IFHTSE global 21: heat treatment and surface engineering in first decades of twenty-first century Part 5 – Modelling based design of surface engineered systems

Abstract

Aspects of surface engineering design, namely, simulation of processing and service behaviour and prediction of properties, are discussed and summarised. Available design techniques are demonstrated by means of examples. Two process simulation modelling approaches, relating to oxidation of Ti alloys and nitriding of steels, are described. A novel optimisation approach to extract Young's modulus, yield strength and work hardening exponent of a power law (load–displacement) material is presented. Design and service behaviour simulation case studies of surface engineered titanium alloy gears for the sports car industry are also presented. It is shown that advances in theoretical principles and computational methodologies and tools, together with the ever increasing abundance and accuracy of thermodynamics and kinetics databases of materials and processing atmospheres, have enhanced the reliability of surface engineering simulations to a level good enough for industrial application. Barriers and development trends in surface engineering design are also addressed.     

Maintaining design intent for aircraft manufacture

Design and manufacture of aircraft requires deep multi-disciplinary understanding of system behaviour. The intention of the designer can get lost due to the many changes occurring to the product and the inability of the methods and tools used to capture it. Systems engineering and optimisation tools underpin industrial approaches to design, but are not without issue. The challenge is to find a route from concept to manufacture which enables designers to maintain their original intent. The novelty in this work is that the parameterisation used to build the CAD model reflects the manufacturing capability, ensuring design intent is maintained from concept to manufacture.     

Robustification of CNC Controllers for Machine Tools Motor Drives

This paper presents a methodology for enhancing the robustness of a CNC controlled system by convex optimisation of the Youla parameter. The methodology requires as a first step the design of an initial polynomial controller. This controller is then robustifled considering temporal and frequency constraints, which are formulated by means of the Youla parameterisation within a convex optimisation framework. The optimal Youla parameter is finally obtained by solving this optimisation problem. In this way, a compromise between robustness and closed loop behaviour can be easily managed. An application to the position control of an induction motor drive is presented, where the robustness of different controllers (PID or GPC) regarding model uncertainties in high frequency is enhanced while respecting a temporal template for the disturbance rejection.     

Evolutionary multi-objective design optimisation with real life uncertainty and constraints

This paper presents the application of a robust evolutionary multi-objective evaluation technique for design optimisation in constrained problems with real life uncertainty. The design optimisation approach handles uncertainty using statistical formulations. The approach handles uncertainties associated both with design variables and design criteria. Constraints violation within the neighbourhood of a design is considered as part of a measurement for degree of feasibility and robustness of a solution. An industrial case study is presented on long product rolling system to minimise thermal effects and mechanical stress. The case study incorporates real life design variable tolerance and model sensitivity and process uncertainties.     

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.     

Designing a Turbine Blade Cooling System Using a Generalised Regression Genetic Algorithm

The design of a turbine blade cooling system is a multi-objective optimisation problem involving constraints and complex Interaction among its design variables. The aim of this paper is to develop a methodology to optimise this design using Evolutionary Computing techniques. This paper presents Generalised Regression Genetic Algorithm (GRGA) and the mathematical model of a real-life turbine blade cooling system. Even in the presence of variable interaction, the methodology identifies a number of good feasible designs from which one could be finally chosen based on designer's preferences. The research also demonstrates that GRGA is capable of optimising a real-life design.     

Modelling and optimisation of magnetic abrasive finishing process based on a non-orthogonal array with ANN-GA approach

