科学试验系统界面布局设计

目的研究科学试验系统界面布局设计方法,提升科研技术人员交互效率和工作有效性。方法通过分析科学试验系统界面的交互任务,提取了明确的设计需求,以某精密科学试验系统界面为案例,根据视觉认知规律和交互操作习惯,采用眼动记录仪试验数据进行界面布局设计。结果提出了一套涵盖科学试验系统界面结构设计、界面模式设计和单一界面中各模块单元布局设计的方法流程。结论使用该布局方法得到界面布局方案,通过对设计方案用户满意度评估验证了该方法的有效性。     

A simulated annealing algorithm based on a closed loop layout for facility layout design in flexible manufacturing systems

Facility layout design problems in flexible manufacturing systems (FMS) differ from traditional facility design problems and are more difficult to solve because there are more constraints that must be considered (i.e., cell shape, cell orientation, pick-up and drop-off point positions). The focus of this paper is on the closed loop type layout, which is based on a predetermined layout pattern. This layout pattern is commonly found in manufacturing settings since it requires a simplified material handling system configuration and since it facilitates a modular and expandable layout structure. The open-field type layout problem, where there is no predetermined layout pattern, may potentially have a more efficient configuration, since there are fewer restrictions. However, this problem is more difficult to solve and may result in configurations that are not desirable due to the lack of structure or modularity. The procedure developed in this paper improves the efficiency of the closed loop configuration by changing the rectangular shape of the loop to different sizes. In many cases, the resulting closed loop layout proves to be as efficient as the open field layout. A simulated annealing procedure (SA-CL) is used to search for the configuration that minimizes the total material handling costs. A comparison of the results with existing methods indicates that, based on solution quality and computational time, the SA-CL offers a favourable alternative for efficient layout design.     

An application of an expert system in layout compaction of VLSI design

Abstract

Recent research in knowledge‐based expert systems of VLSI design tools has concentrated on placement, routing, and cell generation. This paper presents an alternative application for artificial intelligence (AI) techniques on compaction design for a VLSI mask layout‐expert compactor. In order to overcome the shortcomings of iterative search through a large problem space within a working memory, and therefore, to speed‐up the runtime of compaction, a set of rule‐based region query operations and knowledge‐based techniques for the plane sweep method are proposed in this system. Experimental results have explored the possibility of using expert system technology (EST) to automate the compaction process by “reasoning” out the layout design and applying sophisticated expert rules to its knowledge base.     

The constraints satisfaction problem approach in the design of an architectural functional layout

A design support system with a new strategy for finding the optimal functional configurations of rooms for architectural layouts is presented. A set of configurations satisfying given constraints is generated and ranked according to multiple objectives. The method can be applied to problems in architectural practice, urban or graphic design—wherever allocation of related geometrical elements of known shape is optimized. Although the methodology is shown using simplified examples—a single story residential building with two apartments each having two rooms—the results resemble realistic functional layouts. One example of a practical size problem of a layout of three apartments with a total of 20 rooms is demonstrated, where the generated solution can be used as a base for a realistic architectural blueprint. The discretization of design space is discussed, followed by application of a backtrack search algorithm used for generating a set of potentially ‘good’ room configurations. Next the solutions are classified by a machine learning method (FFN) as ‘proper’ or ‘improper’ according to the internal communication criteria. Examples of interactive ranking of the ‘proper’ configurations according to multiple criteria and choosing ‘the best’ ones are presented. The proposed framework is general and universal—the criteria, parameters and weights can be individually defined by a user and the search algorithm can be adjusted to a specific problem.     

A Closer Look at the Design of Cutterheads for Hard Rock Tunnel-Boring Machines

The success of a tunnel-boring machine (TBM) in a given project depends on the functionality of all components of the system, from the cutters to the backup system, and on the entire rolling stock. However, no part of the machine plays a more crucial role in the efficient operation of the machine than its cutterhead. The design of the cutterhead impacts the efficiency of cutting, the balance of the head, the life of the cutters, the maintenance of the main bearing/gearbox, and the effectiveness of the mucking along with its effects on the wear of the face and gage cutters/muck buckets. Overall, cutterhead design heavily impacts the rate of penetration (ROP), rate of machine utilization (U), and daily advance rate (AR). Although there has been some discussion in commonly available publications regarding disk cutters, cutting forces, and some design features of the head, there is limited literature on this subject because the design of cutterheads is mainly handled by machine manufacturers. Most of the design process involves proprietary algorithms by the manufacturers, and despite recent attention on the subject, the design of rock TBMs has been somewhat of a mystery to most end-users. This paper is an attempt to demystify the basic concepts in design. Although it may not be sufficient for a full-fledged design by the readers, this paper allows engineers and contractors to understand the thought process in the design steps, what to look for in a proper design, and the implications of the head design on machine operation and life cycle.     

