Prediction of Robot Execution Failures Using Neural Networks

In recent years, the industrial robotic systems are designed with abilities to adapt and to learn in a structured or unstructured environment. They are able to predict and to react to the undesirable and uncontrollable disturbances which frequently interfere in mission accomplishment. In order to prevent system failure and/or unwanted robot behaviour, various techniques have been addressed. In this study, a novel approach based on the neural networks (NNs) is employed for prediction of robot execution failures. The training and testing dataset used in the experiment consists of forces and torques memorized immediately after the real robot failed in assignment execution. Two types of networks are utilized in order to find best prediction method - recurrent NNs and feedforward NNs. Moreover, we investigated 24 neural architectures implemented in Matlab software package. The experimental results confirm that this approach can be successfully applied to the failures prediction problem, and that the NNs outperform other artificial intelligence techniques in this domain. To further validate a novel method, real world experiments are conducted on a Khepera II mobile robot in an indoor structured environment. The obtained results for trajectory tracking problem proved usefulness and the applicability of the proposed solution

Neural Extended Kalman Filter for State Estimation of Automated Guided Vehicle in Manufacturing Environment

To navigate autonomously in a manufacturing environment Automated Guided Vehicle (AGV) needs the ability to infer its pose. This paper presents the implementation of the Extended Kalman Filter (EKF) coupled with a feedforward neural network for the Visual Simultaneous Localization and Mapping (VSLAM). The neural extended Kalman filter (NEKF) is applied on-line to model error between real and estimated robot motion. Implementation of the NEKF is achieved by using mobile robot, an experimental environment and a simple camera. By introducing neural network into the EKF estimation procedure, the quality of performance can be improved

NASA Tech Briefs, December 1988

This month's technical section includes forecasts for 1989 and beyond by NASA experts in the following fields: Integrated Circuits; Communications; Computational Fluid Dynamics; Ceramics; Image Processing; Sensors; Dynamic Power; Superconductivity; Artificial Intelligence; and Flow Cytometry. The quotes provide a brief overview of emerging trends, and describe inventions and innovations being developed by NASA, other government agencies, and private industry that could make a significant impact in coming years. A second bonus feature in this month's issue is the expanded subject index that begins on page 98. The index contains cross-referenced listings for all technical briefs appearing in NASA Tech Briefs during 1988

Manufacturing Metrology

Metrology is the science of measurement, which can be divided into three overlapping activities: (1) the definition of units of measurement, (2) the realization of units of measurement, and (3) the traceability of measurement units. Manufacturing metrology originally implicates the measurement of components and inputs for a manufacturing process to assure they are within specification requirements. It can also be extended to indicate the performance measurement of manufacturing equipment. This Special Issue covers papers revealing novel measurement methodologies and instrumentations for manufacturing metrology from the conventional industry to the frontier of the advanced hi-tech industry. Twenty-five papers are included in this Special Issue. These published papers can be categorized into four main groups, as follows: Length measurement: covering new designs, from micro/nanogap measurement with laser triangulation sensors and laser interferometers to very-long-distance, newly developed mode-locked femtosecond lasers. Surface profile and form measurements: covering technologies with new confocal sensors and imagine sensors: in situ and on-machine measurements. Angle measurements: these include a new 2D precision level design, a review of angle measurement with mode-locked femtosecond lasers, and multi-axis machine tool squareness measurement. Other laboratory systems: these include a water cooling temperature control system and a computer-aided inspection framework for CMM performance evaluation

NASA Tech Briefs, July 1990

Topics include: New Product Ideas; NASA TU Services; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences; Life Sciences

NASA Tech Briefs, July 1995

Topics include: mechanical components, electronic components and circuits, electronic systems, physical sciences, materials, computer programs, mechanics, machinery, manufacturing/fabrication, mathematics and information sciences, book and reports, and a special section of Federal laboratory computing Tech Briefs

Supportless Fabrication, Experimental, and Numerical Analysis of the Physical Properties for a Thin-Walled Hemisphere

