A. Y. C. Nee

Performance of an ant colony optimisation algorithm in dynamic job shop scheduling problems

The goal of the current study is to identify appropriate application domains of Ant Colony Optimisation (ACO) in the area of dynamic job shop scheduling problem. The algorithm is tested in a shop floor scenario with three levels of machine utilisations, three different processing time distributions, and three different performance measures for intermediate scheduling problems. The steady-state performances of ACO in terms of mean flow time, mean tardiness, total throughput on different experimental environments are compared with those from dispatching rules including first-in-first-out, shortest processing time, and minimum slack time. Two series of experiments are carried out to identify the best ACO strategy and the best performing dispatching rule. Those two approaches are thereafter compared with different variations of processing times. The experimental results show that ACO outperforms other approaches when the machine utilisation or the variation of processing times is not high.

Multi-User NC Machining Simulation over the WWW

The realisation of a virtual manufacturing system requires an open network environment to perform 3D graphics simulation or virtual reality information processing among multiple users. This paper introduces a prototype system that employs Java, the virtual reality modelling language (VRML) and the external authoring interface (EAI) to perform NC machining simulation. A client/server paradigm is adopted to construct a multi-user enabled interactive environment. A novel method is presented to update dynamically the geometry of a workpiece being cut in a VRML-based scene. In addition, the prototype system is analysed in terms of functionality and performance.

An intelligent fixture with a dynamic clamping scheme

Abstract Both proper fixture design and optimum fixturing execution are crucial to workpiece quality assurance in manufacturing. This paper deals with an integrated approach to fixturing problems and, in particular, a ‘live’ fixture with sensory feedback and on-line fixturing control strategy to perform an optimal fixturing operation. The framework of an integrated fixture design procedure is first presented. The functions and structure of an intelligent fixture are proposed. The prototype intelligent fixture with dynamic clamps capable of delivering accurate but varying clamping intensity is developed. This novel set-up has been proven to be effective for workpiece quality improvement and productivity enhancement through machining experiments on thin-walled workpieces.

A computational geometry approach to optimum clamping synthesis of machining fixtures

Automatic fixturing configuration is an important task to be addressed in manufacturing. An optimum clamping planning approach for arbitrarily shaped workpieces based on computational geometry of contacting wrenches is developed. A clamping analysis algorithm drawing on the metric of force closure is presented, in which all feasible clamping points are automatically found by examining whether the convex hull of bounding wrenches associated with constraining contacts contains the origin. Optimal clamping points are then chosen from the feasible clamping regions according to a proposed criterion based on the radius of the maximal inscribed hypersphere centred at the origin within the convex hull. The clamping sequence is in turn decided by the feasible clamping area where the clamps are situated. A clamping equilibrium criterion is also proposed so that robust clamping layout can be generated. Case studies are included to demonstrate the effectiveness and capabilities of the developed methodology.

An enhanced ant colony optimiser for multi-attribute partner selection in virtual enterprises

Increasing global competition drives enterprises, especially small and medium-sized enterprises, to collaborate in order to respond faster to customers’ needs, reduce operating costs, increase capacity, and produce customised products to reach the market quicker. A virtual enterprise (VE) is an important manufacturing paradigm to address this trend in the dynamic global economy. Partner selection is a key issue tightly coupled to the success of a VE coalition, and because of its complexity, it is considered a multi-attribute optimisation problem. In this paper, an enhanced ant colony optimiser (ACO) is proposed to address the partner selection problem. Five attributes (namely, cost, time, quality, reputation, and risk) considering both qualitative and quantitative aspects have been investigated to evaluate the candidate partners. Experiments have been conducted to validate the performance of the enhanced ACO algorithm, and the results show that the enhanced ACO algorithm can produce better results in terms of search accuracy and computing time.

A simulation-based genetic algorithm approach for remanufacturing process planning and scheduling

Graphical abstractDisplay Omitted HighlightsWe consider integrated process planning and scheduling for remanufacturing.Two potentially conflicting objective functions are considered simultaneously.A simulation-based genetic algorithm approach is developed.Key parameters of the algorithm have been fine-tuned.Extensive computational experiments and evaluations have been performed. Remanufacturing has attracted growing attention in recent years because of its energy-saving and emission-reduction potential. Process planning and scheduling play important roles in the organization of remanufacturing activities and directly affect the overall performance of a remanufacturing system. However, the existing research on remanufacturing process planning and scheduling is very limited due to the difficulty and complexity brought about by various uncertainties in remanufacturing processes. We address the problem by adopting a simulation-based optimization framework. In the proposed genetic algorithm, a solution represents the selected process routes for the jobs to be remanufactured, and the quality of a solution is evaluated through Monte Carlo simulation, in which a production schedule is generated following the specified process routes. The studied problem includes two objective functions to be optimized simultaneously (one concerned with process planning and the other concerned with scheduling), and therefore, Pareto-based optimization principles are applied. The proposed solution approach is comprehensively tested and is shown to outperform a standard multi-objective optimization algorithm.

