Zhao Liang Jiang

Inventory-shortage driven optimisation for product configuration variation

Inventory control is a critical problem in manufacturing systems. Inventory shortage significantly affects system productivity, while excessive stocks increase the operation cost. It is difficult to avoid fully inventory shortage under mass customisation manufacturing based on product configuration. In this paper, we propose a new approach for inventory-shortage driven optimisation of dynamic product configuration variation to meet the requirements of product configuration change and find suitable combination of parts by considering cost, lead-time and inventory variation. The multi-objective optimisation model uses a multi-objective genetic algorithm and adds impact cost, lead-time and inventory factors to the normal configuration optimisation model. An industrial case study demonstrates the practicality and effectiveness of the proposed approach. By means of this research, valid solutions for configuration variation are available to the decision makers.

Error compensation of thin plate-shape part with prebending method in face milling

Low weight and good toughness thin plate parts are widely used in modern industry, but its flexibility seriously impacts the machinability. Plenty of studies focus on the influence of machine tool and cutting tool on the machining errors. However, few researches focus on compensating machining errors through the fixture. In order to improve the machining accuracy of thin plate-shape part in face milling, this paper presents a novel method for compensating the surface errors by prebending the workpiece during the milling process. First, a machining error prediction model using finite element method is formulated, which simplifies the contacts between the workpiece and fixture with spring constraints. Milling forces calculated by the micro-unit cutting force model are loaded on the error prediction model to predict the machining error. The error prediction results are substituted into the given formulas to obtain the prebending clamping forces and clamping positions. Consequently, the workpiece is prebent in terms of the calculated clamping forces and positions during the face milling operation to reduce the machining error. Finally, simulation and experimental tests are carried out to validate the correctness and efficiency of the proposed error compensation method. The experimental measured flatness results show that the flatness improves by approximately 30 percent through this error compensation method. The proposed method not only predicts the machining errors in face milling thin plate-shape parts but also reduces the machining errors by taking full advantage of the workpiece prebending caused by fixture, meanwhile, it provides a novel idea and theoretical basis for reducing milling errors and improving the milling accuracy.

Multi-Objective Optimization of Product Configuration

In the context of globalization and mass customization, selecting the appropriate product configuration requires a simultaneous consideration of multiple criteria or objectives, which are in conflict with each other. The large solution space implies that analyzing each feasible solution is a combinatorial problem. Furthermore, no single optimal solution exists; on the contrary, there is a set of valid optimal solutions, i.e., the solution set is Pareto-optimal. We present the configuration problem from the perspective of using two types of attributes: static, i.e., the attributes that have pre-defined and constant values throughout the configuration process, and dynamic, i.e., attributes whose values vary according to decisions that are being made during the configuration process. We pose the product configuration as a multiobjective optimization problem requiring that multiple objective functions cannot be combined into a single objective function. We demonstrate the applicability of using Multi-Objective Genetic Algorithms (MOGA) to solve the problem and converge to a Pareto-optimal solution set from the large number of feasible solutions.Copyright

Case reuse based product fuzzy configuration

Abstract This paper proposes a new probabilistic approach to deciding product fuzzy configuration by using the cases in the case library. The main idea relies on condition probability calculation for deriving the probability of selecting an undecided subassembly with given the known selection of a group of decided subassemblies. The predefined product structure is used as the predetermined condition, and the up consistency rule is proposed as the preprocessing step. The conditional probability is extended as the core algorithm. The evaluation method of judgment threshold is designed based on the maximum likelihood estimation. A practical example is applied for proving the validity of this method. By comparing with case-based reasoning (CBR), this new method avoids the properties information blind spots in product configuration procedure and avoids the redundant constraints post-checking procedure. As the result, this method can solve the huge complex configuration data problem, and truly make the fuzzy configuration possible.

Supplier selection and order splitting in multiple-sourcing inventory systems

Supplier selection and inventory control are critical decision processes in single-item multiple-supplier systems. An integer linear programming model is proposed to help managers determine the reorder level, choose the best suppliers, and place the optimum order quantities so that the total average inventory cost is minimum, and constraints of supplier ability, quality, and demand are considered. An algorithm combining the branch-bound algorithm and enumeration algorithm is developed to solve the problems. Application of the proposed model in an automobile industry shows that it is effective.

