Optimisation of hybrid tandem metal active gas welding using Gaussian process regression

In this paper, an additional filler wire with opposite polarity was inserted in tandem flux cored arc welding process to increase the welding speed and deposition rate. In this hybrid welding, the optimisation of welding parameters is required to improve the bead geometry which directly indicates the welding quality. However, the correlation between the parameters and the bead geometry is hard to identify, so the process parameters are usually selected intuitively by the experienced engineers. Therefore, welding process modelling is constructed with the Gaussian process regression model, and parameter optimisation is performed with sequential quadratic programming optimisation algorithm. The proposed modelling optimisation process is verified by performing the welding experiment using the parameters that are optimised by the proposed process.     

Physico-chemical behaviour of Poly (Ether Ketone) (PEK) in High Temperature Laser Sintering (HT-LS)

Laser sintering (LS) of polymers has high potential for growth as a manufacturing technique into a wide range of applications provided the range of engineering polymers available for LS expands and machines and LS process conditions are optimised for such materials.This study is the first investigation into laser sintering of both virgin and used polyether ketone (PEK) powder using a bespoke high temperature (HT) polymer laser sintering machine (known commercially as EOSINT P800). The physico-chemical results reveal that, in spite of polymer degradation, used PEK has a viable processing window for LS manufacturing which, combined with optimisation of specific parameters can successfully lead to manufacture of good quality parts. The proposed sintering mechanism of both, virgin and used powders is supported by the experimental data. The incorporation of 30% used HP3 PEK powder led to an approximately 17% drop in tensile strength.     

Structure optimisation of metallic load introduction elements embedded in CFRP

The combination of construction parts made of fibre-reinforced plastics (FRP) and metal holds great lightweight design potential but places high demands on the necessary joining technologies. Metallic load introduction elements that are embedded in the manufacturing process of FRP components are a promising joining technology. In order to fully exploit the potential of this technology, approaches to increase the load bearing capacity of inserts, particularly under pull-out loads, have been missing. The aim is therefore to derive a method for the simulative structural optimisation of embedded inserts. The load bearing capacity increases under pull-out loads through smoothing of failure-critical stress peaks using the optimisation of the thickness distribution of the insert’s base plate. The increase of the load bearing capacity of the optimised insert geometry is confirmed through experimental validation.     

Comparison of multiple regression and back propagation neural network approaches in modelling top bead height of multipass gas metal arc welds

Abstract

With the advanced developments and automation of the welding process, the use of process optimisation techniques has increased. The objective of the present paper is to describe process optimisation techniques for the gas metal arc (GMA) welding process, based on experimental results generated by the process. Back propagation (BP) neural network and multiple regression methods are employed to study relationships between process parameters and top bead height for robotic multipass welding process, and to select a suitable model that provides the weld final configuration and properties as output and employs the process parameters as input. The process parameters, namely pass number, arc current, welding voltage and welding speed are optimised to produce the required top bead height. These techniques have achieved good agreement with the experimental data and yielded satisfactory results. Also, the BP neural network that was developed was compared to the empirical equations for predicting top bead height through additional experiments, and it was evident that the BP neural network was considerably more accurate than multiple regression techniques.     

CVD diamond coatings on geometrically complex cutting tools

The manufacturing of chemical vapour deposition (CVD) diamond coated shaft type cutting tools is demanding due to the complex design of the cutting edges and the cobalt content of the cemented carbide. The influencing parameters of substrate, pre-treatment and diamond film on the tool cutting performance are discussed. The optimised manufacturing route of CVD diamond coated thread milling drills is identified with the use of material and tribological tests. Following the optimised production of the tools, the thread milling drills are then applied in the machining of AlSi17Cu4Mg, whereby the tool performance is characterised with respect to their wear behaviour, the process forces and temperatures as well as the workpiece quality.     

