Automated 3D Model Generation and Programming of Robot Assembly Cells based on Neutral Files

Automatic layout generation of robotic production cells in a 3D manufacturing simulation environment

Modern manufacturing systems consist of a sequence of robotic systems, such as robotic cells, which are widely used in the industry. As they become commonplace and play important roles in modern industrial production, efficient modelling and use of robotic cells become a necessity. Robotization brings many measurable benefits. It enables effective utilization of both human resources and the possessed machine park and thus contributing to the improvement of product quality and efficiency. Industrial robots not only increase the growth of productivity, by replacing man in monotonous works, requiring enormous, not achievable for human precision but above all ensure the accuracy and repeatability of the product quality. Appropriate design and modelling of robotic manufacturing systems are crucial in the efficient integration of work cell components and proper maintenance of robotic production systems. Therefore, new solutions for programming environments in industrial applications are sought which not only allow reprogramming of industrial robots but also enable the virtual design of new production lines, which in turn allows the faster introduction of higher quality products. This article presents the modelling and programming of robotic work cells in the Siemens Process Simulate environment.

In this chapter, we described the design and integration of a robotic welding cell using a 3D simulation environment. The design was useful for building the CORBA-based distributed robotized welding cell in this research project. Issues such as layout definition, communication design, welding part design, robot and welding station commissioning were considered. The design also included a voice-command driven environment using the Microsoft Speech Application Interface V5.0. Definition of Context Free Grammars were used so that it was possible to start a typical robot task using a human operator's voice using verbal commands such as "weld perimeter" or "weld trajectory". The design and simulation previous to the implementation of an automated welding cell is useful, because possible errors can be prevented such as problems of area distribution, security, dimensions, etc. In addition to its great utility to save costs and avoid unnecessary damage to machinery and equipment. The design of complex robot systems using multisensorial inputs, high-level machine interfaces and distributed networked systems will be elements is of primary importance for advance robot manipulators in the near future so that the work reported in this chapter intents to demonstrate alternative guidelines to design such complex systems.

A simulation model for use in designing robotic assembly cells

The evolution of virtual commissioning has enabled significant advances in analysis, safety testing, programming and failure anticipation in projects involving industrial robots. To make this possible, a virtual model equal to the real one is needed and our research objective is to develop the Digital Twin of the ABB IRB-120 process and robot to digitally validate the robotic cell and minimize installation and project implementation time in the “shop floor” in the context of I4 .0 Currently, a major problem related to Virtual Commissioning activities is the lack of precision between the virtual model and the physical model (digital twin), due to difficulties related to mechanical fabrication after model generation 3D The use of the Process Simulate software (SIEMENS PLM), allowed more accurate and real results to commission the robotic cell of the Robotics Advanced Institute - I.A.R. The main analysis tools are shown in this study.

Auto-Generated Specification of Assembly Units by Formalized Requirements for a higher Maturity in the Engineering Process

Manufacturing companies must ensure high productivity and low production cost in rapidly changing market conditions. At the same time products and services are evolving permanently. In order to cope with those circumstances, manufacturers should apply the principles of smart manufacturing together with continuous processes improvement. Smart manufacturing is a concept where production is no longer highly labor-intensive and based only on flexible manufacturing systems, but production as a whole process should be monitored and controlled with sophisticated information technology, integrated on all stages of the product life cycle. Process improvements in Smart Manufacturing are heavily reliance on decisions, which can be achieved by using modeling and simulation of systems with different analyzing tools based on Big Data processing and Artificial Intelligence (AI) technologies. This study was performed to automate an estimation process and improve the accuracy for production cell’s performance evaluation. Although there have been researches performed in the same field, the substantial estimation process outcome and accuracy still need to be elaborated further. In this article a robot integrated production cell simulation framework is developed. A developed system is used to simulate production cell parametric models in the real-life situations. A set of rules and constraints are created and inserted into the simulation model. Data for the constraints were acquired by investigating industries’ best production cells performance parameters. Information was gathered in four main fields: company profile and strategy, cell layout and equipment, manufactured products process data and shortcomings of goal achievements or improvement necessary to perform. From those parametric case model, a 3D virtual manufacturing simulation model is built and simulated for achieving accurate results. The integration of manufacturing data into decision making process through advanced prescriptive analytics models is a one of the future tasks of this study. The integration makes it possible to use “best practice” data and obtained Key Performance Indicators (KPIs) results to find the optimal solutions in real manufacturing conditions. The objective is to find the best solution of robot integrated cell for a certain industry using AI enabled simulation model. It also helps to improve situation assessment and deliberated decision-making mechanism.