Self-assembled liquid crystalline nanoparticles as an ophthalmic drug delivery system. Part II: optimization of formulation variables using experimental design

In the field of keratoconus treatment, a lipid-based liquid crystal nanoparticles system has been developed to improve the preocular retention and ocular bioavailability of riboflavin, a water-soluble drug. The formulation of this ophthalmic drug delivery system was optimized by a simplex lattice experimental design. The delivery system is composed of three main components that are mono acyl glycerol (monoolein), poloxamer 407 and water and two secondary components that are riboflavin and glycerol (added to adjust the osmotic pressure). The amounts of these three main components were selected as the factors to systematically optimize the dependent variables that are the encapsulation efficiency and the particle size. In this way, 12 formulas describing experimental domain of interest were prepared. Results obtained using small angle X-rays scattering (SAXS) and cryo-transmission electron microscopy (cryo-TEM) evidenced the presence of nano-objects with either sponge or hexagonal inverted structure. In the zone of interest, the percentage of each component was determined to obtain both high encapsulation efficiency and small size of particles. Two optimized formulations were found: F7 and F1. They are very close in the ternary phase diagram as they contain 6.83% of poloxamer 407; 44.18% and 42.03% of monoolein; 46.29% and 48.44% of water for F7 and F11, respectively. These formulations displayed a good compromise between inputs and outputs investigated.     

Combination of Lean value-oriented conception and facility layout design for even more significant efficiency improvement and cost reduction

Global market competition and fluctuating customers’ demands require manufacturing enterprises to focus on cost reduction and efficiency improvement to increase competitiveness and sustainability. The purpose of the research was the elaboration of the methodology and procedure of a new combined efficiency improvement method which basically applies Lean methods and also uses the facility layout design (FLD) method simultaneously, integrating the different advantages of these methods, which is even more efficient that applying each of the methods individually. The main significant added-value of the study is the elaboration of a new combined method, which results in even more significant improvement of efficiency and several KPIs, furthermore, cost reduction, which is confirmed by a real case study for the improvement of a manufacturing plant. In the case study, the application of 13 Lean methods and the FLD method (which aims at the minimisation of material workflow, travel distance of materials, material handling cost and space used for assembly) led to the improvement of 10 quantitative and 5 qualitative indicators: productivity; cycle-time; number of workstations and operators; WIP (work-in-process) inventories; space used for assembly; material workflow; travel distance of materials; material handling cost; labour cost; component supply; products’ quality; transparency; standardisation; workplace ergonomics.     

Self-assembled liquid crystalline nanoparticles as an ophthalmic drug delivery system. Part I: influence of process parameters on their preparation studied by experimental design

To develop self-assembled liquid crystalline nanoparticles as a drug delivery system for keratoconus treatment, a formulation containing riboflavin a water-soluble drug, two surfactants (poloxamer 407 and mono acyl glycerol – monoolein-) and water was optimized and prepared by emulsification and a homogenization process. A fractional factorial design was applied to estimate the main effects and interaction effects of five parameters on two responses, namely particle size and encapsulation efficiency. The five parameters are the temperature of the two phases, the duration of emulsification, the presence of heating during homogenization, the number of passes and pressure. The most influent parameters are the presence of heating during the homogenization and the pressure that led to the production of nanoparticles with an average size of 145?nm and an average encapsulation efficiency of 46%.     

D-optimal experimental design for production models in nonstandard experiments

Robust design is an effective Quality by Design method to reduce product variation by selecting levels of design factors. For a number of situations, a nonstandard design region with linearly limited resources is needed to conduct an experiment. In the literature, little attention has been given to the development of robust design models for the nonstandard design region with a combination of linearly limited resources and a limited number of design points. In this paper, a selection scheme of D-optimal experimental design points is proposed to generate design points using the modified exchange algorithm for the nonstandard design region while specifying linearly limited resources and the limited number of design points. The modified exchange algorithm is able to generate global design points with less time complexity than the improved Fedorov algorithm. In addition, robust design models linking a D-optimal experimental design with quality considerations are proposed in order to obtain optimum settings of design factors for the product. Comparative studies are also presented. Finally, a real-life experimental study shows that the proposed models with the desirability function and the sequential quadratic programming technique achieve greater variance reduction than the traditional counterparts.     

An exact penalty function method for optimising QAP formulation in facility layout problem

A quadratic assignment problem (QAP), which is a combinatorial optimisation problem, is developed to model the problem of locating facilities with material flows between them. The aim of solving the QAP formulation for a facility layout problem (FLP) is to increase a system’s operating efficiency by reducing material handling costs, which can be measured by interdepartmental distances and flows. The QAP-formulated FLP can be viewed as a discrete optimisation problem, where the quadratic objective function is optimised with respect to discrete decision variables subject to linear equality constraints. The conventional approach for solving this discrete optimisation problem is to use the linearisation of the quadratic objective function whereby additional discrete variables and constraints are introduced. The adoption of the linearisation process can result in a significantly increased number of variables and constraints; solving the resulting problem can therefore be challenging. In this paper, a new approach is introduced to solve this discrete optimisation problem. First, the discrete optimisation problem is transformed into an equivalent nonlinear optimisation problem involving only continuous decision variables by introducing quadratic inequality constraints. The number of variables, however, remains the same as the original problem. Then, an exact penalty function method is applied to convert this transformed continuous optimisation problem into an unconstrained continuous optimisation problem. An improved backtracking search algorithm is then developed to solve the unconstrained optimisation problem. Numerical computation results demonstrate the effectiveness of the proposed new approach.