scholarly journals Automated Coordinate Measuring Machine Inspection Planning Knowledge Capture and Formalization

Author(s):  
Dimitrios Anagnostakis ◽  
James Ritchie ◽  
Theodore Lim ◽  
Raymond Sung ◽  
Richard Dewar

Capturing the strategy followed during a coordinate measuring machine (CMM) inspection planning session has been an extremely challenging issue due to the time-consuming nature of traditional methods, such as interviewing experts and technical documents data mining. This paper presents a methodology demonstrating how a motion capture-based system can facilitate direct and nonintrusive CMM operator logging for capturing planning strategies and representing in knowledge formats. With the use of recorded motion data, embedded knowledge and expertise can be captured automatically and formalized in various formats such as motion trajectory graphs, inspection plans, integrated definition (IDEF) model diagrams, and other representations. Additionally, a part program can be generated for driving a CMM to execute component measurement. The system's outputs can be used to help understand how a CMM inspection strategy is planned, as well as training aids for inexperienced operators and the rapid generation of part programs.

Author(s):  
Dimitrios Anagnostakis ◽  
James Ritchie ◽  
Theodore Lim ◽  
Raymond Sung ◽  
Richard Dewar

One of the most challenging tasks throughout the development and manufacturing of a product is the capturing and formalization of engineering knowledge and expertise. In the past, many researchers have successfully proposed different techniques for capturing knowledge during the design, process and assembly planning of a product. However, few efforts have focused on applying knowledge capture to the task of product verification for Coordinate Measuring Machine (CMM) inspection; most of these are manual, obtrusive for the user and time consuming since the main sources of knowledge come from documentation such as handbooks, guides or interview transcripts. This paper describes a tool for the automated logging of a planner’s actions while carrying out an inspection planning task in a virtual CMM measurement environment. The tool involves a combination of 3D motion tracking and a post-processor to decipher the context strategy in the form of an inspection plan. Various representations of a captured strategy will benefit CMM operators by providing them a tool for: understanding planning strategies, better training methods for inexperienced users and producing more efficient part programs in a shorter time.


2015 ◽  
Vol 4 (1) ◽  
pp. 125 ◽  
Author(s):  
Wilma Polini ◽  
Giovanni Moroni

Coordinate Measuring Machine (CMM) inspection planning is an activity performed by well-trained operators, but different measurement techniques, using the same data analysis algorithms yield in different measurement results. This is a well-recognized source of uncertainty in coordinate measurement. A CMM, provided with an automatic inspection planning (CAIP) system, permits to implement more accurate and efficient operating procedures and to fit higher quality assurance standards and tighter production timings.In this paper we present a frame of a CAIP system, able to deal with almost all the decisional stages of CMM inspection. Moreover, original approaches have been developed and presented in inspection feature selection, part set-up, probe configuration, and path planning.


2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Osama Abdulhameed ◽  
Abdulrahman Al-Ahmari ◽  
Syed Hammad Mian ◽  
Mohamed K. Aboudaif

Inspection planning is considered an essential practice in the manufacturing industries because it ensures enhanced product quality and productivity. A reasonable inspection plan, which can reduce inspection costs and achieve high customer satisfaction, is therefore very important in the production industry. Considerations such as preparations for part inspection, measuring machines, and their setups as well as the measurement path are described in an inspection plan which is subsequently translated into part inspection machine language. Therefore, the measurement of any component using a coordinate measuring machine (CMM) is the final step preceded by several other procedures, such as the preparation of the part setup and the generation of the probe path. Effective measurement of components using CMM can only be done if the preceding steps are properly optimized to automate the whole inspection process. This paper has proposed a method based on artificial intelligence techniques, namely, artificial neural network (ANN) and genetic algorithm (GA), for fine-tuning output from the different steps to achieve an efficient inspection plan. A case study to check and validate the suggested approach for producing effective inspection plans for CMMs is presented. A decrease of nearly 50% was observed in the travel path of the probe, whereas the CMM measurement time was reduced by almost 25% during the actual component measurement. The proposed method yielded the optimum part setup and the most appropriate measuring sequence for the part considered.


2020 ◽  
Vol 18 (S3) ◽  
pp. 141-152
Author(s):  
Jin Guang ◽  
Fan Li

Computer aided inspection planning based on CAD coordinate measuring machine is the basic key technology, which occupies a very important position in the quality system. Nowadays, the research of integration technology has made a lot of achievements, but the research on information integration of system and system lags far behind the information integration of machining. How to use the part information and measurement-related knowledge provided by the product model to automatically generate optimal testing procedures and testing instructions for use is the basic task of the oriented intelligent system. This paper aims at the development of intelligent CMM inspection planning system based on 3D, from the overall analysis and design of the system to the extraction of detection geometric information, sampling planning, detection path planning and automatic generation of quack measurement program and other related key technologies will be studied. This paper briefly analyzes the relevant factors that determine the number of sampling points and summarizes the point requirements for the distribution of sampling points, and in view of the shortcomings and limitations of various methods of sampling point distribution on the general surface, a sampling strategy with adaptive step size is proposed, which solves the problem of sampling planning on the general surface. In the specific implementation of sampling planning, it is divided into two ways: edge-based and face-based geometric element measurement and sampling. Considering the requirements of distribution points, specific sampling algorithms are designed respectively, focusing on the analysis of the differences between edges and faces.


2006 ◽  
Author(s):  
Haibin Zhao ◽  
Junying Wang ◽  
Boxiong Wang ◽  
Jianmei Wang ◽  
Huacheng Chen

2021 ◽  
Vol 11 (18) ◽  
pp. 8411
Author(s):  
Slavenko M. Stojadinovic ◽  
Vidosav D. Majstorovic ◽  
Adam Gąska ◽  
Jerzy Sładek ◽  
Numan M. Durakbasa

Industry 4.0 represents a new paradigm which creates new requirements in the area of manufacturing and manufacturing metrology such as to reduce the cost of product, flexibility, mass customization, quality of product, high level of digitalization, optimization, etc., all of which contribute to smart manufacturing and smart metrology systems. This paper presents a developed inspection planning system based on CMM as support of the smart metrology within Industry 4.0 or manufacturing metrology 4.0 (MM4.0). The system is based on the application of three AI techniques such as engineering ontology (EO), GA and ants colony optimization (ACO). The developed system consists of: the ontological knowledge base; the mathematical model for generating strategy of initial MP; the model of analysis and optimization of workpiece setups and probe configuration; the path simulation model in MatLab, PTC Creo and STEP-NC Machine software, and the model of optimization MP by applying ACO. The advantage of the model is its suitability for monitoring of the measurement process and digitalization of the measurement process planning, simulation carried out and measurement verification based on CMM, reduction of the preparatory measurement time as early as in the inspection planning phase and minimizing human involvement or human errors through intelligent planning, which directly influences increased production efficiency, competitiveness, and productivity of enterprises. The measuring experiment was performed using a machined prismatic workpiece (PW).


Sign in / Sign up

Export Citation Format

Share Document