Waguih ElMaraghy

Modelling of Manufacturing Systems Complexity

Abstract This research aims at understanding the effects of human worker attributes within the manufacturing system. The system must balance human characteristics, needs, skills and capabilities within the technical and business environment, in order to be effective. A model is needed to provide insights into the sensitivities of the manufacturing system. A methodology to systematically determine the product and process complexity for any manufacturing environment was introduced - a rapid Manufacturing Complexity Assessment Tool (MCAT). The complexity model is based on three elements: total quantity of information, diversity of information and the information content which corresponds to the effort to produce a feature within a product. Product and process complexity are decoupled and treated independently in this analysis. An example is presented to illustrate the methodology for both. The purpose is to develop a tool that helps to identify areas of complexity that can be simplified.

Automatic sampling for CMM inspection planning of free-form surfaces

Coordinate measuring machines (CMMs) are used to examine the conformity of the produced parts with the designers intent. The inspection of free-form surfaces is a difficult process due to their complexity and irregularity. Many tasks are performed to ensure a reliable and efficient inspection using CMMs. Sampling is an essential and vital step in inspection planning. Efficient and reliable approaches to determine the locations of the points to be sampled from free-form surfaces using the CMM were developed. Four heuristic algorithms for sampling based on the NURBS features of free-form surfaces are presented. The sampling criteria are equiparametric, surface patch size and the surface patch mean curvature. An algorithm for automatic selection of sampling algorithms performs complexity checks on NURBS surfaces, including the surface curvature changes and surface patch size changes, and selects the suitable sampling algorithm. Extensive simulations were performed using the developed methodologies to evaluate their performance using free-form surfaces with different degrees of complexity and compared with the uniform sampling pattern. The CMM measurement errors and manufacturing form errors have been simulated in these studies. The developed algorithms provide a useful tool in selecting the effective sampling plans for the tactile CMM inspection planning of free-form surfaces.

A control approach to explore the dynamics of capacity scalability in reconfigurable manufacturing systems

Abstract This paper presents a dynamic model and analysis for one of the major characteristics of reconfigurable manufacturing systems (RMSs)—capacity Scalability. The dynamic model is analyzed using its transfer function. Dynamic characteristics associated with the delay in capacity Scalability and how to minimize this delay are discussed using control approaches. The problem of how to supply exact capacity in response to market changes is also examined by solving the dynamic problem of the production offset phenomenon in RMSs. The effect of work in process as a damping factor for production disturbances during capacity Scalability is addressed. Finally, a general capacity Scalability controller design is proposed to improve the dynamic performance of RMSs in response to sudden demand changes. The proposed controller considers the different activities associated with the capacity scalability process. A numerical example is also presented to highlight the applicability of the approach.

A Systematic Design Approach for Reconfigurable Manufacturing Systems

The evolution of manufacturing systems is triggered by the dynamic customer environment of its time. The main characteristics of today’s customers’ environment are mass customization and responsiveness to market demand and thus the reconfigurable manufacturing system was suggested for such environment. This paper presents a systematic approach for the design of reconfigurable manufacturing systems and how to control that design process through developing an open mixed architecture for that purpose. The architecture prescribes the different design activities starting from capturing market demand to the system-level configuration and finally the component-level implementation, and also provides some performance measures that are used to control the design process. An example of a reconfigurable automatic PCB assembly line is used to illustrate an application of the developed architecture in real world manufacturing system design.

Change in Manufacturing - Research and Industrial Challenges

Change, in products and systems, has become a constant in manufacturing. This sector continues to witness major market shifts, introduction of new materials and processing technologies as well as great changes in consumer preferences and productsvariety. This presents significant challenges to industrialists, researchers and educators alike. Changes can most often be anticipated but some go beyond the design range. This requires providing innovative change enablers and adaptation mechanisms to mitigate the effects of, and capitalize on, changes in manufacturing. Experiential research and learning is essential to enhance responsiveness and prepare production leaders of the future. The training and experimentation could be significantly enhanced if manufacturing systems could be brought into the laboratories of academic and research institutions. This paper describes the latest state-of-the art fully reconfigurable “plug & play” changeable and flexible “Factory-in-the-Lab” infrastructure and supporting design innovation and advanced research environment. It discusses the use of the iFactory and iDesign system to address these challenges and develop many key technologies and strategies for success. It presents severa novel approaches and methods for achieving the desired balance in the wide spectrum of variation in markets, products, processes, systems and manufacturing enterprises.

