Robotic assembly of threaded fasteners in a non-structured environment

Over the past two decades, a major part of the manufacturing and assembly market has been driven by the increasing demand for customised products. This has created the need for smaller batch sizes, shorter production times, lower costs, and the flexibility to produce families of products—or to assemble different parts—with the same sets of equipment. Consequently, manufacturing companies have deployed various automation systems and production strategies to improve their resource efficiency and move towards right-first-time production. Threaded fastening operations are widely used in assembly and are typically time-consuming and costly. In high-volume production, fastening operations are commonly automated using jigs, fixtures, and semi-automated tools. However, in low-volume, high-value manufacturing, fastening operations are carried out manually by skilled workers. The existing approaches are found to be less flexible and robust for performing assembly in a less structured industrial environment. This motivated the development of a flexible solution, which does not require fixtures and is adaptable to variation in part locations and lighting conditions. As a part of this research, a novel 3D threaded hole detection and a fast bolt detection algorithms are proposed and reported in this article, which offer substantial enhancement to the accuracy, repeatability, and the speed of the processes in comparison with the existing methods. Hence, the proposed method is more suitable for industrial applications. The development of an automated bolt fastening demonstrator is also described in this article to test and validate the proposed identification algorithms on complex components located in 3D space

Chain of refined perception in self-optimizing assembly of micro-optical systems

Today, the assembly of laser systems requires a large share of manual operations due to its complexity regarding the optimal alignment of optics. Although the feasibility of automated alignment of laser optics has been shown in research labs, the development effort for the automation of assembly does not meet economic requirements – especially for low-volume laser production. This paper presents a model-based and sensor-integrated assembly execution approach for flexible assembly cells consisting of a macro-positioner covering a large workspace and a compact micromanipulator with camera attached to the positioner. In order to make full use of available models from computer-aided design (CAD) and optical simulation, sensor systems at different levels of accuracy are used for matching perceived information with model data. This approach is named "chain of refined perception", and it allows for automated planning of complex assembly tasks along all major phases of assembly such as collision-free path planning, part feeding, and active and passive alignment. The focus of the paper is put on the in-process image-based metrology and information extraction used for identifying and calibrating local coordinate systems as well as the exploitation of that information for a part feeding process for micro-optics. Results will be presented regarding the processes of automated calibration of the robot camera as well as the local coordinate systems of part feeding area and robot base

Automated freeform assembly of threaded fasteners

Over the past two decades, a major part of the manufacturing and assembly market has been driven by its customer requirements. Increasing customer demand for personalised products create the demand for smaller batch sizes, shorter production times, lower costs, and the flexibility to produce families of products - or different parts - with the same sets of equipment. Consequently, manufacturing companies have deployed various automation systems and production strategies to improve their resource efficiency and move towards right-first-time production. However, many of these automated systems, which are involved with robot-based, repeatable assembly automation, require component- specific fixtures for accurate positioning and extensive robot programming, to achieve flexibility in their production. Threaded fastening operations are widely used in assembly. In high-volume production, the fastening processes are commonly automated using jigs, fixtures, and semi-automated tools. This form of automation delivers reliable assembly results at the expense of flexibility and requires component variability to be adequately controlled. On the other hand, in low- volume, high- value manufacturing, fastening processes are typically carried out manually by skilled workers. This research is aimed at addressing the aforementioned issues by developing a freeform automated threaded fastener assembly system that uses 3D visual guidance. The proof-of-concept system developed focuses on picking up fasteners from clutter, identifying a hole feature in an imprecisely positioned target component and carry out torque-controlled fastening. This approach has achieved flexibility and adaptability without the use of dedicated fixtures and robot programming. This research also investigates and evaluates different 3D imaging technology to identify the suitable technology required for fastener assembly in a non-structured industrial environment. The proposed solution utilises the commercially available technologies to enhance the precision and speed of identification of components for assembly processes, thereby improving and validating the possibility of reliably implementing this solution for industrial applications. As a part of this research, a number of novel algorithms are developed to robustly identify assembly components located in a random environment by enhancing the existing methods and technologies within the domain of the fastening processes. A bolt identification algorithm was developed to identify bolts located in a random clutter by enhancing the existing surface-based matching algorithm. A novel hole feature identification algorithm was developed to detect threaded holes and identify its size and location in 3D. The developed bolt and feature identification algorithms are robust and has sub-millimetre accuracy required to perform successful fastener assembly in industrial conditions. In addition, the processing time required for these identification algorithms - to identify and localise bolts and hole features - is less than a second, thereby increasing the speed of fastener assembly

Integration of a vision-guided robot into a reconfigurable component- handling platform

