Approach to net-shape preforming using textile technologies. Part I: edges

Considering cost-effective fibre-reinforced plastic (FRP) parts manufactured by resin transfer moulding (RTM) technologies, net-shape preforms can contribute to mechanical post treatment free manufacturing, hence reducing cycle-time and cost. In addition to this, RTM specific problems, such as fibre pinching when closing the tool can be avoided. The wide range of textile technologies used for FRP parts offers various possibilities to achieve net-shape parts within the preforming process including bores or inserts (see part II). The potential of the presented technologies is evaluated and discussed. Apart from standardised testing methods, a new side-impact testing was specially designed for determining the in-plane damage-tolerance of net-shape manufactured FRP parts. Folding to net-shape shows poor quality but improves mechanical properties. Rim-protections with braided inserts or over-edge stitching technologies which provide exact preform dimensions, improved mechanical performance. Especially side-impact tolerance or in-plane damage tolerance was increased. Ultra-sonic testing was successfully applied to visualise the differences in damage tolerance and propagation between these new rim technologies.     

陶瓷注射成形中脱脂工艺及其新进展

陶瓷注射成形是先进的陶瓷近净成形技术,可高效成形形状复杂的陶瓷部件.而脱脂是陶瓷注射成形中最复杂和重要的环节.详细介绍了陶瓷注射成形各种脱脂工艺的脱脂原理,并对各种脱脂工艺的优缺点进行了比较.     

A DSS approach to developing systems to support production planning and control in smaller companies

Smaller Manufacturing Enterprises (SMEs) would benefit from manufacturing decision support systems (DSSs) in which the links between customer orders and manufacturing operations were maintained throughout the production planning process. However, systems with this characteristic are not available at prices that most SMEs can afford. This paper describes how a prototype DSS with this feature was developed and validated using data from a manufacturer of fencing materials. A hybrid relational database/object-oriented approach to modelling the manufacturing process is outlined. Static and slowly changing data about the manufacturing system were stored on a relational database, while more dynamic production planning information was built into an object model. The system made use of a 'Bill of Production' for each manufactured item that contained both materials and operation information, and was constructed at the time it was required from information held on the database.     

Integration and application of feature recognition and mould manufacturing planning navigating process

Plastic injection mould manufacturing is diverse and complex. Traditional mould manufacturing planning often relied on experience and techniques. Therefore, in order not to affect the operation or even cause some loss of the enterprises, geometry information of computer-aided design (CAD) should be converted into manufacturing information of computer-aided process planning (CAPP) and CAM for automatic feature recognition to shorten the lead time. This study is the secondary development in a CAD environment creating a network plastic injection mould manufacturing navigating system and CAD/CAPP for integration and application. As part design is feature-based, each processing step can be regarded as a feature. By applying the hybrid recognition technology of the graph-based approach, the rule-based approach and hint-based approach in the analysis of part feature appearances, it can automatically categorise all the manufacturing features of the part and convert them into corresponding processes. The CAPP developed in this study can convert the part manufacturing features into corresponding processes and plan the part process sequence according to the process priority constraint relations. As proved by the case studies, the use of CAPP in this study can reduce planning time by 87%. It can provide users with a reference regarding process sequence, and speed up the part planning progress. Coupled with the mould manufacturing navigating process, it can achieve automatic design and manufacturing.     

Product platforms design,selection and customisation in high-variety manufacturing

Product platforms represent an effective strategy implemented by manufacturers to cope with dynamic market demands, decrease lead-time and delay products differentiation. A decision support system (DSS) for product platforms design and selection in high-variety manufacturing is presented. It applies median-joining phylogenetic networks (MJPN) for the platforms design and phylogenetic tree decomposition for platforms selection by determining the product family phylogenetic network and defines the platforms at various levels of assembly corresponding to different trade-offs between number of platforms (variety) and number of assembly/disassembly tasks (customisation effort). Product platforms are reconfigured and customised to derive final product variants. The phylogenetic tree is decomposed in multiple levels, from the native platforms to the final variants. New Platforms Reconfiguration Index (PRI) and Platforms Customisation Index (PCI) were developed as metrics to evaluate the platforms customisation effort. A case study of a large family of plastic valves is used to demonstrate the DSS application. It shows reduction of 60% in platforms variety and increases in platform customisation assembly/disassembly tasks by only 20% leading to significant production and inventory efficiencies and cost savings. This methodology supports companies in the design and selection of best product platforms for high-variety to reduce cost and delivery time.     

