Automated tool sequence selection for 3-axis machining of free-form pockets

This paper describes an efficient method to find the lowest cost tool sequence for rough machining free-form pockets on a 3-axis milling machine. The free-form pocket is approximated to within a predefined tolerance of the desired surface using series of 2.5-D layers of varying thicknesses that can be efficiently removed with flat-end milling cutters. A graph-based method finds an optimal sequence of tools for rough machining the approximated pocket. The algorithm used here can be tuned to suit any available tool set and preferred cost models. The tool sequence that is obtained is near optimal, and may take into account tool wear, as well as various overhead costs of the machine shop.     

On setup level tool sequence selection for 2.5-D pocket machining

This paper describes algorithms for efficiently machining an entire setup. Previously, the author developed a graph based algorithm to find the optimal tool sequence for machining a single 2.5-axis pocket. This paper extends this algorithm for finding an efficient tool sequence to machine an entire setup. A setup consists of a set of features with precedence constraints, that are machined when the stock is clamped in a particular orientation. The precedence constraints between the features primarily result from nesting of some features within others. Four extensions to the basic graph algorithm are investigated in this research. The first method finds optimal tool sequences on a feature by feature basis. This is a local optimization method that does not consider inter feature tool-path interactions. The second method uses a composite graph for finding an efficient tool sequence for the entire setup. The constrained graph and subgraph approaches have been developed for situations where different features in the setup have distinct critical tools. It is found that the first two methods can produce erroneous results which can lead to machine crashes and incomplete machining. Illustrative examples have been generated for each method.     

A neural network based methodology for machining operations selection in Computer-Aided Process Planning for rotationally symmetrical parts

The relevant literature on machining operations selection in Computer-Aided Process Planning (CAPP) by decision trees, expert systems and neural networks has been reviewed, highlighting their contributions and shortcomings. This paper aims at contributing to the applicability of back-propagation neural network method for the selection of all possible operations for machining rotationally symmetrical components, by prestructuring the neural network with prior domain knowledge in the form of heuristic or thumb rules. It has been achieved by developing two forms of representation for the input data to the neural network. The external representation is used to enter the crisp values of the input decision variables (namely the feature type and its attributes such as diameter or width, tolerance and surface finish). The purpose of internal representation is to categorize the above crisp values into sets, which correspond to all the possible different ranges of the above input variables encountered in the antecedent ‘IF’ part of the thumb rules mentioned above. The input layer of the neural network has been designed in such a way that one neuronal node is allocated for each of the feature types and the sets of various feature attributes. In the output layer of the neural network, one neuronal node is allocated to each of the various feasible machining operation sequences found in the consequent ‘THEN’ part of the thumb rules. A systematic method for training of the neural network has been presented with the above thumb rules used to serve as guidelines for choosing the input patterns of the training examples. This method simplifies the process of training, reduces the time for preparation of training examples and hence the time to develop the overall process planning system. It can further help ensure that the entire problem domain is represented in a better manner and improve the quality of response of the neural network. The example of an industrially-relevant rotationally symmetrical workpiece has been analyzed using the proposed approach to demonstrate its potential for use in the real manufacturing environment. By this novel methodology, workpieces of complex shapes can be handled by investing a very limited amount of time, hence making it attractive and cost effective for industrial applications. Received: June 2005 / Accepted: January 2006     

The Domain of Admissible Orientation concept: A new method for five-axis tool path optimisation

This work deals with the optimisation of tool paths in five-axis machining. The objective is to improve the kinematic behaviour of machine tools during milling. The orientation of the tool axis at each point of a tool path is optimised while ensuring quality constraints. These are modelled using the Domain of Admissible Orientation (DAO) concept expressed in the P-System and transformed into the M-System. This article aims at defining the DAO and presents an example of optimisation using this concept. This optimisation is a minimisation of the movement generated by each rotation axis and is applied to two test parts.     