Facility Layout Planning Using Self-Organization Method

This paper proposes a novel approach to facility layout planning based on the concept of biological manufacturing systems that deal with the inherent complexity of manufacturing using ideas of self-organization, evolution and learning. A self-organization method is proposed to generate facility layout plans autonomously according to the material flow which emerges from the local interactions among production elements. A case study of facility layout planning is presented for semiconductor manufacturing, in which it is difficult to find a proper layout because of complex process flow. The effectiveness of the proposed method is discussed in terms of the accumulated traveling distance of products and the throughput.     

Integrating physical and numerical simulation techniques to design the hot forging process of stainless steel turbine blades

The paper presents a joint application of finite-element-based numerical simulation and real-material-based physical simulation techniques for design and optimisation of the hot forging operations to manufacture high strength stainless steel turbine blades.2D simulations of the forging steps carried out using a suitably calibrated finite element model are combined with systematic analysis of microstructure evolution during forging experiments, with particular care to formation of the brittle δ-ferrite phase at high temperatures. A correlation is established between microstructure and thermal and mechanical parameters characterising forging operations.On the bases of numerical and experimental results, the actual forging process is re-designed, reducing the total number of forging steps. Industrial trials, conducted with the optimised process parameters, demonstrate the effectiveness of the developed procedure.     

Recent advances in engineering design optimisation: Challenges and future trends

Traditional engineering design optimisation which is the process of identifying the right combination of product parameters is often done manually, time consuming and involves a step by step approach. This paper identifies recent approaches to automating the manual optimisation process and the challenges that it presents to the engineering community. Engineering design optimisation is classified based on design evaluation effort and degrees of freedom viewpoints. An overview of different approaches for design optimisation is presented. The study identifies scalability as the major challenge for design optimisation techniques. Large-scale optimisation requires significant computing power and efficient algorithms such as swarm intelligence.     

A probabilistic approach for the optimisation of ultrasonic array inspection techniques

Ultrasonic arrays are now used routinely for the inspection of engineering structures in order to maintain their integrity and assess their performance. Such inspections are usually optimised manually using empirical measurements and parametric studies which are laborious, time-consuming, and may not result in an optimal approach. In this paper, a general framework for the optimisation of ultrasonic array inspection techniques in NDE is presented. Defect detection rate is set as the main inspection objective and used to assess the performance of the optimisation framework. Statistical modelling of the inspection is used to form the optimisation problem and incorporate inspection uncertainty such as crack type and location, material properties and geometry, etc. A genetic algorithm is used to solve the global optimisation problem. As a demonstration, the optimisation framework is used with two objective functions based on array signal amplitude and signal-to-noise ratio (SNR). The optimal use of plane B-scan and total focusing method imaging algorithms is also investigated. The performance of the optimisation scheme is explored in simulation and then validated experimentally. It has been found that, for the inspection scenarios considered, TFM provides better detectability in a statistical sense than plane B-scan imaging in scenarios where uncertainty in the inspection is expected.     

高炉用冷拔厚壁无缝钢管的生产工艺试验

概述了武钢炼铁高炉用Φ38mm×11mm×9000mm定尺无缝钢管的生产工艺试验过程。根据产品要求,对冷拔工艺道次进行了优化。按冷加工钢管壁厚变化的规律,重点研究了厚壁管冷变形的情况,并设计出符合高炉用无缝钢管要求的产品。经生产实践证明,其工艺可行。同时指出厚壁管的定壁道次及空拔道次的壁厚变化是该产品生产中的关键。     

Neurofuzzy approach to process parameter selection for friction surfacing applications

Friction surfacing is an advanced manufacturing process, which has been successfully developed and commercialised over the past decade. The process is used for corrosion and wear resistant coatings and for reclamation of worn engineering components. At present, the selection of process parameters for new coating materials or substrate geometries experimentally requires lengthy development work. The major requirement is for the flexibility to enable rapid changes of process parameters in order to develop new applications, with variations of materials and geometries in a cost effective and reliable manner. Further improvement requires development of appropriate mathematical models of the process, which will facilitate the introduction of optimisation techniques for efficient experimental work as well as the introduction of real time feedback adaptive control. This paper considers the use of combined artificial intelligence and modelling techniques. It includes a new frame of a Neurofuzzy-model based Decision Support System — FricExpert, which is aimed at speeding up the parameter selection process and to assist in obtaining values for cost effective development. Derived models can then be readily used for optimisation techniques, discussed in our earlier work.     