Improving the performances of part dispatching based on multiple process plans using graph theory

An intrinsic characteristic for manufacturing a part is the existence of diverse processing routes. In this paper, we deal with the improvement of part dispatching performance based on the availability of multiple process plans of a machined part. Procedures to represent multiple process plans with a graphic representation are developed so as to facilitate the utilization of process plans. We propose a simulation model to test the impacts of multiple process plans on the performances of real time scheduling. The study shows that by applying the simulation model to multiple processes in the manufacturing of machined parts, the flexibility of the manufacturing system can be increased; the total production flow time can be reduced; and the efficient utilization of production resources is improved.     

Automated manufacturability assessment of rotational parts by grinding

Automated manufacturability assessment of a given design is a key requirement in realizing complete integration of design (CAD) and manufacturing (CAM). The paper deals with a system developed for automated manufacturability assessment by machining processes. The purpose of this system is to assist designers in their effort to come up with manufacturable parts economising in terms of cost and time. Unlike most of the work done in the past, which concentrates on assessment by primary machining processes such as turning and milling, the present work deals with grinding operation. The present system uses geometric reasoning to extract manufacturing specific information from a part model. It uses a knowledge base consisting of grinding process knowledge and control rules, which are activated by the designer. It also has a provision to account for company-specific manufacturing resources to come up with meaningful assessment for part manufacturability. The use of the proposed system is demonstrated for the assessment of axisymmetric parts to be manufactured by cylindrical and internal grinding processes.     

Fuzzy logic based leanness assessment and its decision support system

Manufacturing organisations have been witnessing a transition from mass manufacturing to lean manufacturing. Lean manufacturing is focused on the elimination of obvious wastes occurring in the manufacturing process, thereby enabling cost reduction. The quantification of leanness is one of the contemporary research agendas of lean manufacturing. This paper reports a study which is carried out to assess the leanness level of a manufacturing organisation. During this research study, a leanness measurement model has been designed. Then the leanness index has been computed. Since the manual computation is time consuming and error-prone, a computerised decision support system has been developed. This decision support system has been designated as FLBLA-DSS (decision support system for fuzzy logic based leanness assessment). FLBLA-DSS computes the fuzzy leanness index, Euclidean distance and identifies the weaker areas which need improvement. The developed DSS has been test implemented in an Indian modular switches manufacturing organisation.     

Robust design of composites manufacturing processes with process simulation and optimisation methods

Due to the increasing variations in raw materials and manufacturing processes, composite manufacturing processes have more part-to-part variations compared with the metal manufacturing processes. To improve part quality consistency, tooling design optimisation is an imperative step for addressing the stochastic behaviour of composite manufacturing processes. This paper presents an optimisation approach for the typical composite manufacturing technique of resin transfer moulding (RTM), which minimises the sensitivity of the mould design to uncertain material properties by choosing appropriate locations of injection gates and vents. This paper proposes a stochastic simulation based approach for the RTM processes. Normal distribution and Weibull distribution were utilised as the statistical models for representing the permeability values for the main region and race-tracking, respectively. Based on the statistical properties of the permeability, a graph-based two-phase heuristic (GTPH) was adopted to minimise the flow dispersion value (a quantitative measure for part quality consistency) such that the process design is not sensitive to the material and process parameter variations.     

CAE FOR THE MANUFACTURING ENGINEER: THE ROLE OF PROCESS SIMULATION IN CONCURRENT ENGINEERING

As America refocuses its attention on the factory, design and manufacturing engineers must work together closely to design the appropriate products, and matching production process in a team effort. By building off the designer's CAE tools that predict product performance, the manufacturing engineer is today able to simulate the proposed production process. Process simulations for the following manufacturing processes are available or being developed:

▪Forging, ▪Machining, ▪Injection Molding, ▪Die Casting, ▪Investment Casting, ▪Metal Forming, ▪Heat Treating, ▪Assembly Tolerancing

By utilizing the same 3-D solid model and finite element modeling tools used by the designer, coupled to powerful analysis simulation tools to predict the transient nonlinear heat transfer and plastic material flow found in many manufacturing processes, the manufacturing engineer is able to explore alternative processing plans, evaluate trade-offs and even influence the design to produce superior products.

Process simulation brings a science to support the manufacturing engineers experience for reduced lead time, lower cost, increase product quality and better understanding of the process. The next step will be to directly link the process simulation to an expert system.

This paper describes the current state of technology in the area of manufacturing process computer simulation for a number of manufacturing operations and suggests how these tools can be used “up-front” and lead to concurrent engineering.     

Post occupancy evaluation in New Zealand

The paper describes the development of a POE (post occupancy evaluation) programme for evaluating government buildings in New Zealand. The programme is being developed by a research team at the School of Architecture, VUW (Victoria University of Wellington), under a research contract from the New Zealand MWD (Ministry of Works and Development). The aim of the programme is to enable MWD and its client departments to assess the effectiveness of their buildings and of their delivery, operations and maintenance processes as part of their inhouse activities.The MWD regard the programme as a long term commitment. The VUW research team has therefore concerned itself with developing a flexible programme incorporating a robust evaluation process which can be used by non-expert personnel (in terms of POE experience) on a wide range of buildings. Such an evaluation process is now proposed and its initial testing is complete. This paper outlines that process and discusses the current status of the POE programme.     

