Mohamed Kashkoush

Consensus tree method for generating master assembly sequence

An assembly process plan for a given product provides the sequence of assembly operations, their times as well as the required tools and fixtures for each operation. Much research has been done on automating and optimizing assembly sequence generation as the most important part of an assembly process plan. A novel method for generating the assembly sequence of a given product based on available assembly sequence data of similar products is presented. The proposed method uses a binary tree form to represent the assembly sequences of an existing family of products. A Genetic Algorithm is employed to find the consensus tree that represents the set of all assembly sequence trees with minimum total dissimilarity distance. This is similar to defining Generic Bill-of-Material. The generated consensus tree serves as a master assembly sequence for the product family. The assembly sequence for a new product variant that falls within, or significantly overlaps with, the scope of the considered family of products can be directly extracted from the derived master assembly sequence tree. The developed method is demonstrated using a family of three control valves. This novel method greatly simplifies and enhances automatic assembly sequence generation and minimizes subsequent modifications, hence, reduces assembly planning cost and improves productivity.

An integer programming model for discovering associations between manufacturing system capabilities and product features

Valuable implicit knowledge and patterns are accumulated over time in industrial databases at various stages of product development and production. An example of such hidden patterns would be the cutting tool which is typically used to produce a profiling feature in a given steel part. Discovering and interpreting such patterns would be useful in supporting and optimizing the operations and planning activities such as process planning and manufacturing systems synthesis. A novel knowledge discovery model is introduced to extract useful correlations between the manufacturing domain and design domain based on historical manufacturing data. An Integer Programming model is developed, for the first time, to extract association rules between sets of various product features and manufacturing capabilities used in their production. These associations identify the specific manufacturing system capabilities associated with (i.e. typically used for) the production of each product feature. The discovered knowledge is then used to synthesize the required manufacturing system capabilities for new products with new combinations of features. The proposed IP model was demonstrated using a case study of seven instances of machined parts and the corresponding milling machines used to produce them. The advantages of the proposed association rule discovery IP model were also demonstrated by comparing it with existing association rule discovery methods. The proposed model is simple and easy to implement and automate. Utilizing the proposed model in manufacturing system synthesis should greatly assist in speeding-up product development and manufacturing systems design and re-design.

Designing modular product architecture for optimal overall product modularity

ABSTRACT Designing modular product architectures is beneficial to both product development and manufacturing. Several techniques exist for clustering product components into modules; all these techniques, with few exceptions, do not consider the hierarchical structure of the product. Product architecture consists of a number of hierarchical levels, which add a useful dimension to modularity analysis. Designing product architecture that maximises its overall modularity over all levels of the product structure is the main focus of this research. A novel mathematical programming model has been developed to construct the product structure tree for a given product with optimal modularity at all its hierarchical levels without prior knowledge of their number. A genetic algorithm capable of handling practical problem sizes was also developed. A hand-held body massager is used for demonstrating and validating the proposed modular product architecture design method. A case study from the automotive industry was employed for comparing the obtained modularity analysis output with the relevant state-of-the-art research, and demonstrating achieved improvements. Product modularity is addressed in this research using a new perspective which promotes optimal overall modularity. It improves management of product changes and variety leading to more cost-effective product development and manufacturing.

Generating Master Assembly Sequence Using Consensus Trees

An assembly process plan for a given product provides the sequence of assembly operations, operation times as well as the required tools and fixtures for each operation. Much research has been done on automating and optimizing assembly sequence generation as being the most important part of an assembly process plan. This paper proposes a novel method for generating the assembly sequence of a given product based on available assembly sequence data of similar products. The proposed method uses a binary tree form to represent the assembly sequences of an existing family of products. A Genetic Algorithm is employed to find the consensus tree that represents the set of all assembly sequence trees with minimum total dissimilarity distance. This is similar to defining Generic Bill-of-Material (GBOM). The generated consensus tree serves as a master assembly sequence for the product family. The assembly sequence for a new variant that falls within, or significantly overlaps with, the scope of the considered family of products can be directly extracted from that master assembly sequence tree. The proposed novel method greatly simplifies and enhances automatic assembly sequence generation. It helps reduce assembly planning cost and improve productivity.

A mixed-integer model for two-dimensional polyominoes strip packing and tiling problems

Two-dimensional irregular strip packing problem is one of the common cutting and packing problems, where it is required to assign (cut or pack) a set of 2D irregular-shaped items to a rectangular sheet. The sheet width is fixed, while its length is extendable and has to be minimised. In this paper, a new mixed-integer programming (MIP) model is introduced to optimally solve a special case of the problem, where item shapes are polygons with orthogonal edges, named polyominoes. Polyominoes strip packing may be classified as polyominoes tiling; a problem that can also be handled by the proposed model. Reasonable problem sizes (e.g. 45 polyominoes inside a 10 × 25 sheet) are solvable using an ordinary PC. Larger problem sizes are expected to be solvable when using state-of-the-art computational facilities. The model is also verified via a set of benchmark problems that are collected from the literature and provided optimal solution for all cases.