Thesis (M. Tech.) -- Central University of Technology, Free State, 2010The latest technological trend in manufacturing worldwide is automation. Reducing human labour by using robots to do the work is purely a business decision. The reasons for automating a plant include: Improving productivity Reducing labour and equipment costs Reducing product damage Monitoring system reliability Improving plant safety. The use of robots in the automation sector adds value to the production line because of their versatility. They can be programmed to follow specific paths when moving material from one point to another and their biggest advantage is that they can operate for twenty-four hours a day while delivering consistent quality and accuracy. Vision-Guided Robots (VGRs) are developed for many different applications and therefore many different combinations of VGR systems are available. All VGRs are equipped with vision sensors which are used to locate and inspect various objects. In this study a robot and a vision system were combined for a pick-and-place application. Research was done on the design of a robot for locating, inspecting and picking selected components from a moving conveyor system

Design of an Adaptable Tooling System for Part to Part Variation Processing

Today’s automotive manufacturing facilities use different robotic systems with the specifically designed end of arm tooling (EOAT). Regardless of how accurate these robotic systems may be, they are programmed to repeat the same task and move to the same position repeatedly. As convenient as this process may be, it does not allow robots to automatically readjust to different part variations without the human assistance. This situation is especially noticeable in the plastics manufacturing industry, e.g., fuel tank welding. This thesis describes the systematic design methodology of an adaptable tooling system for a part to part variations processing aimed at automotive plastic fuel tank manufacturing. By combining a 3D vision system with a PLC, and a Fanuc R-2000iB/165F 6 axis robot, the system provides the robot with the ability to automatically readjust the processing unit to different part variations. The design approach specifies programming and device correlation by using Siemens S7, Fanuc TP, and SICK AG software. A case study using a fuel tank sample was developed to check the system for functionality and performance. Results of the study indicate that the system is accurate within ±0.25 mm, which is well suited for fuel tank manufacturing. The study signifies a new approach to vision guided robotics (VGR). It utilizes existing equipment for applications where part variation may be present. Three patent applications were published during the course of this research. They each cover plastic fuel tank welding applications

Implementation of automated assembly

Research has shown that about 60 - 80% wealth producing activities is related to manufacturing in major industrial countries. Increased competition in industry has resulted in a greater emphasis on using automation to improve productivity and quality and also to reduce cost. Most of the manufacturing works such as machining, painting, storage, retrieval, inspection and transportation have changed to automation successfully, except assembly. Manual assembly is predominant over automatic assembly techniques due to inherent assembly problem and the fact that the assembly machines lack the innate intelligence of human operator and lack sufficient flexibility to changeover when product designs and market demands change. With the advent of flexible manufacturing systems, which involve very large capital costs and complex interactions. For the reduction the risk of the investment and analyze the system, simulation is a valuable tool in planning the systems and in analyzing their behavior, and get the best use of them. This thesis applies animation techniques to simulate an automatic assembly system. In chapter 1 to 9, we cover some of the fundamental concepts and principles of automatic assembly and simulation. Some manufacturers put the subject of part orientation first on their list of priorities; but design for assembly (DFA) techniques have proven extremely valuable in developing better assembly techniques and ultimately, better products. We discuss DFA in chapter 1, part feeding and orientation in chapter 2. Chapter 3, 4 and 5 are concerned with assembly process, machines and control system, respectively. Annual sales for industrial robots have been growing at the rate of about 25 percent per year in major industrial countries, we review the robot application in chapter 6. The cost of material handling is a significant portion of the total cost of production, material storage uses valuable space and consumes investment, we cover these two topics in chapter 7 and 8. Chapter 9 is concerned with simulation. In chapter 10, 11,12 and 13, we implement a software package IGRIP to build a model of an automatic assembly system and analyze the result

A Graph-based Optimization Framework for Hand-Eye Calibration for Multi-Camera Setups

Hand-eye calibration is the problem of estimating the spatial transformation between a reference frame, usually the base of a robot arm or its gripper, and the reference frame of one or multiple cameras. Generally, this calibration is solved as a non-linear optimization problem, what instead is rarely done is to exploit the underlying graph structure of the problem itself. Actually, the problem of hand-eye calibration can be seen as an instance of the Simultaneous Localization and Mapping (SLAM) problem. Inspired by this fact, in this work we present a pose-graph approach to the hand-eye calibration problem that extends a recent state-of-the-art solution in two different ways: i) by formulating the solution to eye-on-base setups with one camera; ii) by covering multi-camera robotic setups. The proposed approach has been validated in simulation against standard hand-eye calibration methods. Moreover, a real application is shown. In both scenarios, the proposed approach overcomes all alternative methods. We release with this paper an open-source implementation of our graph-based optimization framework for multi-camera setups.Comment: This paper has been accepted for publication at the 2023 IEEE International Conference on Robotics and Automation (ICRA