Ahmad Mayyas

Incorporating quality function deployment and analytical hierarchy process in a knowledge-based system for automotive production line design

The authors present an intelligent tool to perform the design of automotive production line, specifically for automotive body-in-white panels. The system adopts quality function deployment principle and analytical hierarchy process methodology to be the main reasoning logic for design decisions, and to explore the effect of enhancing these traditional tools with an artificial intelligent scheme, namely the knowledge-based system. Moreover, all knowledge and expertise of production line design are stored in an interactive knowledge base. Thus, the system is knowledge-base-oriented and exhibits the ability to repetitively deal with design problems as changes occur to design needs or manufacturing process options. Furthermore, this technique offers two knowledge bases: the first holds the production requirements and their correlations to essential process attributes, while the second contains available manufacturing processes and their characteristics to fabricate body-in-white panels. Finally, the work contains a case study to verify the developed systems functionality and merits. The results demonstrate an increase in the design procedure efficiency and reduction in the inconsistency. Similarly, the system provides improved results interpretation using graphical representation, in addition to the possibility of tracking the justification of final selected manufacturing processes down to the customer requirements.

Design considerations of flat patterns analysis techniques when applied for folding 3-D sheet metal geometries

This paper discusses a systematic approach to implement the principles of Flat Pattern Analysis FPA for folding sheet metal products. The paper starts by highlighting the needs for the vehicular structure forming process with respect to the main production line requirements through using Quality Function Deployment QFD matrix. Additionally, the potentials of fold forming for sheet metal parts in achieving the major production needs will then be benchmarked against other forming techniques through a decision making tool namely; the Analytical Hierarchy Process AHP. The study investigates the application of flat pattern tools for sheet metal products derived from analysis for thin or zero thickness sheets (i.e. paper origami). The analysis sets an approach to generate all possible configurations of flat patterns that result in a specific 3-D structure profile. Secondly, a set of optimality selection metrics are developed and applied to these configurations to help determine the most optimized flat pattern. These optimality measures are a metric based on compactness, a metric for nesting efficiency to describe the strip layout planning, and two measures to assess the manufacturing aspect i.e. bending operation in terms of number and orientation of bend lines.

Eco-material selection assisted with decision-making tools, guided by product’s attributes; functionality and manufacturability

This study proposes an eco-material selection approach assisted with decision-making tools namely; analytical hierarchy process (AHP) and quality function deployment (QFD). The study derives the material selection indices for an automobile’s structural and closure panels based on each panel; manufacturability, functionality requirements (load bearing characteristics). Additionally, two constraints are mainly defined; the cost (economic aspect) and the environmental impact (using embodied energy and recyclability). The decision-making tools prioritise the derived metrics based on current original equipment manufacturers (OEMs) perspective. The developed approach is then applied to rank different lightweight material options for vehicular panels. This study has the potential to present a balanced scheme between technological, economic and ecological aspects of automotive body-in-white (BIW) design, and to be implemented in a life-cycle assessment (LCA) study.

Knowledge-based system, equipped with cluster analysis for eco-material selection: an automobile structure case study

The aim of this study is to develop a material selection framework structured around a knowledge-based system (KBS). Specifically, a hybrid data mining technique is employed to extract knowledge from large datasets using cluster analysis techniques; the mined knowledge then serves as the inference logic within the KBS designed for material selection purposes. Cluster analysis results are used as a basis for the tree-based structure of the KBS where if–then rules are developed based on the general cluster properties; that is, inference logic is structured in a way such that it can predict general sustainability characteristics of the material as well as its exact mechanical, cost and physical properties. To develop the structure of the KBS, the selection structure employs sustainable material indices. Additionally, the proposed material selection model of the KBS is purposefully composed of material sustainability, functionality and cost indices. The constructed knowledge is then demonstrated for selecting automobile structural panels.

Knowledge-based systems in sheet metal stamping: a survey

This manuscript surveys and analyses the different implementations of knowledge-based systems (KBS) in sheet metal workings. The scope will cover different stamping phases; specifically the stamped part design, the stamping process planning, the die structure planning and manufacturing with the focus on progressive die designs, and lastly, the strip layout planning. The analysis of the KBS in each of these stamping phases will include the intended function of the system, the KBS construction methodology and the reasoning logic scheme utilised to manipulate the content of the knowledge bases. The survey compares the followed approaches in terms of challenges and merits found for each followed approach. This work serves as a current state-of-the-art analysis for employing KBS in sheet metal stamping (SMS).

Knowledge Based Systems KBS for energy efficiency: Energy aware manufacturing

This manuscript discusses the implementation of information technology tools; specifically, computerized Knowledge Based Systems KBS, in managing the energy flows, conversions, and expenditures within manufacturing environments; including renewable energy sources (namely, landfill gas). The study presents actual KBS embodiments in energy auditing, modeling, and optimization. Additionally, the text presents how IT tools can be used at a system level to integrate sustainability planning within manufacturing planning. The developed tool also addresses the question “how energy-efficient are renewable energy sources” using a novel simulation platform based on hybrid scheme.