A genetic algorithm for the optimisation of assembly sequences

This paper describes a Genetic Algorithm (GA) designed to optimise the Assembly Sequence Planning Problem (ASPP), an extremely diverse, large scale and highly constrained combinatorial problem. The modelling of the ASPP problem, which has to be able to encode any industrial-size product with realistic constraints, and the GA have been designed to accommodate any type of assembly plan and component. A number of specific modelling issues necessary for understanding the manner in which the algorithm works and how it relates to real-life problems, are succinctly presented, as they have to be taken into account/adapted/solved prior to Solving and Optimising (S/O) the problem. The GA has a classical structure but modified genetic operators, to avoid the combinatorial explosion. It works only with feasible assembly sequences and has the ability to search the entire solution space of full-scale, unabridged problems of industrial size. A case study illustrates the application of the proposed GA for a 25-components product.     

Optimization of oval–round pass design using genetic algorithm

The primary purpose of this paper is to propose a computer aided optimal design system to support a generalized oval–round pass design, which is widely used as both intermediate and final passes in the process of rod rolling. This system, which is based on a hybrid model and the genetic algorithm, is developed to improve the efficiency, to reduce the manufacturing errors, as well as to extend the useful life of rolls through uniform wear design. Generalized parametric equations are established for geometrical modeling, graphic plotting of oval–round passes, as well as calculation of the cross section area, contact area and the lengths of contact arcs along the cross section of round groove in the MATLAB programming environment. Moreover, these equations can also realize the parametric transformation between roll profile and mathematical models for the oval–round pass design and optimization. The genetic algorithm is employed for the optimal design of oval–round passes in this paper. The objective functions are formulated for minimization of power consumption in the rolling process, variances between ideal dimensions and design dimensions, as well as variances between the lengths of contact arcs. To reduce the complexity and computational burden of the system, some reliable empirical formulas for the calculations of contact area and contact arc length are applied. Finally, the proposed approach is applied to an oval–round pass design. Through simulation and comparison of results against experimental data acquired from literature, it is found that this system is reliable, effective and easier to use.     

Development of energy-saving optimization for the oval-edging oval pass design using genetic algorithm

In the multi-pass rod rolling systems, where the final products are round bars with different sizes, oval-edging oval passes are widely employed as intermediate passes. This study attempts to present a generalized strategy to support the optimal design of oval-edging oval passes. A design system based on this strategy is developed to minimize power consumption, to reduce trials and errors in industry, as well as to improve the design efficiency and flexibility. A series of equations are established for geometrical modeling and graphic plotting of oval-edging oval passes, which can realize the parametric transformation for roll pass design and optimization. A novel formula based on statistical analysis and data fitting is proposed for the calculation of power consumption. To verify this formula, results obtained are compared with those calculated from using the Smirnov’s formula. The comparison confirmed the accuracy and efficiency of this formula. Finally, the proposed approach is applied for the optimal design of oval-edging oval passes, which are widely employed for the intermediate passes in the rod rolling systems. Through the comparison between simulation results and experimental data from literature, it is concluded that the proposed method can reduce power consumption of these passes and is able to improve the efficiency and flexibility of intermediate pass design without compromising its accuracy.     

Knowledge-based tool for planning of enterprise resources in ASEAN SMEs

Manufacturing has been identified as a key pillar of growth in many Southeast Asian (ASEAN) economies. However, in the last decade many countries have become keen competitors for foreign direct investments. Many countries are trying to improve their total business capabilities by encouraging computerisation of small and medium sized enterprises (SME). Manufacturing SMEs (M-SMEs) are tasked to adopt technologically advanced programmes. With an improving public education system and more literate work force, more SMEs are better positioned to tap into the knowledge-based economy. There is tremendous amount of knowledge intensive activities within the multi-flows of the M-SMEs.Although the concept of ERP systems and artificial intelligence (AI) techniques have been around for more than two decades, this has largely remained the domain of the larger companies. ASEAN M-SMEs have been slow to implement it. In this paper, the various strategic and operational requirements of regional M-SMEs are presented and a knowledge-based resources planning model making use of AI techniques is proposed. This improved AI model makes use of the large amount of accumulated knowledge typically found in the M-SMEs, especially those in the electronics and precision engineering sectors. This includes a case study of how an electronics precision engineering company adopted the proposed AI model.     