激光头中的OPU零件注射成型

介绍了激光头中的OPU零件注射成型的原理、注射工艺和与其相关的工艺参数,分析了OPU零件的生产工艺,论证OPU零件的注射成型技术的可行性。还阐述了零件质量检验的意义和方法,并对生产中出现的一些问题作了分析。     

Fuzzy-logic application in the structural optimisation of a support plate for electrical accumulator in a motor vehicle

In this paper, we analyse the design of a support plate for the accumulator of an electric motor vehicle. The support is an integral part of motor vehicle chassis. Therefore, geometrical configuration and boundary conditions require careful optimisation research of both function and structural behaviour, since lightness and dimension problems in the presence of dynamic stresses due to external factors have to be considered.

Since these factors are complex and not homogeneous, the problem requires multi-criteria analysis. The presence of factors that are not precisely computable calls for fuzzy-logic application to optimisation problems, because fuzzy-logic is non-standard logic, particularly suitable for making choices in structural design.

In plate optimisation, in fact, not numerically quantifiable characteristics such as a part’s workability, numerically determinable structural values such as stresses and strains, and analytically calculable properties such as weight come into play.

These four parameters become the domain of fuzzy membership functions, by which we will extract membership grade values (co-domain). Design variables (domain) are plate thickness, ashlar’s number on the plate and stiffening ashlar’s depth.

In our research, we characterise fuzzy correlation between parameters and required characteristics in order to determine, according to non-standard logics, the best topological configuration which corresponds to the optimisation of individualised characteristics in conformity with design constraints.

Results show value improvement in stress and strain in comparison with the not yet optimised plate and small reduction in workability, whereas the mass is almost the same.     

Solution of the one-dimensional Bratu equation arising in the fuel ignition model using ANN optimised with PSO and SQP

In this paper, an efficient procedure based on the neural networks methodology is presented for the solution of the fuel ignition model in one dimension. The neural networks were optimised with the particle swarm optimisation algorithm hybridised with sequential quadratic programming. The accuracy and convergence of the scheme are analysed by Monte Carlo simulations and their statistical analyses for three test cases of the problem represented by Bratu-type equations. It was found that the hybrid approach converges in all cases, and can solve the problem with higher accuracy and reliability than most of the methodologies used so far to solve this problem.     

Influence of nickel recovery efficiency on crystallinity and microhardness of electroless Ni–P coatings and optimisation using Taguchi technique

Abstract

This research paper reports on the improvement of nickel recovery efficiency (NRE) and microhardness of the electroless Ni–P coating process. At present, in the electroless nickel coating process, the NRE is very low. Owing to this reason, the coating cost of electroless nickel is very high. Earlier researchers have tried to recover the nickel from the used bath after coating is completed and some achieved success in their attempt by further improving the nickel recovery from 25 to 35%. To overcome the above problem, the excess amount of reducing agent (RA) is supplied to the bath during the coating process to compensate for the consumed amount of electrons. The influence of adding excess amount of RA on NRE and other properties of the deposit have been investigated in this study. The NRE was significantly increased from 35 to 61%.The various coatings parameters used in the electroless bath were optimised to get the high NRE using a Taguchi optimisation technique.     

Experimental investigation of the cutting force reduction during the blanking operation of AHSS sheet materials

Within the manufacturing process of sheet metals, blanking represents an essential process operation. As the industrial application of high-strength multi-phase steels grows, the blanking process must consider high blanking and shear forces which are characteristic of these materials. This paper presents possibilities for reducing these forces. Experiments were performed utilizing a novel tool concept which can correlate necessary blanking forces to the punch stroke in three dimensions and in direct force path. Results from three different AHSS materials are presented showing the variation of decisive blanking parameters such as clearance, shearing angle and sheet positioning angle.     

