Analysis and optimization of temperature distribution in carbon fiber reinforced composite materials during microwave curing process

Vacuum assisted microwave curing technologies and modified optical sensing systems have been employed to investigate the influence of ply orientation and thickness on through-thickness temperature distribution of carbon fiber reinforced composite laminates. Two different types of epoxy systems have been studied. The results demonstrated that the ply orientation did not affect the temperature distribution of composite materials. However, the thickness was an important influencing factor. Nearly 10 ◦C temperature difference was found in 22.5 mm thick laminates. Through analyzing the physical mechanisms during microwave curing, the temperature difference decreased when the heat-loss in surface laminates was reduced and the absorption of microwave energy in the center laminates was improved. The maximum temperature difference of the samples formed using the modified microwave curing technologies in this research could be reduced by 79% to 2.1 ◦C. Compared with the 5.29 ◦C temperature difference of laminates using thermal heating process, the maximum temperature difference in laminates using modified microwave curing technologies was reduced by 60%, and the curing time was cut down by 25%

Sculptured Surface Oriented Machining Error Synthesis Modeling for five-axis Machine Tool Accuracy Design Optimization

Customer-oriented design is very important for machine tool manufacturers to win competition in the market. Mechanical parts with complicated sculptured surface are widely utilized in mechanical systems such as automobiles, aircrafts and wind turbines, and they are often machined by five-axis machine tools with high precision requirements. However, traditional machine tool design has not accounted for the varied machining errors in producing complex sculptured surface, which leads to inferior performance. To address this challenge, a novel machining error synthesis model is proposed in this paper for accuracy optimization in designing general five-axis machine tools used for making various sculptured surfaces. The new synthesis model is constructed by integrating a generic machine tool volumetric error model and two new surface machining error production models, and it bridges between the surface machining profile error and the machine tool accuracy. The synthesis model is then applied as a constraint in machine tool accuracy design optimization. A cost-tolerance function is formulated to construct the objective function, and a heuristic algorithm is developed to implement the optimization. These modeling and optimization methods are validated by one case study

Innovative product development (Editorial)

This special issue on innovative product development contains seven invited papers from the CIRP-sponsored 6th International Conference of Digital Enterprise Technology, held at the University of Hong Kong on the 14th – 16th December 2009 (DET 2009). The conference addresses aspects of electronic business and digital enterprise technology, bringing academic rigour and novelty to industrial applications. Over 120 delegates from 12 countries attended the conference

A comparative experiment for the analysis of microwave and thermal process induced strains of carbon fiber/bismaleimide composite materials

Carbon fiber reinforced bismaleimide composites provide many outstanding properties and are widely used in aerospace applications. However, cure-induced strains are present in virtually all composites that severely impact on the whole service lifecycle of composite components. This paper will demonstrate that the cure-induced strains can be drastically reduced in fiber/ bismaleimide composites using the microwave curing process. Nearly 95% reduction of cure-induced strains has been achieved compared with the conventional thermal heating process. The microwave manufacturing cycle for composites was only 36% of the thermal processing cycle. When using the microwave process, the spring-in angle of an L-shaped part was reduced by about 1.2°Compared by using thermal heating

Incorporation of ship motion prediction into active heave compensation for offshore crane operation

Ship motion has significant effects on certain maritime applications like offshore crane operation. In particular, the vertical heave motion is undesired for safe transferring, accurate positioning and subsea installation. In recent years, there have been growing tasks in utilizing ship motion data for online operation improvement based on the development of virtual simulation environment, digital twin and automatic remote-control systems. How to effectively utilize ship motion data is fundamental to these tasks. This paper presents a neural-network-based method to predict ship motion and use the prediction to improve active heave compensation (AHC) of offshore crane operation. A virtual prototype of the lifting system is developed including implementation of the proposed AHC algorithms. A multilayer perceptron model is trained to predict ship motion. By feeding the future motion of the ship into the controller, the lifting performance can be tested in the virtual environment and the result can be applied to its counterpart. Through simulation with measured sensor data, the proposed method is verified efficient in improving crane operation performance.acceptedVersion© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

On-line part deformation prediction based on deep learning

Deformation prediction is the basis of deformation control in manufacturing process planning. This paper presents an on-line part deformation prediction method using a deep learning model during numerical control machining process, which is different from traditional methods based on finite element simulation of stress release prior to the actual machining process. A fourth-order tensor model is proposed to represent the continuous part geometric information, process information, and monitoring information, which is used as the input to the deep learning model. A deep learning framework with a Conventional Neural Network and a Recurrent Neural Network has been constructed and trained by monitored deformation data and process information associated with interim part geometric information. The proposed method can be generalised for different parts with certain similarities and has the potential to provide a reference for an adaptive machining control strategy for reducing part deformation. The proposed method was validated by actual machining experiments, and the results show that the prediction accuracy has been improved compared with existing methods. Furthermore, this paper shifts the difficult problem of residual stress measurement and off-line deformation prediction to the solution of on-line deformation prediction based on deformation monitoring data

A Co-simulation-Based System Using Vico for Marine Operation

Marine operations are becoming more and more demanding. Efficient modeling and analysis of marine operations under environmental effects, especially in high sea states, will provide a means to improve operational safety. Traditional modeling and analysis are often carried out based on establishing the combined equations of the multi-body system. However, modeling, simulation and analysis of sub-systems may be performed in different software tools or require extensive derivation. It is inconvenient to vary the system configuration regardless of manufacturing design or behavior analysis perspectives. Co-simulation as an emerging technology enables the reusing and sharing of models so that different sub-systems can be modeled independently but simulated together. In this study, a system based on a co-simulation platform - Vico is proposed, which enables the digitalization of marine operations from modeling, configuration to simulation. The system consists of multiple sub-models of the ship, the marine crane and their coupling component, which are all converted and exported as functional mock-up units (FMUs). Various scenario settings such as environmental effect, ship maneuver and crane payload can be configured for the simulation of specific marine operations. Taking the research vessel Gunnerus as the testbed, two case studies about the impacts from the environment and a shipboard crane on marine operations are conducted. The simulation results verify the effectiveness of the marine operation system. The system could also be a foundation for further research on onboard support of marine operations.acceptedVersio