ABSTRACT

Magnetic abrasive finishing (MAF) is an advanced precise finishing method that achieves micro-level to nano-level surface roughness. In industries, MAF is highly recommended where zero or negligible post-process surface defects are an obligatory requirement. In the same context, process optimisation is essential for making it commercially viable. This study presents an artificial neural network and genetic algorithm (ANN-GA), a robust modelling and optimisation tool (applicable to any sort of data set orthogonal array design or non-orthogonal array design) that is applied to scrutinise and improve the performance of the magnetic abrasive finishing of stainless steel SS302. In addition, the results from ANN-GA modelling and optimisation have been compared with conclusions drawn from conventionally used Taguchi-ANOVA analysis. An L₂₇ non-orthogonal array design has been opted for as per machining set-up restriction. Abrasive size, voltage, machining gap, and rotational speed were the design variables considered in the present research work. It was found that the parametric design used in this study provides a straightforward, methodical, and proficient method of modelling and optimisation of change of surface roughness or finishing behaviour during the MAF process. Modelling and optimisation done with ANN-GA show a maximum value of (ΔR _a)_max equal to 0.256?µm, which is 7% better than the result obtained from Taguchi-ANOVA analysis.     

A review of rolling system design optimisation

Rapid product development and efficient use of existing resources are key competitive drivers in the steel industry and it is imperative that solution strategies are capable of delivering high quality solutions at low cost. However, traditional search techniques for Rolling System Design (RSD) are ad hoc and users of them find it very difficult in satisfying the required commercial imperatives. This paper presents a comprehensive review of approaches for dealing with RSD problems over the years in terms of modelling and optimisation of both quantitative and qualitative aspects of the process. It critically analyses how such strategies contribute to developing timely low cost optimal solutions for the steel industry. The paper also explores the soft computing based technique as an emerging technology for a more structured RSD optimisation. The study has identified challenges posed by RSD for an algorithmic optimisation approach, especially for evolutionary computing based techniques.     

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.     

A design method of data analytics process for condition based maintenance

Many condition monitoring systems use data analytics processes such as anomaly detection to understand machine conditions. Such data analytics processes have been designed by data scientists. However, domain knowledge is indispensable for designing the process, and data scientists have difficulty in acquiring such knowledge from domain engineers. This paper proposes a design method of data analytics processes and an engineering tool. In this method, data scientists propose hypotheses about each step of the process to domain engineers. Then, data scientists update the process on the basis of feedback from domain engineers. The engineering tool helps data scientists to interact with domain engineers.     

Economic application of virtual commissioning to mechatronic production systems

The interaction of heterogenous control hard and software plays a key role in enabling mechatronic production systems to become flexible and agile systems. Nevertheless, control software engineering still tends to be the last step within the development process. To a large extent it is carried out during the commissioning phase of the production ramp-up. On the first hand this leads to a loss of time and quality as well as to a loss of reputation and future orders on the second hand. A method that is referred to as Virtual Commissioning tries to overcome this situation. The aim is to enable control software engineering to, both take over the initiative in system design and to perform important activities earlier in the design process of production equipment. In this paper, the technological and economical scalability of Virtual Commissioning is analyzed. Based on the analysis, a technical concept for a scalable simulation environment is presented. The paper concludes with a new method for the economic application of Virtual Commissioning.     

Combined reverse engineering and CAD approach for mould modelling in casting simulation

Casting process simulation is nowadays established as a strategic tool in process optimisation to improve product quality. However, sometimes the required three-dimensional mathematical model of the casting is not available, because the original drawings are not-up-to date and parts are subjected to design changes. In these cases, reverse engineering (RE) is the most suitable method to reconstruct the geometrical model of the casting. In this paper, a RE-based procedure is proposed to obtain the three-dimensional CAD model of a casting from its physical equipment. The proposed procedure involves four phases: pre-digitising, digitising of equipment parts, surface reconstruction, and three-dimensional CAD modelling. An aeronautical part fabricated by sand casting is selected as case study to verify the feasibility of this approach. Outcomes prove that the RE methodology is adequate to virtually reconstruct the geometry of each single part of the equipment as well as the whole geometry of the assembled mould.     