Development of efficient numerical heat transfer model coupled with genetic algorithm based optimisation for prediction of process variables in GTA spot welding

Abstract

Although numerical heat transfer models based on conduction mode of heat transfer have become a strong basis for the quantitative analysis of fusion welding, they still find limited use in actual design for three primary reasons. First, these traditional models consider a volumetric heat source term, which ironically requires a-priori knowledge of the final weld pool dimensions. Second, the numerical models need confident values of a few parameters, e.g. arc efficiency and arc radius, which are usually uncertain and requires many trial and error simulations to realise their suitable values. Third, these models are rarely attempted for the prediction of possible weld conditions for a requisite or target weld dimensions, which is of paramount interest in design for welding. The present work attempts to circumvent these issues by linking a genetic algorithm (GA) based global optimisation scheme with a finite element based three-dimensional numerical heat transfer model. The numerical model includes a volumetric heat source that adapts itself to the computed weld pool geometry at any instant. The GA module identifies the optimum values of a set of uncertain parameters needed for the reliable modelling calculations and next, identifies the suitable values of the process variables, e.g. weld current, for a target weld dimension. In each case, the GA module guides the numerical model to compute weld dimensions for a given set of inputs, traces the sensitivity of the error in prediction on the inputs being optimised, updates them accordingly and reuses the numerical model to finally obtain their optimised values. The complete integrated model is validated with a number of experimental results in gas tungsten arc spot welding processes.     

Hydromechanical Deep-Drawing of Aluminium-Alloys at Elevated Temperatures

This paper presents a technology that combines the different effects used in hydromechanical and warm deep drawing to reduce the drawing force and to increase the transmittable drawing force in the deep drawing process of aluminium sheets by flange heating and by using a counter pressure. Adequate process parameters and an optimised tool design are discussed in thermo-mechanically coupled Finite Element Simulations. The required system parameters, such as temperature and strain rate dependent flow curves, temperature dependent friction coefficients and heat transfer coefficients, were detected in different model experiments for the numerical simulations. Experimental results are presented to highlight the possibilities and limitations of this forming method.     

A virtual machine concept for real-time simulation of machine tool dynamics

When designing CNC machine tools it is important to consider the dynamics of the control, the electrical components and the mechanical structure of the machine simultaneously. This paper describes the structure and implementation of a concept for real-time simulation of such machine tools using a water jet cutting machine as an application. The concept includes a real control system, simulation models of the dynamics of the machine and a virtual reality model for visualisation. The real-time capability of the concept, including the simulation of electrical and rather detailed mechanical component models is proofed. The validation process indicates good agreement between simulation and measurement, but suggests further studies on servo motor, connection and flexibility modelling. However, already from the initial simulation results presented in this paper it can be concluded that the influence of structural flexibility on manufacturing accuracy is of importance at desired feeding rates and accelerations. The fully automated implementation developed in this work is a promising base for dealing with this trade-off between productivity and accuracy of the manufacturing process through multidisciplinary optimisation.     

Optimisation of friction-stir welding process using vibro-acoustic signal analysis

Abstract

This paper presents a mathematical model to predict the tensile strength (TS) of friction-stir welded (FSW) AA1050 aluminium alloy joint. This model has been obtained from the optimisation of the parameters of models developed from vibro-acoustic signals produced during the process. The multiple response method was used to obtain the optimisation model. During experimental development, the process parameters selected to be assessed were rotation speed (RS), travel speed, and tool profile (TP). The TS of the welded joint and the root mean square (RMS) value of the vibro-acoustic signal in the frequency range of 3·2–6·4 kHz were analysed as response variables. In order to validate the model, the optimal values of the factors and responses are presented, showing their agreement with the experimental values obtained. This model can be used to develop and optimise the parameters of the automatic control of the FSW process, based on the vibro-acoustic signal generated.