Automated inspection process planning: Algorithmic inspection feature recognition, and inspection case representation for CBR

General metrological inspection planning is among the least explored computer-aided process planning (CAPP) domains. This paper explores certain basic issues involved in inspection planning using case-based reasoning in an environment of a Generic CAPP Support System. Firstly, algorithmic methods for characterizing and extracting inspection features are proposed and discussed. A sequential knowledge based filtering method is developed to reduce the number of inspection features typically encountered in metrological inspection planning. Finally, a formalized approach for case representation of relevant inspection domain knowledge using a newly developed parametric-list technological feature graph (PLTFG) is presented.     

Recognition of machining features for cast then machined parts

Mechanical parts are typically manufactured using multiple manufacturing processes. Primary processes such as casting realize the primary shape of the part, while secondary processes such as machining generate more detailed shape of the part. This paper presents a feature recognition method to support machining process planning for cast-then-machined parts. From the part model including the specification of machined faces, we generate the starting workpiece for machining, which represents the casting output in sufficient detail to support machining process planning. The starting workpiece is generated by identifying faces to be made by casting followed by machining, then offsetting the part through these faces by a uniform machining thickness to obtain cast faces, and combining the halfspaces induced by machined faces and the halfspaces induced by their bounding cast faces to enclose removal volumes. Machining features are then recognized from the removal volumes using a volume decomposition method called Alternating Sum of Volumes with Partitioning.     

Automated Lead Discovery and Development in HTS Datasets

The analysis of biological screening results has traditionally been a labor intensive process. Scientists familiar with the biological data under investigation would visually inspect the results, evaluate the quality and promise of active data points and identify leads. The introduction and widespread use of high throughput screening systems has increased the size of biological datasets immensely thus pushing the traditional analysis method to its limits. In this presentation we describe a new automated approach that emulates the decision making process of human experts. This approach combines knowledge-based techniques with human expertise to enable rapid identification, characterization and prioritization of lead candidates.     

Workforce planning and technology installation optimisation for utilities

With the advent of government mandates to upgrade technologies which are owned by utilities but used by customers, utilities are pressurised into planning future workforce requirements. The smart meter rollout in Great Britain provides such an example. Previous models of workforce planning are numerous, but extensions for large-scale technology installation schemes have been paid less attention.This paper provides three contributions to the field: (i) a novel formulation of the aggregate planning model which accounts for learning rates of the workforce when executing a new task, (ii) an enhanced process of employing uncertainty analysis using Sobol sampling, and (iii) a numerical example supplying an illustrative instance of the modelling and uncertainty analysis in practice. The proposed modelling framework can be used as a tool to further the planning capabilities and strategic decision-making toolkits of the utility sector.     

Automated Nucleic Acid Purification for Large Samples

Advancements in the fields of genomic screening, molecular pathology and clinical research have resulted in a major increase in the demand for high quality DNA and RNA. This escalating demand has resulted in a sample preparation bottleneck and an emphasis on the development of new technologies to automate the purification process. Gentra has developed the AUTOPURE LS™ nucleic acid purification instrument, a platform capable of high-throughput sample purification from large samples, such as 10 mL whole blood. This article presents data showing the equivalency of DNA purified using manual and automated processing.     

Genetically optimised disassembly sequence for automotive component reuse

Environmental sustainability through end-of-life recovery has become the main items of contest in the automotive industries. Component reuse as one of the product recovery strategy is now gaining importance in view of its impact on the environment. Disassembly as one of the determinant factors for reuse is a very important and difficult process in life cycle engineering. To enable reuse, a certain level of disassembly of each component is necessary so that parts of the products that have arrived at their end-of life can be easily taken apart. Improvements to the disassembly process of products can be achieved at two levels: in the design phase, making choices that favours the ease of disassembly of the constructional system (design for disassembly) and planning at best and optimising the disassembly sequence (disassembly sequence planning). Hence, finding an optimal disassembly sequence is important to increase the reusability of the product. This paper presents the development work on an optimisation model for disassembly sequence using the genetic algorithms (GA) approach. GA is chosen to solve this optimisation model due to its capability in solving many large and complex optimisation problems compared with other heuristic methods. The fitness function of the GA in this study is dependent on the increment in disassembly time. Comparison of results using different combinatorial operators and tests with different probability factors are shown. This paper will present and discuss the disassembly sequence of an engine block, as a case example which achieves the minimum disassembly time.     