Gene expression programming-based viscosity-mixing rule for asphalt blends

Mixing rule plays a significant role in the prediction of resultant viscosity of asphalt/oil blends. This work presents a novel gene expression programming (GEP)-based approach to obtain a viscosity-mixing rule. To develop these expressions, two distinct binary blends comprising of varying proportion of unmodified binder and polymer modified binder were prepared and tested for their resultant viscosity at different temperatures. The obtained data were used to (i) develop the GEP-based viscosity-mixing rule and (ii) calculate resultant viscosity with viscosity-mixing rules reported in the literature (like the Arrhenius model). Statistical analysis indicated that the accuracy of GEP-based mixing rule is superior over other viscosity-mixing rules reported in the literature.     

Simulation modelling and analysis of scheduling in robotic flexible assembly cells using Taguchi method

Due to increasing competition in the developing global economy, today’s companies are facing greater challenges than ever to employ flexible manufacturing systems (FMS) capable of dealing with unexpected events and meeting customers’ requirements. One such system is robotic flexible assembly cells (RFACs). There has been relatively little work on the scheduling of RFACs, even though overall scheduling problems of FMS have attracted significant attention. This paper presents Taguchi optimisation method in conjunction with simulation modelling in a new application for dynamic scheduling problems in RFACs, in order to minimise total tardiness and number of tardy jobs (N_T). This is the first study to address these particular problems. In this study, Taguchi method has been used to reduce the minimum number of experiments required for scheduling RFACs. These experiments are based on an L9 orthogonal array with each trial implemented under different levels of scheduling factors. Four factors are considered simultaneously: sequencing rule, dispatching rule, cell utilisation and due date tightness. The experimental results are analysed using an analysis of mean to find the best combination of scheduling factors and an analysis of variance to determine the most significant factors that influence the system’s performance. The resulting analysis shows that this proposed methodology enhances the system’s scheduling policy.     

Towards Energy Efficient Shape Rolling:Roll Pass Optimal Design and Case Studies

Shape rolling is widely employed in the production of long workpieces with appropriate cross-section profiles for other industrial applications. In the development of shape rolling systems, roll pass design(RPD) plays an essential role on the quality control of products, service life of rolls, productivity of rolling systems, as well as energy consumption of rolling operations. This study attempts to establish a generic strategy based on hybrid modeling and an improved genetic algorithm, to support the optimizations of RPD and shape rolling operations at a systematic perspective.Objectives include improving the quality and e ciency of RPD, reducing energy consumption of shape rolling, as well as releasing the demands on costly trails and expert knowledge in RPD. Hybrid modeling based on cross-disciplinary knowledge is developed to overcome the limitations of isolated single-disciplinary models. And conventional genetic algorithm is improved for the implementation of optimal design. Targeting to integrate empirical data and published reliable solutions into optimizations, a parameters estimation method is proposed to transfer the initially misaligned models into a uniform pattern. A tool based on the Matlab platform is developed to demonstrate the optimal design operations, with case studies involved to validate the proposed methodology.     

Prediction of density and viscosity of bitumen

Abstract

It is well known fact that temperature and pressure significantly affects density and viscosity of bitumen. The present work utilizes Gene Expression Programming (GEP) approach to develop models to predict density and viscosity of bitumen. To evaluate the accuracy of proposed GEP based models, results reported by various researchers were utilized. This includes test results regarding Athabasca, Cold Lake and Gas free bitumen. The developed GEP based models were compared with the conventional empirical regression equations. The statistical analysis indicates that GEP based models work better than other existing models for density and viscosity of bitumen.     

A decision support system for cellular manufacturing system design

With the growth of competitive pressure in the global markets, there has been an increase in demand in industry for cellular manufacturing systems (CMSs) in order to improve productivity and process flexibility. The design of CMSs for industrial applications is a complex and knowledge intensive process as it involves the consideration of many factors including production data and process characteristics. This paper describes the development and implementation of a decision support system for the feasibility and conceptual design of CMSs. The system is based on the knowledge-based system approach, and is able to make recommendations of system feasibility, cell formation techniques and cell types. A case study is also presented to demonstrate the capability of the decision support system.