With the development of urbanization in villages and towns, rural layout planning is an important basis for infrastructure construction and plays a role in the field of rural planning and design space. Remote sensing monitoring can provide real-time and effective layout information for the planning system, which is convenient to reflect the rural grid division and layout style intuitively. In this paper, a lightweight deep residual network is used for distributed computing in 3D graphics. Its feature extraction and connection mode are improved by the SDN connection network structure. It is applied to the generation of super-resolution remote sensing images. Through machine learning’s image local feature processing, we construct the vector representation of super-resolution data. Then, we reduce the network layer output by folding the multi-layer convolution, forming the feature matrix space of lightweight processing. The structure introduces a non-parametric remote-sensing information processing mechanism, which can improve the quality of service and computing performance of devices in the Internet of Things. The design model generates more grids, and the logical reasoning time is faster. The physical characteristics enable it to be applied to more application scenarios. After the experimental analysis on the built simulation platform, the processing effect of the proposed model for remote sensing images is significantly better than that of the comparison scheme because the average performance is improved by 11.2%. Further, the best visualization effect is achieved. It keeps the best network communication state. Improving the model plays a decisive role in the quality of remote sensing image feature extraction and determines the performance and effectiveness of the designed algorithm. The method is more suitable for the embedded terminal in the Internet of Things state. It provides technical support for the future development of low-altitude remote sensing and can play a greater practical role.

Three-dimensional models of human intestinal epithelium mimic the differentiated form and function of parental tissues often not exhibited by two-dimensional monolayers and respond to Salmonella in key ways that reflect in vivo infections. To further enhance the physiological relevance of three-dimensional models to more closely approximate in vivo intestinal microenvironments encountered by Salmonella, we developed and validated a novel three-dimensional co-culture infection model of colonic epithelial cells and macrophages using the NASA Rotating Wall Vessel bioreactor. First, U937 cells were activated upon collagen-coated scaffolds. HT-29 epithelial cells were then added and the three-dimensional model was cultured in the bioreactor until optimal differentiation was reached, as assessed by immunohistochemical profiling and bead uptake assays. The new co-culture model exhibited in vivo-like structural and phenotypic characteristics, including three-dimensional architecture, apical-basolateral polarity, well-formed tight/adherens junctions, mucin, multiple epithelial cell types, and functional macrophages. Phagocytic activity of macrophages was confirmed by uptake of inert, bacteria-sized beads. Contribution of macrophages to infection was assessed by colonization studies of Salmonella pathovars with different host adaptations and disease phenotypes (Typhimurium ST19 strain SL1344 and ST313 strain D23580; Typhi Ty2). In addition, Salmonella were cultured aerobically or microaerobically, recapitulating environments encountered prior to and during intestinal infection, respectively. All Salmonella strains exhibited decreased colonization in co-culture (HT-29-U937) relative to epithelial (HT-29) models, indicating antimicrobial function of macrophages. Interestingly, D23580 exhibited enhanced replication/survival in both models following invasion. Pathovar-specific differences in colonization and intracellular co-localization patterns were observed. These findings emphasize the power of incorporating a series of related three-dimensional models within a study to identify microenvironmental factors important for regulating infection.