Zonal Compensation for Work-Piece Elastic Deformation through Fixture Layout Optimization

Work-piece elastic deformation caused by clamping force from fixture will lead to part bending or distorting, which is critical to lower its quality performance. In this paper, a novel zonal compensation method is proposed to compensate the work-piece elastic deformation through fixture layout optimization based on High Definition Metrology (HDM). The zonal surface flatness data of work-piece in fixture is obtained with densely optical measurement firstly. Then, they are reorganized according to the accuracy requirement based on the zonal distribution. Finally, the surface flatness compensation model based on fixture layout is made. The practicality and effectiveness of this new approach was verified using the case of Aluminum alloy 6061 part milling.

Order-oriented cooperative sequencing optimisation in multi-mix-model assembly lines

Order-oriented products assembly sequence among different assembly lines becomes a critical problem for mass customisation manufacturing systems. It significantly affects system productivity, delivery time, and manufacturing cost. In this paper, we propose a new approach to extend the traditional products sequencing from mixed model assembly line (MMAL) to multi-mixed model assembly lines (MMMALs) to obtain the optimal assembly sequence with the objectives of minimising consumption waviness of each material in the lines, assembly line setup cost, and lead-time. A multi-objective optimisation algorithm based on variable neighbourhood search methods (VNS) is developed. We perform an industrial case study in order to demonstrate the practicality and effectiveness of the proposed approach.

A Simulation Study of Logistics Activities in Mixed-model Assembly Lines with Genetic Algorithm

Logistics activities in mixed-model assembly lines involve the material flow that depends heavily on the arrival sequence of products to be assembled in these lines. The main effort of this paper is to analyse how the arrival sequence of mixed models to a specific assembly line affects the logistics activities of the line, where a smoothly- and evenly- distributed material flow is desired. However, the uncertainty caused by the stochastic arrival sequence of customer orders in a make-to-order environment increases the complexity of logistics activities. Confronted with this uncertainty and complexity, we put the logistics activities into a more vivid and intuitive scenario by simulating the behaviour of materials. To begin with, the logistics activities occurring in a specific automobile assembly line is investigated. Secondly, a logistics simulation model of the assembly lines is built. Using the genetic algorithm toolbox embedded in Matlab7.0 package, we obtain subsequently an optimised model sequence that enables an evenly- distributed material flow. Finally, simulation results generated from Matlab7.0 Package are discussed.

A design change analysis model as a change impact analysis basis for semantic design change management

Lack of objective design change analysis model is a major problem for the accurate change impact prediction. To solve this problem, this paper proposes an ontology-based model named design change analysis model to organize the unstructured design properties as a basis for design change management. Design change analysis model is constructed by formalizing the mechanical design specifications in the form of design property network. Benefiting from the fine-grained organization, design change analysis model ensures the objectivity and accuracy of design change impact assessment. Since design change analysis model satisfies the attributions of small-world network, the change impact assessment should focus on more meaningful aspects including the linkage weight, node degree, and long-chain linkages of design change analysis model. With design change analysis model, the changeability of each design property which provides a quantified change propagation measurement could be evaluated. Additionally, different components could be distinguished in design change analysis model by using clustering algorithms without specializing them in advance. Design change analysis model modeled in web ontology language is a semantic enrichment model. It supports the semantic design change management due to the mathematic logic-based semantics of web ontology language and semantic web rule language. These lays a basis for updating changes among heterogeneous product development systems, and acquiring feasible change impacted properties.

Evaluation of a design property network-based change propagation routing approach for mechanical product development

Design changes are unavoidable in new mechanical product development, and the propagation of changes imposes negative impacts on the design cycle and cost. Due to the non-uniqueness of changing propagation paths, searching for the optimal change propagation path with minimum change-related impact tends to be a serious challenge before the implementation of the change. In this paper, a mathematical programming model is presented to route change propagation. First, the design change analysis model (DCAM) is built based on the design property network. In the DCAM, the design properties are connected by linkages, and the weights of these linkages are objectively estimated by mining the change records. In addition, the change propagation intensity (CPI) is defined by quantifying the change propagation impact. The CPI is indicated by four assessment factors: propagation likelihood, node degree, long-chain linkage, and design margin. Furthermore, the optimal influence propagation path, which corresponds to the minimized maximum of accumulated CPI, is sought with a modified ant colony optimization (ACO) algorithm. A case study and solution comparison verify the feasibility and validity of the proposed approach.