Although multi-axis bead deposition-based additive manufacturing processes have been investigated in many aspects in the literature, a general process planning approach to address collision detection and prevention still needs to be developed to fabricate complex thin-wall geometries in a supportless fashion. In this research, an algorithm is presented that partitions the surfaces of the part and finds the appropriate tool orientation for each partition to avoid collisions. This algorithm is applied to segment the surface of a thin-wall hemisphere dome and fabricate it without the need of support structures. Two main fabrication strategies are developed: wedge-shaped partitioning, and a rotary toolpath. A five-axis toolpath and a 2+1+1-axis toolpath is introduced to fabricate the partitioned build scenarios. A rotary (1+3-axis) toolpath is also developed. It is concluded that planar slicing brings limitations to reduce the number of partitions that can be modified by a constant step over toolpath. On one hand, the partitioning strategy provides an opportunity to fabricate geometries in a supportless fashion by direct energy deposition additive manufacturing, on the other hand, it introduces physical properties challenges such as surface roughness and hardness variations. Process planning, data collection, and experimental/numerical procedures are implemented to investigate the surface roughness variations (Ra measurement) of fabricated domes. Hence, two solutions are developed using Matlab programming. A mount solution uses the magnified pictures of the exposed surface edges of mount samples as input data. The other solution uses a 3D point cloud of the surface. The innovation of the 3D point cloud solution is the distance factor that is applied in the calculations. The results of this solution are compared to the mount solution. Since the input data of the mount solution is more accurate, the results are more reliable than the 3D point cloud method. The Ra variation diagrams show lower Ra values for the 5-axis sample and the highest values for the rotary sample. Large surface irregularities are noticed at the transition points between partitions, which escalates the roughness values drastically in the region. The sudden alteration of the tool orientation between partitions causes these surface irregularities. Additionally, process planning, data collection, and experimental/numerical analyses are developed to explore hardness variations of the fabricated domes along the slicing direction. The hardness diagram of the 2+1+1-axis sample shows a recognizable pattern for partitions 2-4. The hardness is around 200 (HV) within the partitions but drops to 150 (HV) at the transition points between partitions. Partitions 5-8 show a less recognizable pattern. Although the rotary sample is fabricated in 3 intermittent fabrication sections, it does not show any significant pattern related to the sectioning. The statistical analysis of the hardness shows the highest standard deviation for the 5-axis sample and the least for the rotary one. Finite element analysis of the hardness and residual stress are performed by the ESI Sysweld software for 144 beads of the 2+1+1-axis sample. To reduce the calculation time (a factor of 15 times), a variable mesh size of the beads and substrate are introduced. This means that the element size of the beads grows for the regions farther from the measurement region. The resultant hardness diagram predicts the peak and valley of the experimental diagram for the partitions 1-4, but it misses some patterns for partitions 5-8. Fast Fourier transformation analyses of the surface roughness and experimental/numerical hardness data show a repetitive pattern by the wavelength of the partition length. The preparation time and accuracy of the finite element analysis results reveal that an experimental fabrication and measurement test is preferred at this time, or a new method of numerical analysis is required. This research clearly illustrates the challenges associated with building a complex component and understanding its characteristics. On one hand, splitting the part geometry by different partitioning shapes facilitates the fabrication of the geometries in a supportless fashion. However, this fabrication strategy introduces inconsistency in the mechanical properties. Hardness variations generated by a partitioning strategy needs to be dealt with (possibly by a post-heat treatment). Surface quality at the transient points needs to be investigated more. This foundational research highlights the process planning challenges associated with metal bead based deposition processes, and highlights relevant challenges for similar process families

Multi-angle valve seat machining: experimental analysis and numerical modelling

Modern automotive manufacturers operate in highly competitive markets, heavily influenced by Government regulation and ever more environmentally conscious consumers. Modern high-temperature, high-pressure engines that use high hardness multi-angle valve seats are an attractive environmental option, but one that manufacturers find requires more advanced materials and tighter geometric tolerances to maintain engine performance.Tool manufacturers meet these increasingly tougher demands by using, higher hardness cutting materials such as polycrystalline cubic boron nitride (pcBN), that on paper, promise to wear at a lower rate, require less coolant and deliver tighter tolerances than their carbide counterparts.The low brittle fracture toughness of pcBN makes tools that use it vulnerable to minute chipping. A review of literature for this work pointed to no clear answer to this problem, although suggestions range from manufacturing defects, dynamic and flexibility problems with the production line machinery and fixtures, and radial imbalances in the cutting loads.This work set about experimentally investigating those potential explanations, coming to the conclusion that the high radial imbalance of the cutting loads is responsible for pcBN cutting insert failure during multi-angle valve seat machining, and that by simply relocating the cutting inserts around the multi angle cutting tool, the imbalance can be reduced, thus extending the life of the cutting inserts.It is not always easy to predict the imbalance due to the multiple flexibilities in the system, and simulating such a system in 3D with all its associated cutting phenomena such as friction, thermal expansion, chip flow and shearing, would call upon extraordinary computational power and extremely precise experimental inputs to reduce cumulative error.This thesis proves that such a 3D simulation can be made, that runs in exceptionally short durations compared to traditional methods, by making a number of simplifications.MSC Marc was used to host the simulation, with a parametric script written in Python responsible for generating the model geometry and cutter layout. A Fortran program was developed that is called upon by Marc to calculate the required cutting load outputs and generate new workpiece meshes as material is removed.</div

Optimization of Operation Sequencing in CAPP Using Hybrid Genetic Algorithm and Simulated Annealing Approach

In any CAPP system, one of the most important process planning functions is selection of the operations and corresponding machines in order to generate the optimal operation sequence. In this paper, the hybrid GA-SA algorithm is used to solve this combinatorial optimization NP (Non-deterministic Polynomial) problem. The network representation is adopted to describe operation and sequencing flexibility in process planning and the mathematical model for process planning is described with the objective of minimizing the production time. Experimental results show effectiveness of the hybrid algorithm that, in comparison with the GA and SA standalone algorithms, gives optimal operation sequence with lesser computational time and lesser number of iterations

Optimization of Operation Sequencing in CAPP Using Hybrid Genetic Algorithm and Simulated Annealing Approach

In any CAPP system, one of the most important process planning functions is selection of the operations and corresponding machines in order to generate the optimal operation sequence. In this paper, the hybrid GA-SA algorithm is used to solve this combinatorial optimization NP (Non-deterministic Polynomial) problem. The network representation is adopted to describe operation and sequencing flexibility in process planning and the mathematical model for process planning is described with the objective of minimizing the production time. Experimental results show effectiveness of the hybrid algorithm that, in comparison with the GA and SA standalone algorithms, gives optimal operation sequence with lesser computational time and lesser number of iterations