An adaptive and upgradable agent-based system for coordinated product development and manufacture

Last decade witnessed the growth of globally decentralized product development and manufacture. The complexity of products created in such a distributed environment often requires close collaborations among a number of design and production partners. In this paper, an agent-based system for coordinated product development and manufacture is presented. The system consists of two categories of agents. The first category consists solely of a managing agent (MA) and the second consists of many functional agents such as the manufacturability evaluation agent, resource agent, process-planning agent, scheduling agent, etc. Each agent represents a domain expert and it can be installed in an individual computer. All the agents could be dispersed in geographically different regions and communicate with one another through the Internet. With each functional agent having specific functionality, the MA is the center of the intelligent system. It assists the work of other agents and enables them to collaborate closely with one another. With such a system in place, a product life cycle can be optimized from product design to final manufacturing as all the procedures are considered comprehensively and integrally, and each procedure is performed in a way as to ease and expedite the work of other agents. An agent language, which includes many specifically defined performatives, is defined. Each agent can perform its task intelligently by interpreting the commands (performatives) from other agents. Moreover, with the consideration of possible future methodology changes, the internal structure of each functional agent is modularized into several components. Such an architecture ensures that the system is flexible, adaptive and upgradable.     

Design parameterization and tool integration for CAD-based mechanism optimization

This paper presents an open and integrated tool environment that enables engineers to effectively search, in a CAD solid model form, for a mechanism design with optimal kinematic and dynamic performance. In order to demonstrate the feasibility of such an environment, design parameterization that supports capturing design intents in product solid models must be available, and advanced modeling, simulation, and optimization technologies implemented in engineering software tools must be incorporated. In this paper, the design parameterization capabilities developed previously have been applied to support design optimization of engineering products, including a High Mobility Multi-purpose Wheeled Vehicle (HMMWV). In the proposed environment, Pro/ENGINEER and SolidWorks are supported for product model representation, DADS (Dynamic Analysis and Design System) is employed for dynamic simulation of mechanical systems including ground vehicles, and DOT (Design Optimization Tool) is included for a batch mode design optimization. In addition to the commercial tools, a number of software modules have been implemented to support the integration; e.g., interface modules for data retrieval, and model update modules for updating CAD and simulation models in accordance with design changes. Note that in this research, the overall finite difference method has been adopted to support design sensitivity analysis.     

Frequency weighting for the evaluation of steering wheel rotational vibration

The human perception of rotational hand–arm vibration has been investigated by means of a test rig consisting of a rigid frame, an electrodynamic shaker unit, a rigid steering wheel, a shaft assembly, bearings and an automobile seat. Fifteen subjects were tested while seated in a driving posture. Four equal sensation tests and one annoyance threshold test were performed using sinusoidal excitation at 18 frequencies in the range from 3 to 315 Hz. In order to guarantee the generality of the equal sensation data, the four tests were defined to permit checks of the possible influence of three factors: reference signal amplitude, psychophysical test procedure and temporary threshold shift caused by the test exposure. All equal sensation tests used a reference sinusoid of 63 Hz at either 1.0 or 1.5 m/s² r.m.s. in amplitude. The four equal sensation curves were similar in shape and suggested a decrease in human sensitivity to hand–arm rotational vibration with increasing frequency. The slopes of the equal sensation curves changed at transition points of approximately 6.3 and 63 Hz. A frequency weighting, called W_s, was developed for the purpose of evaluating steering wheel rotational vibration. The proposed W_s has a slope of 0 dB per octave over the frequency range from 3 to 6.3 Hz, a slope of −6 dB per octave from 6.3 to 50 Hz, a slope of 0 dB per octave from 50 to 160 Hz and a slope of −10 dB per octave from 160 to 315 Hz. W_s provides a possible alternative to the existing W_h frequency weighting defined in International Standards Organisation 5349-1 (2001) and British Standards Institution BS 6842 (1987).