Image-based 3D shape reconstruction is essential for 3D modeling in recognition, virtual reality, generation of video games, 3D animation, and in the creation of 3D virtual fitting rooms - the sought-after application in this dissertation. In this work, we propose a novel reconstruction method that uses a generic 3D model that roughly controls the overall reconstruction shape which in turn, reduces the number of 2D images needed to just two (frontal and profile) for obtaining a 3D point model. The generic model consists of a massive set of 3D points that lack saliency, topological meaning, and relational interconnections. This is also true about points in the two canonical images. In this work, we propose finding a small set of interconnected ordered intrinsic salient points (control points) residing on the silhouette of the projections of the generic model onto the two image spaces, as well as finding those for the canonical images. Points on the generic model and the images are automatically given saliency with order and interconnections through loop-subdivision operating on those control points and their equivalent points on the canonical images. The transformation of the generic model into the specific object is done through morphing the points on the generic model to follow and be consistent with their equivalent points on the canonical images. The control points on the canonical images are automatically obtained using an active shape model (ASM) that modifies an average trained control point set attained using an offline training, to account for class shape variability and boundary regularization guaranteeing the control points to reside on the silhouette (boundary) of the individual. The reconstructed 3D model is furtherly refined through smoothing the reconstructed surface, while fixing local holes using a principle component analysis (PCA) decomposition. This reconstruction method is convenient, simple, efficient, and results in an average geometric error of less than 0.5% over 700 diverse body models from the CAESAR dataset. We then implement the contour-based articulated model to the personalized 3D model for pose recovery. The evolution of the model through the articulation process is captured in a video clip. 3D transformations are found and applied to the 3D model on its parts so that the projections of the 3D model 'match' those observed in the video sequence at corresponding frames. This is done by minimizing the error between the frontal projection body region points and the target points from the image for each independent moving part, with sub-resolution recovery errors. Once the personalized 3D model is obtained and the articulated model is implemented, the algorithm simulates how garments virtually appear on the reconstructed person under different poses. Different types of clothes are classified as tight or loose, where for the first type of garments, the garment is mapped onto the person's body, whereas for the second type, the mapping adjusts to accommodate the loose nature of the garment, and its fitting on the body of the reconstructed person model. This creates a virtual in-home fitting room for garment fitting, which is timely and is a needed application that would go hand in hand with online clothes shopping, allowing the trying of garments at home with changing poses before ordering them, and can be updated as often as necessary to accommodate changes in physique, weight, aging, etc. This is also done with minimum requirements from the user aside from taking two images and a video of pose articulations using their cam or smart phone, and stands as a useful, needed, and attractive App.

Proceedings of the 2011 joint ACM workshop on Human gesture and behavior understanding

PurposeThis paper aims to propose an automated framework for agile development and simulation of robotic palletizing cells. An automatic offline programming tool, for a variety of robot brands, is also introduced.Design/methodology/approachThis framework, named AdaptPack Studio, offers a custom-built library to assemble virtual models of palletizing cells, quick connect these models by drag and drop, and perform offline programming of robots and factory equipment in short steps.FindingsSimulation and real tests performed showed an improvement in the design, development and operation of robotic palletizing systems. The AdaptPack Studio software was tested and evaluated in a pure simulation case and in a real-world scenario. Results have shown to be concise and accurate, with minor model displacement inaccuracies because of differences between the virtual and real models.Research limitations/implicationsAn intuitive drag and drop layout modeling accelerates the design and setup of robotic palletizing cells and automatic offline generation of robot programs. Furthermore, A* based algorithms generate collision-free trajectories, discretized both in the robot joints space and in the Cartesian space. As a consequence, industrial solutions are available for production in record time, increasing the competitiveness of companies using this tool.Originality/valueThe AdaptPack Studio framework includes, on a single package, the possibility to program, simulate and generate the robot code for four different brands of robots. Furthermore, the application is tailored for palletizing applications and specifically includes the components (Building Blocks) of a particular company, which allows a very fast development of new solutions. Furthermore, with the inclusion of the Trajectory Planner, it is possible to automatically develop robot trajectories without collisions.

Chapter 15 - Three— Dimensional Modeling of Piping Systems

Layout planning and analysis of a Flexible Manufacturing System based on 3D Simulation and Virtual Reality

The subject of creation of simulation and mathematical models is nowadays more and more current and its application is in almost every aspect of life. The article deals with compiling a mathematical model of a pivoting arm using Lagrange equations of the second kind. Subsequently, the model will be created in the simulation program Matlab/Simulink. The simulation model will as well be assembled in the program Adams. The results of these simulations will be compared in the conclusion. This article presents a procedure for resolving a mechanical system from the beginning, from creation of a mathematical model through creation of a simulation model up to evaluation of the simulation results. This paper presents a procedure for resolving mechanical system from the beginning. Thus, it is done by creating a mathematical model through the creation of a simulation model to evaluate the results of the simulation. According to these simulations will produce a working model of the manipulator, which could be used for teaching purposes.