Multi-modal augmented-reality assembly guidance based on bare-hand interface

One of the most beneficial applications of Augmented Reality (AR) technology is in the traditional manual assembly domain in manufacturing. However, in order to improve the usefulness and effectiveness of the state-of-the-art AR assembly guidance systems, it is imperative to integrate human cognition support to deliver the most appropriate modality and amount of information so that the users can receive and process it effortlessly. In this paper, a novel human Cognition-based interactive Augmented Reality Assembly Guidance System (CARAGS) is proposed to investigate how AR can provide various modalities of guidance to assembly operators for different phases of user cognition process during assembly tasks. An intuitively enhanced bare-hand interface (EBHI) is integrated to facilitate the interaction between the user and the rendered contents. In order to evaluate the benefits of CARAGS for assembly operators, the proposed system is benchmarked against two baseline conditions, namely, a LCD screen-based digital documentation system and a traditional AR assembly guidance system. In addition, a qualitative evaluation of the system performance is performed in terms of intuitiveness, ease of use, and satisfaction of the proposed system.

Integrated product design and assembly planning in an augmented reality environment

Purpose – The purpose of this paper is to present a methodology that integrates design and assembly planning in an augmented reality (AR) environment. Intuitive bare-hand interactions (BHIs) and a combination of virtual and real objects are used to perform design and assembly tasks. Ergonomics and other assembly factors are analysed during assembly evaluation. Design/methodology/approach – An AR design and assembly (ARDnA) system has been developed to implement the proposed methodology. For design generation, 3D models are created and combined together like building blocks, taking into account the product assembly in the early design stage. Detailed design can be performed on the components and manual assembly process is simulated to evaluate the assembly design. Findings – A case study of the design and assembly of a toy car is conducted to demonstrate the application of the methodology and system. Research limitations/implications – The system allows the users to consider the assembly of a product when ...

Assembly planning and evaluation in an augmented reality environment

Assembly planning is very critical in the product design process. Previously, computer-assisted assembly planning is mostly conducted in either an automated or interactive manner. Automated approaches can only be applied to products with simple component configurations, whereas extensive user input in the tedious form of answering questions is required for interactive approaches. Both kinds of approaches do not address issues related to human factors and ergonomics, which cannot be ignored in assembly planning especially in manual assembly. Using augmented reality technologies, an intuitive and efficient approach is proposed for manual assembly planning and evaluation by combining the potential strengths of the two kinds of approach. An AR-assisted assembly planning and evaluation system has been developed to implement the proposed methodology with the use of a 3D bare-hand interaction (3DBHI) tool. After loading all the parts of an assembly into the proposed system, assembly modelling is first completed through assembly geometrical constraint recognition and assembly location refinement with the use of the proposed 3DBHI tool. By analysing the disassembly process, precedence constraints are captured and used to search for feasible assembly sequences from all possible sequences, which are generated using an existing method. Finally, practical or good sequences are selected from the feasible sequences by exploiting an assembly index based on objective evaluation. A case study is conducted to demonstrate the application of the methodology and system.

Automated synthesis of modular fixture designs using an evolutionary search algorithm

Fixture design is a complex problem that requires a designer to ensure that a workpiece is located deterministically, totally restrained and sufficiently supported during a manufacturing process. The use of modular fixtures, while presenting an opportunity to improve the responsiveness of a manufacturing system, adds to the complexity of the fixture design problem. The complexity is a result of the large number of fixture elements in a modular fixture system and the constraints of specified locations in which fixture elements can be placed in a grid-based modular system. This paper presents an evolutionary search algorithm that aids a fixture designer by exploring the large number of possible fixture designs and suggesting an appropriate one. The algorithm can explore the large solution space using a flexible and generic representation and it considers fixture layout and fixture configuration constraints concurrently in arriving at appropriate solutions. The initial results of the algorithm are promising.