Relevance to industry

For the manufacturers of tyres, steering systems and other vehicular components the proposed W_s frequency weighting provides a more accurate representation of human perception of steering wheel rotational vibration than the W_h weighting of ISO 5349-1 and BS6842.     

An Automated System for Processing Sample Vials Stored at Ultra-Low Temperatures

The BIOPHILE Individual Vial Retriever (IVR) system has been developed to provide automated access to vials stored at ultra-low temperatures. The IVR performs storage, retrieval, sorting, cataloging, volume estimation (weighing), barcode reading, and re-racking. All racking operations are performed in an environment designed to keep samples frozen at their optimal temperature. Operating temperatures are −80°C, −40°C, −20°C, and room temperature. Laboratory Information Management System (LIMS) integration, automation integration, chain of custody tracking, and FDA 21CFR Part 11 compliance are supported. This article introduces the IVR and provides information on its characteristics.     

Developing an Automated Enzyme Immunoassay for High-Throughput Screening of Bovine Serum Antibodies to Neospora Caninum

An enzyme-linked immunosorbent assay (ELISA) for Neospora caninum antibodies was automated with a robotic workstation, the Beckman Coulter Biomek 2000, to screen 200 bovine sera. Comparing these results with manually run ELISA data, a 95.92% agreement (K = 0.9592) between the two assays was obtained. The automated assay was specific and sensitive with excellent positive and negative predictive values. The results were repeatable and reproducible. The automation flexibility was high and the operation complexity was minimal. High-throughput screening (HTS) for bovine antibodies to Neospora caninum was achieved. The assay was developed according to the internationally recognized ISO17025 standard requirements.     

基于拟随机序列与克隆选择的进化V-detector 算法

阴性选择(NS)算法是人工免疫的核心方法,检测器生成是其关键。针对其经典V-detector算法中高维数据失效及随机生成初始检测器集过于集中而导致过早收敛等问题,首先采用拟随机序列生成初始检测器;然后通过克隆选择优化检测器集合,以覆盖非自体空间大小及数量作为亲和力标准,克服传统进化阴性选择(ENS)算法的局限性,并采用新型进化算子使得算法生成最优检测器集合;最后,通过实验验证了该方法的有效性。     

Ant colony optimization approach to a fuzzy goal programming model for a machine tool selection and operation allocation problem in an FMS

Due to the global competition in manufacturing environment, firms are forced to consider increasing the quality and responsiveness to customization, while decreasing costs. The evolution of flexible manufacturing systems (FMSs) offers great potential for increasing flexibility and changing the basis of competition by ensuring both cost effective and customized manufacturing at the same time. Some of the important planning problems that need realistic modelling and quicker solution especially in automated manufacturing systems have assumed greater significance in the recent past. The language used by the industrial workers is fuzzy in nature, which results in failure of the models considering deterministic situations. The situation in the real life shop floor demands to adopt fuzzy-based multi-objective goals to express the target set by the management. This paper presents a fuzzy goal programming approach to model the machine tool selection and operation allocation problem of FMS. An ant colony optimization (ACO)-based approach is applied to optimize the model and the results of the computational experiments are reported.     

A simple framework for real-time cryptographic protocol analysis with compositional proof rules

A real-time process algebra, enhanced with specific constructs for handling cryptographic primitives, is proposed to model cryptographic protocols in a simple way. We show that some security properties, such as authentication and secrecy, can be re-formulated in this timed setting. Moreover, we show that they can be seen as suitable instances of a general information flow-like scheme, called timed generalized non-deducibility on compositions (tGNDC), parametric w.r.t. the observational semantics of interest. We show that, when considering timed trace semantics, there exists a most powerful hostile environment (or enemy) that can try to compromise the protocol. Moreover, we present a couple of compositionality results for tGNDC, one of which is time dependent, and show their usefulness by means of a case study.