ALGORITHMS FOR IMPROVING SPEED AND ACCURACY OF AUTOMATED 3D RECONSTRUCTION WITH A DEPTH CAMERA MOUNTED ON AN INDUSTRIAL ROBOT

3D reconstruction technology is used in a wide variety of applications. Currently, automatically creating accurate pointclouds for large parts requires expensive hardware. We are interested in using low-cost depth cameras mounted on commonly available industrial robots to create accurate pointclouds for large parts automatically. Manufacturing applications require fast cycle times. Therefore, we are interested in speeding up the 3D reconstruction process. We present algorithmic advances in 3D reconstruction that achieve a sub-millimeter accuracy using a low-cost depth camera. Our system can be used to determine a pointcloud model of large and complex parts. Advances in camera calibration, cycle time reduction for pointcloud capturing, and uncertainty estimation are made in this work. We continuously capture pointclouds at an optimal camera location with respect to part distance during robot motion execution. The redundancy in pointclouds achieved by the moving camera significantly reduces errors in measurements without increasing cycle time. Our system produces sub-millimeter accuracy.

Process limits for percussion drilling of stainless steel with ultrashort laser pulses at high average powers

The availability of commercial ultrafast lasers reaching into the kW power level offers promising potential for high-volume manufacturing applications. Exploiting the available average power is challenging due to process limits imposed by particle shielding, ambient atmosphere breakdown, and heat accumulation effects. We experimentally confirm the validity of a simple thermal model, which can be used for the estimation of a critical heat accumulation threshold for percussion drilling of AISI 304 steel. The limits are summarized in a processing map, which provides selection criteria for process parameters and suitable lasers. The results emphasize the need for process parallelization.

Compensating Data Shortages in Manufacturing with Monotonicity Knowledge

Systematic decision making in engineering requires appropriate models. In this article, we introduce a regression method for enhancing the predictive power of a model by exploiting expert knowledge in the form of shape constraints, or more specifically, monotonicity constraints. Incorporating such information is particularly useful when the available datasets are small or do not cover the entire input space, as is often the case in manufacturing applications. We set up the regression subject to the considered monotonicity constraints as a semi-infinite optimization problem, and propose an adaptive solution algorithm. The method is applicable in multiple dimensions and can be extended to more general shape constraints. It was tested and validated on two real-world manufacturing processes, namely, laser glass bending and press hardening of sheet metal. It was found that the resulting models both complied well with the expert’s monotonicity knowledge and predicted the training data accurately. The suggested approach led to lower root-mean-squared errors than comparative methods from the literature for the sparse datasets considered in this work.

Digital Twin-Driven Process and Equipment FMECA Generation for Smart Manufacturing Applications

Qualtech Systems, Inc. (QSI)’s integrated tool set, consisting of TEAMS-Designer® and TEAMS-RDS® provides a comprehensive digital twin-driven and model-based systems engineering approach that can be deployed for fault management throughout the equipment life-cycle – from its design for fault management to condition-based maintenance of the deployed equipment. In this paper, we present QSI’s approach towards adapting and enhancing their existing model-based systems engineering (MBSE) approach towards a comprehensive digital twin that incorporates constructs necessary for development of a Process Failure Modes and Criticality Analysis (P-FMECA) and integrates that with an Equipment FMECA. The paper will discuss the various levels of automation towards incorporation of these model constructs and their reuse towards automation of the development of the different digital twins and subsequently the automatic generation of the combined Process and Equipment FMECA. This automated ability to develop the integrated FMECA that incorporates both Process-level Failure Modes and Equipment-level Failure Modes allows the system designer and operators to correlate and identify process failures down to their root causes at the equipment-level and thereby producing a comprehensive actionable systems-level view of the entire Smart Manufacturing facility from a fault management design and operations perspective. The paper will present the application of this novel technology for the Advanced Metal Finishing Facility (AMFF) at the Warner-Robins Air Logistics Complex (WR-ALC) in Robins Air Force Base, Georgia, as part of WR-ALC’s initiative towards model-based enterprise (MBE) and smart manufacturing.

Bio-Zirconium Metal–Organic Framework Regenerable Bio-Beads for the Effective Removal of Organophosphates from Polluted Water

Organophosphate-based pesticides, such as diazinon, are among the most toxic organic contaminants to human and environment. Effective removal of diazinon from contaminated water sources is critical. Zirconium Metal−organic frameworks (Zr-MOFs) are promising candidates for the removal of organic contaminants from wastewater. Herein, we report the adequacy of a bio based Zr-MOF named MIP-202 for the removal of diazinon from water. On the other hand, the use of these materials in powder form is not workable, the development of scalable and economical processes and integrative of these materials onto beads is paramount for industrial processes. Hence, it was reported a scalable, bio aqueous solution-based preparation strategy for Bio Zr-MOF beads production. The composite material exposed identical reactivity under the same ambient parameters compared to powdered material in an aqueous solution. These results signify a critical procedure to an integrated strategy for organophosphates removal using bio-based MOFs, which demonstrates high potential for manufacturing applications such as continued removal of organophosphates from wastewater supplies.

Experimental and Numerical Simulation of Neuro Fuzzy Based Cartesian Robot for Soft Material Cutting

Present technology used for soft material cutting robot are laser, water-jet, ultrasonic, plasma and oxy-gas cutting. The scope for designing and fabrication of soft material cutting robots are increasing due to its cost effectiveness and quality of cutting. Cartesian robots are mainly used in assembly and manufacturing applications. They also have a high degree of mechanical rigidity, accuracy, and repeatability. A previous work of more elementary kind was used as a skeleton model to start this work The purpose of the work is the maintenance and implementation of a 4 Degree of freedom robot and also to provide intelligence to the robot using Fuzzy logic and Neuro-Fuzzy for soft material cutting. The kinematic modeling of robot manipulator is done using Denavit-Hartenberg (D-H) parameterization method and Euler-Lagrange method is used for dynamic analysis to determine actuator torque for each joint. The image is acquired with the help of the digital camera which is fixed in a suitable position so that it can scan the entire workspace. Prewitt edge detection algorithm was used for image processing and analysis. The signal for the cutting is interfaced through the Arduino Uno r3 controller.

Research on Mobile User Interface for Robot Arm Remote Control in Industrial Application

The mobile interfaced robot arms are majorly being used nowadays in order to provide the remote-control applicability for various industrial and manufacturing applications. This article proposes a robotic arm platform for controlling the industrial application. The proposed system includes various modules like a robot arm, a controller module and a remote mobile operating application for visualizing the robot arm angles having real time applicability. Augmented reality (AR) is utilized for robot control WIFI communication and the robot angle information is obtained for varying real time environment. This novel approach incorporated the AR technology into mobile application which allow the real time virtual coordination with physical platform. The feasible trajectories are generated using the proposed methodology and a comparison is made between the desired and real trajectory paths. The simulation results are obtained for various assessment indicators and effectual outcomes are achieved with 98.03% accuracy value and 0.185, 0.180 of error and loss values for training phase. The accuracy value of 97.65% is achieved for testing phase with corresponding 0.209 and 0.190 minimum error and loss values. The proposed platform provides the feasible and reliable outcomes in the real time environment for real time manufacturing industry applications.