A neural network model-based open-loop optimization for the automated thermoplastic composite tow-placement system

This study demonstrates the use of on-line optimization algorithms to calculate optimum process set points for manufacturing processes such as the automated thermoplastic tow-placement (ATP) system. An in situ non-linear optimization technique based on artificial neural networks has been developed. This method is implemented in the ATP process and utilizes neural network-based process models to predict material quality as a function of process set points. The set points are computed by maximizing the throughput and maintaining a desired minimum quality. Process history can greatly affect the final part quality and, therefore, is an integral part of the optimization. The controller is validated for the highly non-linear ATP process and successfully predicts optimum processing parameters. The developed approach is applicable to many other manufacturing processes where process simulations exist and conventional control techniques are lacking.     

Cost estimates to guide pre-selection of processes

A process is a method of shaping, joining or surface-treating a material. Process selection has three steps. The first is to identify from the ‘menu’ of all available processes, the subset, which can give a chosen material the desired shape with the desired detail, precision and finish. The second is to choose, from among these, the ones that will do so at the lowest cost. The final step is to investigate the most promising processes in depth, exploring considerations such as availability, in-house experience, safety and environmental issues. The first two steps can be thought of as process pre-selection. Components have to be assembled and finished to create products. Here too, the ability to rank by cost, however crudely, helps guide pre-selection. Cost models are reviewed from the perspective of material and process selection. An approximate model is useful provided it has generality—that is, it must allow comparison of very different processes. Many approaches fail in this. One that works, based on resource consumption, is developed here and its use for selection is illustrated. It has been implemented as part of a tool that allows rapid pre-selection from a database of 112 processes.     

Manufacturing knowledge verification in design support systems

This paper identifies the need for a verification methodology for manufacturing knowledge in design support systems; and proposes a suitable methodology based on the concept of ontological commitment and the PSL ontology (ISO/CD18629). The use of the verification procedures within an overall system development methodology is examined, and an understanding of how various categories of manufacturing knowledge (typical to design support systems) map onto the PSL ontology is developed. This work is also supported by case study material from industrial situations, including the casting and machining of metallic components. The PSL ontology was found to support the verification of most categories of manufacturing knowledge, and was shown to be particularly suited to process planning representations. Additional concepts and verification procedures were however needed to verify relationships between products and manufacturing processes. Suitable representational concepts and verification procedures were therefore developed, and integrated into the proposed knowledge verification methodology.     

General run-to-run (R2R) control framework using self-tuning control for multiple-input multiple-output (MIMO) processes

During recent years, run-to-run (R2R) control techniques have been developed and used to control various semiconductor manufacturing processes. The R2R control methodology combines response surface modelling, engineering process control, and statistical process control. The main objective of such control is to manipulate the recipe to maintain the process output of each run as close to the nominal target as possible. The primary focus of this research is on the multiple-input multiple-output self-tuning control of R2R processes. A general control scheme is presented that can compensate for a variety of noise disturbances frequently encountered in semiconductor manufacturing. The controller can also compensate for various system dynamics, including autocorrelated responses, deterministic drifts, and varying process gains and offsets. Self-tuning controllers are developed to provide on-line parameter estimation and control. A recursive least squares algorithm is normally used to provide on-line parameter estimation to the controller. This type of control strategy used in the proposed self-tuning controller applies the principle of minimizing total cost (in the form of an expected off-target and controllable factors adjustment) to obtain a recipe for the next run. It is shown through the simulation study that even if the control model is non-linear, the self-tuning controller offers satisfactory control performance for R2R applications as compared with those of the control actions provided by the optimizing adaptive quality controller module. At last, a relevant application to chemical mechanical planarization in semiconductor manufacturing, a critical fabrication step involving two quality characteristics (removal rate and within-wafer non-uniformity), is used to illustrate the proposed controller. In this case study, a multivariate statistical process control technique via the Hotelling T?² statistic is also used as a dead-band for further investigation.     

