An overview of automated manufacturing for composite materials

This paper aims to present an overview of composite materials with the focus on automated manufacturing. First, it provides an initial understanding of what composite materials are, their different classifications and their advantages and disadvantages. Then, manufacturing automation is discussed along with the different classifications of robot that are commonly used and the advantages and disadvantages of automation. Market analysis shows that three industries of interest due to their advancements in composite and automated manufacturing are the automotive, aerospace and marine industries. A review of companies currently implementing composite materials and their use of automated manufacturing within these specific industries has therefore been carried out. Finally, research challenges are highlighted, along with directions for future research

A smart sensor box to increase the adaptability of automated manufacturing

A cost-effective and accurate method to add or change sensors in an automated manufacturing line is essential in order to increase the flexibility and adaptability of production systems. In particular, small to medium enterprises (SMEs) and companies offering custom solutions can only compete in the highly interconnected age of Industry 4.0 if their operations are agile and dynamic. This paper presents a new, low-cost solution to this problem through the development of a Smart Sensor Box. The paper introduces the benefits of this highly adaptable system comparing it to currently available solutions, while testing conducted demonstrates the solution’s accuracy and repeatability. The layout and operational capabilities for three versions of the Smart Sensor Box are discussed in detail and example applications are presented

Comprehensive simulation of cooperative robotic system for advanced composite manufacturing : a case study

Composite materials are widely used because of their light weight and high strength properties. They are typically made up of multi-directional layers of high strength fibres, connected by a resin. The manufacturing of composite parts is complex, time-consuming and prone to errors. This work investigates the use of robotics in the field of composite material manufacturing, which has not been well investigated to date (particularly in simulation). Effective autonomous material transportation, accurate localization and limited material deformation during robotic grasping are required for optimum placement and lay-up. In this paper, a simulation of a proposed cooperative robotic system, which integrates an autonomous mobile robot with a fixed-base manipulator, is presented. An approach based on machine vision is adopted to accurately track the position and orientation of the fibre plies. A simulation platform with a built-in physics engine is used to simulate material deformation under gravity and external forces. This allows realistic simulation of robotic manipulation for raw materials. The results demonstrate promising features of the proposed system. A root mean square error of 9.00 mm for the estimation of the raw material position and 0.05 degrees for the fibre orientation detection encourages further research for developing the proposed robotic manufacturing system

An overview of current research in automated fibre placement defect rework

Automated Fibre Placement (AFP) is receiving increasing attention from academia and industry. This is due to its widespread application in major sectors such as the automotive, marine, aerospace, renewables, and rail industry. AFP defects pose a significant barrier to efficient part throughput due to their lengthy processing time in inspection and rework. While there is a plethora of research on the causes and effects of various defect types, the subsequent reworking of these defects has not been thoroughly investigated. Although some papers have examined manual rework, their scope is limited. Insights from manual hand layup and similar methods can be applied to this context. This article presents the first comprehensive review of the academic literature in the field of defect rework, alongside an analysis of rework process experiments conducted by the authors to provide insight into current industry best practices. Following this, the article focuses on analysing the rework process in AFP and proposes guidelines and best practices. It emphasises the need for further research in this area, along with the findings of other researchers, to demystify the rework activity and inspire new investigations. Such research can offer valuable insights into enhancing reworking techniques and exploring automation possibilities. Automation can assist in manual reworking or even eliminate the need for it entirely, thereby reducing manufacturing time. The suggested directions for future work include developing automated rework processes to achieve near-full automation of AFP

Rework signature : assessing quality of reworked defects in automated fibre placement composites

This paper examinesthe assessment of reworking techniques in the realm of composite materials, going beyond conventional cosmetic pass-fail criteria commonly employed by practitioners. The findings of this study indicate that these criteria are not sufficient in ensuring the quality of reworked laminates. An ultrasonic Non-Destructive Testing (NDT) method was employed to investigate the laminates after curing. Our study utilised a linear ultrasonic phased array roller probe to capture through-thickness, depth-wise images (known as B-scans) perpendicular to the orientation of the top ply surface. This provided insights into the volumetric structure of the samples. Comparisons were drawn between pristine samples, samples with embedded defects, and those subjected to the reworking process in terms of their out-of-plane ply waviness on the internal plies. Despite the successful completion of the rework process, a discernible difference between pristine and reworked samples, termed the "novel rework signature" was identified in some samples. This observation underscores the critical need for optimising reworking activities from a quality perspective. Our experimental approach utilises a novel BenchtopAutomated Fibre Placement (AFP) setup which establishes a robust research environment for simulating real-world conditions. The successful generation of laminates using this method demonstrates its potential as a democratised research environment free of the high capital costs traditionally associated with AFP research and development.This research contributes not only to the understanding of rework effectiveness but also emphasises the importance of a comprehensive approach to quality assessment in composite material manufacturing. Additionally, the quantitative method demonstrates an effective acceptance measure for rework analysis. The findings pave the way for future optimisation strategies, emphasising the necessity of considering both internal and surface characteristics in evaluating the integrity of composite materials

A cooperative mobile robot and manipulator system (Co-MRMS) for transport and lay-up of fibre plies in modern composite material manufacture

Composite materials are widely used in industry due to their light weight and specific performance. Currently, composite manufacturing mainly relies on manual labour and individual skills, especially in transport and lay-up processes, which are time consuming and prone to errors. As part of a preliminary investigation into the feasibility of deploying autonomous robotics for composite manufacturing, this paper presents a case study that investigates a cooperative mobile robot and manipulator system (Co-MRMS) for material transport and composite lay-up, which mainly comprises a mobile robot, a fixed-base manipulator and a machine vision sub-system. In the proposed system, marker-based and Fourier transform-based machine vision approaches are used to achieve high accuracy capability in localisation and fibre orientation detection respectively. Moreover, a particle-based approach is adopted to model material deformation during manipulation within robotic simulations. As a case study, a vacuum suction-based end-effector model is developed to deal with sagging effects and to quickly evaluate different gripper designs, comprising of an array of multiple suction cups. Comprehensive simulations and physical experiments, conducted with a 6-DOF serial manipulator and a two-wheeled differential drive mobile robot, demonstrate the efficient interaction and high performance of the Co-MRMS for autonomous material transportation, material localisation, fibre orientation detection and grasping of deformable material. Additionally, the experimental results verify that the presented machine vision approach achieves high accuracy in localisation (the root mean square error is 4.04 mm) and fibre orientation detection (the root mean square error is 1.84 ∘) and enables dealing with uncertainties such as the shape and size of fibre plies