiveness.

Cutting Tool Selection: An Intelligent Methodology and its Interfaces with Technical and Planning Functions

1992 ◽  
Vol 206 (1) ◽  
pp. 49-60 ◽  
Author(s):  
P G Maropoulos
Keyword(s):  
Machine Tool ◽  
Process Planning ◽  
Production Control ◽  
Cutting Tool ◽  
Selection Process ◽  
Shop Floor ◽  
Local Optimum ◽  
Tool Selection ◽  
Machining Processes ◽  
Selection Methodology

This paper presents a new cutting tool selection methodology, namely the intelligent tool selection (ITS), which covers the whole spectrum of tool specification and usage in machining environments. The selection process has five distinct levels and starts by deriving a local optimum solution at the process planning level, which is progressively optimized in the wider context of the shop-floor. Initially, multiple tools are selected for each machining operation and tool lists are formed by sorting selected tools in order of preference. The second selection level provides a tooling solution for a component by considering all the operations required as well as the characteristics of the machine tool. The selected tools are then rationalized by forming a set of tools for machining a variety of components on a given machine tool at level 3 and by increasing the use of common and standard tools within a group of machines at level 4. Finally, the fifth level aims at reducing tool inventory by classifying existing tools into categories according to their usage and is also used for introducing new tools into the manufacturing system. The selection method allows the implementation of the minimal storage tooling (MST) concept, by linking the ordering of new and replacement tools to production control. ITS also uses the concept of tool resources structure (TRS), which specifies all tooling resources required for producing a component. By using the framework provided by ITS, TRS and MST it can be shown that tooling technology interfaces with diverse company functions from design and process planning to material/tool scheduling and tool management. The selection methodology results in higher utilization of tools, improved efficiency of machining processes and reduced tool inventory.

scholarly journals An Integrated Cutting Tool Selection and Operation Sequencing Method

CIRP Annals ◽  
1992 ◽  
Vol 41 (1) ◽  
pp. 517-520 ◽  
Author(s):  
H.M. Rho ◽  
R. Geelink ◽  
A.H. van 't Erve ◽  
H.J.J. Kals
Keyword(s):  
Cutting Tool ◽  
Tool Selection ◽  
Operation Sequencing ◽  
Sequencing Method

Mathematical Model for the Influence of Machine Tool Parts Deformation on Machining Accuracy

2014 ◽  
Vol 556-562 ◽  
pp. 1354-1357
Author(s):  
Li Gong Cui ◽  
Gui Qiang Liang ◽  
Fang Shao
Keyword(s):  
Mathematical Model ◽  
Mathematical Method ◽  
Machine Tool ◽  
Cutting Tool ◽  
Lower Surface ◽  
Design Stage ◽  
Machining Accuracy ◽  
Cutting Point ◽  
The Mathematical Model

This paper presents a mathematical method to analyze the influence of each machine tool part deformation on the machining accuracy. Taking a 3-axis machine tool as an example, this paper divides the machine tool into the cutting tool sub-system and workpiece sub-system. Taking the deformation of lower surface of the machine bed as the research target, the mathematical model of the deformation on the displacement of the cutting point was established. In order to distribute the stiffness of each part, the contribution degree of each part on the machining accuracy was analyzed. Using this mathematical model, the stiffness of each part can be distributed at the design stage of the machine tool, and the machining accuracy of the machine tool can be improved economically.

Graphical Fault Diagnosis Expert System of CNC Machine Tool Based on B/S

2014 ◽  
Vol 945-949 ◽  
pp. 1707-1712
Author(s):  
Bin Shen ◽  
Shu Yu Zhao ◽  
Jia Hai Wang ◽  
Juergen Fleischer
Keyword(s):  
Fault Diagnosis ◽  
Expert System ◽  
Machine Tool ◽  
Case Based Reasoning ◽  
Cnc Machine Tool ◽  
Cnc Machine ◽  
Rule Based ◽  
Detailed Case ◽  
Case Based ◽  
Remote Fault Diagnosis

Based on the authors previous work of developing an expert system for fault diagnosis of CNC machine tool, this paper studied the theory and method of CNC remote fault diagnosis expert system based on B/S, and presents schema and structure of the expert system in detailed. Case based reasoning is used for the multi-alarm diagnosis, and rule based reasoning is used for single-alarm diagnosis. At last fault diagnosis expert system was designed and developed making use of C# and ASP.NET.

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