The analysis of feature-based measurement error in coordinate metrology

Coordinate measurement systems (CMSs) dominate the dimensional control and diagnostics of various manufacturing processes. However, CMSs have inherent errors caused by the lack of a tracing ability for some of the measured part features. This is important for product inspection and process variation reduction in a number of automated manufacturing systems, such as for example the automotive body assembly process. The lack of a feature tracing ability means that instead of measuring a given feature, the CMS may actually measure the area around the selected feature. In this paper, a principle for the part feature tracing ability and the resultant feature-based measurement error analysis are developed to estimate the aforementioned deficiencies in the CMSs. The impact of feature type and part(s) positional variation on the feature-based measurement error is explored. The proposed approach is applicable to both contact and non-contact CMSs including both mechanical and optical coordinate measuring machines An analysis of the error for different measurement algorithms is presented. We show that the developed feature-based measurement error can have a significant impact on the measurement accuracy and hence on process control and the diagnostic algorithms currently used in manufacturing. A feature-based error map and error compensation approach are also developed and presented. Simulations, experimental results and two industrial case studies illustrate the proposed method.     

A stochastic model for the optimal batch size in multi-step operations with process and product variability

Virtually all manufacturing processes are subject to variability, an inherent characteristic of most production processes. No two parts can ever be exactly the same in terms of their dimensions. For machining processes such as drilling, milling, and lathing, overall variability is caused in part by machine tools, tooling, fixtures and workpiece material. Since variability, which can be accumulated from tolerance stacking, can result in defective parts the number of parts produced in a batch is limited. When there are too many parts in a batch, the likelihood of producing all acceptable parts in a batch decreases due to the increased tolerances. On the other hand, too small a batch size incurs an increase of manufacturing costs due to frequent setups and tool replacements, whereas the likelihood of acceptable parts increases. To address this challenge, we present a stochastic model for determining the optimal batch size where we consider part-to-part variation in terms of tool wear, which tends to be proportional to batch size. In this paper, a mathematical model is constructed based on the assumption that the process used for producing preceding parts affects the state of subsequent parts in a probabilistic manner.     

Rapid tooling route selection and evaluation for sand and investment casting

Today, a number of direct routes using rapid prototyping (RP) processes (fused deposition modelling (FDM), laminated object manufacturing (LOM), stereolithography apparatus (SLA), selective laser sintering (SLS), etc.), as well as indirect routes (RP coupled with secondary or soft-tooling processes like RTV vacuum casting) are available for rapid fabrication of tooling for sand and investment casting processes. Each route is unique in terms of geometric, material, quality and cost characteristics; no comprehensive database of their capabilities has been reported, especially for metal casting applications. The problem of selecting the optimal rapid tooling (RT) route is a complex multi-criteria decision-making problem. This paper describes a systematic approach for RT route selection and planning. A database of RT process capabilities was generated through benchmarking experiments, covering 20 different widely used RT routes (both direct and indirect: two- and three-step processes) involving an impeller pattern. In this approach, RT process route selection involves translating the tooling requirements specified by the casting engineer into weighted tooling attributes using quality function deployment and analytic network process (QFD-ANP), which along with part specifications is used for RT route selection by calculating the overall process compatibility indices. The routes are ranked as per the value of the overall compatibility index. Once the optimal route is selected, process planning is carried out by retrieving a similar process plan using case-based reasoning (CBR). The methodology has been implemented in a software program using Visual C + + programming language in a Windows environment. The methodology is demonstrated and validated with an industrial example of a separator body casting. It has proved to be a robust evaluation and decision-making tool for selecting appropriate tooling route for a given casting based on customer requirements.     

An integrated decision support system for strategic supply chain optimisation in process industries: the case of a zinc company

We introduce a menu-driven user-friendly decision support system (DSS) for supply chain planning based on optimisation. The DSS is based on a multi-source (supplier), multi-destination (warehouse) network having multiple manufacturing facilities, with multiple materials and multiple storage areas. This integrated supply chain model performs multiple period planning. The use of this DSS requires little knowledge of management sciences tools. We discuss the need for an integrated approach towards supply chain modelling for the process industry. We present the integrated model in the form of a database structure. We validate the model with the real data of a zinc company and demonstrate the impact of optimisation in terms of percentage improvement. The result shows that it is possible to improve unit contribution to profit from 1.89 to 4.66%.     

Methods for the capture of manufacture best practice in product lifecycle management

The capture of manufacturing best practice knowledge in product lifecycle management systems has significant potential to improve the quality of design decisions and minimise manufacturing problems during new product development. However, providing a reusable source of manufacturing best practice is difficult due to the complexity of the viewpoint relationships between products and the manufacturing processes and resources used to produce them. This paper discusses how best to organise manufacturing best practice knowledge, the relationships between elements of this knowledge plus their relationship to product information. The paper also explores the application of UML-2 as a system design tool which can model these relationships and hence support the reuse of system design models over time. The paper identifies a set of part family and feature libraries and, most significantly, the relationships between them, as a means of capturing best practice manufacturing knowledge and illustrates how these can be linked to manufacturing resource models and product information. Design for manufacture and machining best practice views are used in the paper to illustrate the concepts developed. An experimental knowledge based system has been developed and results generated using a power transmission shaft example.