“…The analysis of PMPs setup and probe configuration for CP3M is mainly integrated into accessibility analysis (Limaiem and Maraghy, 1997; Ziemian and Medeiros, 1997, 1998; Jackman and Park, 1998; Chiang and Chen, 1999; Spitz et al ., 1999; Rico et al ., 2002; Wu et al ., 2004; Alvarez et al ., 2008). If viewed disintegrated, the basic difference is in that the analysis of setup is PMP-oriented, whereas the accessibility analysis is PMP-feature-oriented.…”
Section: Analysis Of Pmps Setup For Cp3mmentioning
confidence: 99%
“…Fig. 2.Local feature accessibility: (a) sphere for thorough slot (GMap i VMap), (b) cone for concave surface, (c) cone for convex surface, and (d) cone for planar surface (adapted from Limaiem and Maraghy (1997) and Spitz et al . (1999)).…”
Section: Analysis Of Pmps Setup For Cp3mmentioning
Industry 4.0 represents high-level methodologies for the development of new generation manufacturing metrology systems, which are more intelligent (smart), autonomous, flexible, high-productive, and self-adaptable. One of the systems capable of responding to these challenges is a cyber-physical manufacturing metrology system (CP2MS) with techniques of artificial intelligence (AI). In general, CP2MS systems generate Big data, horizontally by integration [coordinate measuring machines (CMMs)] and vertically by control. This paper presents a cyber-physical manufacturing metrology model (CP3M) for Industry 4.0 developed by applying AI techniques such as engineering ontology (EO), ant-colony optimization (ACO), and genetic algorithms (GAs). Particularly, the CP3M presents an intelligent approach of probe configuration and setup planning for inspection of prismatic measurement parts (PMPs) on a CMM. A set of possible PMP setups and probe configurations is reduced to optimal number using developed GA-based methodology. The major novelty is the development of a new CP3M capable of responding to the requirements of an Industry 4.0 concept such as intelligent, autonomous, and productive measuring systems. As such, they respond to one smart metrology requirement within the framework of Industry 4.0, referring to the optimal number of PMPs setups and for each setup defines the configurations of probes. The main contribution of the model is productivity increase of the measuring process through the reduction of the total measurement time, as well as the elimination of errors due to the human factor through intelligent planning of probe configuration and part setup. The experiment was successfully performed using a PMP specially designed and manufactured for the purpose.
“…The analysis of PMPs setup and probe configuration for CP3M is mainly integrated into accessibility analysis (Limaiem and Maraghy, 1997; Ziemian and Medeiros, 1997, 1998; Jackman and Park, 1998; Chiang and Chen, 1999; Spitz et al ., 1999; Rico et al ., 2002; Wu et al ., 2004; Alvarez et al ., 2008). If viewed disintegrated, the basic difference is in that the analysis of setup is PMP-oriented, whereas the accessibility analysis is PMP-feature-oriented.…”
Section: Analysis Of Pmps Setup For Cp3mmentioning
confidence: 99%
“…Fig. 2.Local feature accessibility: (a) sphere for thorough slot (GMap i VMap), (b) cone for concave surface, (c) cone for convex surface, and (d) cone for planar surface (adapted from Limaiem and Maraghy (1997) and Spitz et al . (1999)).…”
Section: Analysis Of Pmps Setup For Cp3mmentioning
Industry 4.0 represents high-level methodologies for the development of new generation manufacturing metrology systems, which are more intelligent (smart), autonomous, flexible, high-productive, and self-adaptable. One of the systems capable of responding to these challenges is a cyber-physical manufacturing metrology system (CP2MS) with techniques of artificial intelligence (AI). In general, CP2MS systems generate Big data, horizontally by integration [coordinate measuring machines (CMMs)] and vertically by control. This paper presents a cyber-physical manufacturing metrology model (CP3M) for Industry 4.0 developed by applying AI techniques such as engineering ontology (EO), ant-colony optimization (ACO), and genetic algorithms (GAs). Particularly, the CP3M presents an intelligent approach of probe configuration and setup planning for inspection of prismatic measurement parts (PMPs) on a CMM. A set of possible PMP setups and probe configurations is reduced to optimal number using developed GA-based methodology. The major novelty is the development of a new CP3M capable of responding to the requirements of an Industry 4.0 concept such as intelligent, autonomous, and productive measuring systems. As such, they respond to one smart metrology requirement within the framework of Industry 4.0, referring to the optimal number of PMPs setups and for each setup defines the configurations of probes. The main contribution of the model is productivity increase of the measuring process through the reduction of the total measurement time, as well as the elimination of errors due to the human factor through intelligent planning of probe configuration and part setup. The experiment was successfully performed using a PMP specially designed and manufactured for the purpose.
“…In particular, the same types of screws and bolts are respectively used for the PC model in this research. (7) video (8) RAM (9) DVD (10) CD (11) floppy (12) speaker (13,14) hard drives (15) switch (16)(17)(18) cables, and (19-21) covers.…”
Section: Examples and Discussionmentioning
confidence: 99%
“…A GAC is used to represent feasible angles of attack toward an object. The concept of GACs has been extensively investigated, and leads to promising results for the accessibility analysis of tools in many applications such as multi-axis machining, laser scanning and coordinate measuring machine (CMM) [12][13][14][15].…”
Section: Fast Assembly-tool Feasibility Analysismentioning
“…DirCone GAC( Obstacle X) 1 for each face of the enclosing cube do 2 create a perspective viewing matrix as in Figure 17 3 fill the frame buffer with 1s 4 render X using 0 color bits 5 read the frame buffer into the direction cone's face 6…”
Abstract| Quality and process control activities in a mechanical product's life cycle require that components be measured, or dimensionally inspected. Computer-controlled dimensional inspection is typically performed with Coordinate Measuring Machines (CMMs), which are very precise Cartesian robots that use touch probes to measure the coordinates of points on a workpiece's surfaces. Automatic planning and programming of inspection tasks with a CMM involve spatial reasoning, to determine how to orient the part on the CMM, which probes to use, how to orient the probes, an so on. This paper introduces the notions of accessibility and approachability, which are important for inspection planning, and describes two sets of implemented algorithms for computing accessibility information. One of these sets of algorithms performs exact computations on polyhedral objects and is relatively slow, whereas the other uses discrete approximations and achieves high speed by exploiting standard computer graphics hardware. The discretized algorithm has been tested on real-world parts, and is su ciently fast for industrial applications.
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