Prediction of the dynamic behaviour of machine tools during the design process using mechatronic simulation models based on finite element analysis

Increased productivity, higher velocities and acceleration for feed and cutting motions are requirements for innovative machine tools. At the same time the production process must achieve reduced form and position deviations of the work-piece. Therefore knowledge of the dynamic behaviour of machine tools during the design process is essential to develop high-performance machines. Using finite element analysis and mechatronic simulation, taking the mechanical, electrical and control systems into account, is the first step for optimisation. Developing the control parameters using these simulation techniques is one of the major steps in detecting the mechatronic characteristics. This paper presents a method for developing the control parameters concerning tool to work-piece deviations of mechatronic simulation models including disturbance variables. As an example a 2-axis CNC test stand for feed drive axes will be visualised with its simulation and measurement results in the time and the frequency domain.     

Quality optimisation for laser machining under transient conditions

Quality improvements in laser machining have been achieved by a newly developed model-based optimisation strategy. The specific aims of such efforts are to assure machining quality right up to boundaries or pre-machined sections, which are inherent in intricate part geometry. Such boundaries frustrate heat-transfer and result in bulk heating of the workpiece. This in turn leads to a degradation of the machining quality. In order to achieve such optimisation, transient heat-transfer is modeled. Close inspection of the laser–workpiece interaction zone reveals that the machining front exhibits dynamic behaviour, and such mobility plays a significant role in temperature determination. Non-linear parameter adaption profiles are generated via the optimisation strategy in order to stabilise the machining front temperatures. Currently, trial-and-error based experimentation is needed in order to improve machining quality in such regions. Thus model-based optimisation has the added benefit of reducing this step whilst leading to an optimal solution. Experimental results are presented and it is demonstrated that such process manipulation can lead to significant quality improvements.     

Condition based factory planning

Consecutive planning approaches, common in the theory of factory design today, fail to support planning projects in practice. They neglect the interactions and dynamics in the planning task and project as well as the subjectivity introduced by the different stakeholders. Unconsidered interactions, conflicting motives and inflexible project structure lead to time-consuming, expensive and late adaptations. Local optimisation and deviations from the overall objectives are consequences of insufficient synchronisation and coordination. The approach proposed in this paper strives for a paradigm shift from consecutive processes to a modular, parallel approach, which can be reconfigured according to the specific conditions of the planning project and enterprise. This new approach integrates the modularisation and configuration of the planning process as well as aspects of management of instability and second order observation. It has been successfully employed in industry cases, which will be introduced in this paper.     

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.     

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.     

首钢京唐2250mm热轧生产线采用的先进技术

首钢京唐钢铁有限公司2250mm宽带钢热轧生产线按照动态精准设计体系设计,设计产能550万t／a,产品抗拉强度可达1000MPa,设计中采用了当今国际轧钢领域的20多项先进技术,整体工艺技术装备达到国际先进水平。本文介绍了首钢京唐2250mm热轧生产线的设计特点和采用的先进技术。工程设计中采用了步进式加热炉、热装热送技术、定宽压力机、二辊可逆式R1粗轧机、四辊可逆式R2粗轧机、7架四辊精轧机、CVC^plus板形控制技术、具有快速冷却功能的层流冷却技术（TMCP）等;并自主设计开发了托盘式钢卷运输设备、交一直一交传动控制、自动化检测与控制系统及完善的除尘环保系统。该生产线投产后,主要技术经济指标达到设计水平,实现了高效、优质、节能的目标。     

IFHTSE Global 21: heat treatment and surface engineering in the first decades of the twenty-first century: Part 1 – Introduction to ongoing IFHTSE activity

Abstract

Engineering systems and components are the result of a complex chain of actions, starting with a design to match the functional demands: materials science and technology are intimately involved; there are often multiple stages in the manufacturing process, as well as essential quality assurance methods for the component and system. Each step in the process has significance; optimum outcomes and cost effectiveness depend heavily on their being orchestrated and managed as an integrated sequence. Depending on the circumstances, the relative importance of each step may be viewed differently, but the heat treatment and surface engineering manufacturing processes are always likely to be critical. These processes themselves represent a complex balance of materials, technology, energy and environmental factors to achieve an optimum combination of functionality, industrial feasibility and economic and social viability.