Quality prediction in manufacturing system design

Expected product quality is affected by manufacturing system design decisions. Product quality prediction would allow a manufacturer to make better choices of system parameters at the early design stage and, hence, enhance competitiveness through achieving higher quality levels. A Configuration Capability Indicator (CCI) that maps the manufacturing system configuration parameters into an expected product quality level has been developed. A hierarchical fuzzy inference system was developed for modeling the relationship between manufacturing system design parameters and the resulting product quality level. A configuration capability zone is proposed to graphically represent the configuration capability, for a system configuration that produces more than one product, and compare it to the benchmark six-sigma capability. The developed model has been applied to two case studies (Test Parts ANC-90 and ANC-101, and Rack Bar Machining) with different system configurations for illustration and verification. The results demonstrate the ability of the CCI to compare different system configurations from a quality point of view and to support the decision-making during the early stages of manufacturing system planning and design.

Variety, Complexity and Value Creation

Products variety and complexity and value generation for individual customers as well as the whole society are discussed. Dynamic changes in the world economy and their impact on sustainable manufacturing competitiveness are analysed. An overview of products variety, its sources and drivers and its effect on products design, planning of manufacturing is presented. The effect of variety on complexity of products and systems is highlighted as well as strategies for managing and profiting from it. Variety to enhancing customers’ value and the relation between product space and economic complexity are discussed. A multi-facetted strategy for sustainable competitive manufacturing is presented.

Global Kinematic Model generation for n-DOF reconfigurable machinery structure

Automated model generation and solution for motion planning and re-planning of automated systems will play an important role in the future of reconfigurable manufacturing systems. Multi-DOF kinematic structure was generated for any combination of either rotational or translational type of joints, using a novel methodology presented in this paper. This model is named n-DOF Global Kinematic Model (n-GKM). All possible kinematic structures are considered in 3D space, which is divided into eight subspaces and three planes. The planes are perpendicular to the x, y, and z axis of the kinematic structures base frame. The intersections of the three planes divide 3D space into eight subspaces. The kinematic structure generated using the proposed methodology belongs to any of the three surfaces or to any of the eight subspaces. This kinematic structure can be implemented for any robotic system or CNC machine. A methodology for model generation is presented and demonstrated with 2-DOF Global Kinematic Model (2-GKM) case study.

Modeling of Manufacturing Process Complexity

Gaining momentum in several fields of study is the recognition of the need for a viewpoint that includes the human element as an integral part of the modern production system beyond traditional ergonomics. The “intellectual capital” is as much of a resource as money, materials, software and hardware. A model that considers the human players in tandem with the physical elements is needed to provide insights into the sensitivities of the manufacturing system. Using Systems Analysis and Design methods, a framework has been developed, which is valid for different perspectives and environments, to assess the elements of manufacturing complexity. The manufacturing complexity index allows people with diverse backgrounds to rapidly evaluate alternatives and risks with respect to the product, process or operation tasks. In this paper, the technique for evaluating the process complexity metric is presented. An analysis is performed comparing the relative process complexity for a power steering pump bracket that is manufactured in a CNC machining cell and a dedicated line. The areas of complexity are clearly evident. This provides insight for risk assessment as this systematic approach can be used to “mathematically” show tradeoffs for each important criterion during the design stages.

Effect of Time-Based Parameters on the Agility of a Dynamic MPC System

This paper presents a dynamic manufacturing planning and control (MPC) system that can maintain agility through the ability to dynamically switch between different policies due to varying market strategies. The dynamic behavior of the developed system is investigated by studying the effect of the time based parameters on responsiveness and cost effectiveness of the system reflected in the natural frequency and the damping of its different configurations. Results showed that the agility requirements are directly affected by the time based parameters of the MPC system: production lead time, capacity scalability delay, and shipment time. This resulted in a better understanding of the requirements for a well designed agile MPC system.