A Periodicity Metric for Assessing Maintenance Strategies

Organised by: Cranfield UniversityThe maintenance policy in manufacturing systems is devised to reset the machines functionality in an economical fashion in order to keep the products quality within acceptable levels. Therefore, there is a need for a metric to evaluate and quantify function resetting due to the adopted maintenance policy. A novel metric for measuring the functional periodicity has been developed using the complexity theory. It is based on the rate and extent of function resetting. It can be used as an important criterion for comparing the different maintenance policy alternatives. An industrial example is used to illustrate the application of the new metric.Mori Seiki – The Machine Tool Company; BAE Systems; S4T – Support Service Solutions: Strategy and Transitio

Assessing the Complexity of a Recovered Design and its Potential Redesign Alternatives

Organised by: Cranfield UniversityReverse engineering techniques are applied to generate a part model where there is no existing documentation or it is no longer up to date. To facilitate the reverse engineering tasks, a modular, multiperspective design recovery framework has been developed. An evaluation of the product and feature complexity characteristics can readily be extracted from the design recovery framework by using a modification of a rapid complexity assessment tool. The results from this tool provide insight with respect to the original design and assists with the evaluation of potential alternatives and risks, as illustrated by the case study.Mori Seiki – The Machine Tool Compan

Max-plus Modeling of Manufacturing Flow Lines

AbstractMax-plus algebra can be used to model manufacturing flow lines using linear state-space-like equations which can be used in analysis and control. This paper presents a method for easy and quick generation of the max-plus equations for manufacturing flow lines of any size or structure. The generated equations can model flow lines with infinite as well as finite buffer sizes.A flow line to be modeled is initially assumed to have infinite buffers for all stations. The line model equations are then generated as a combination of serial and merging stations after identifying the different stages using an adjacency matrix for the flow line. In the generated equations, the dynamics of the system are captured in two matrices that are function of the processing times of the different stations in the line. After generating these equations, extra terms are added to account for the finite buffers where for each buffer size, a matrix is added multiplied by the vector of system parameters delayed by the buffer size plus one.The method is intuitive and easy to understand and code in software and thus can facilitate quick analysis of different configurations of manufacturing flow lines and assessing what if scenarios. This can also allow quick on-line reconfiguration of controllers for frequently reconfiguring flow lines

Agile MPC system linking manufacturing and market strategies

Increasing complexity and interdependency in manufacturing enterprises require an agile manufacturing paradigm. This paper considers a dynamic control approach for linking manufacturing strategy with market strategy through a reconfigurable manufacturing planning and control (MPC) system to support agility in this context. A comprehensive MPC model capable of adopting different MPC strategies through distributed controllers of inventory, capacity, and WIP is presented. A hierarchical supervisory controller (referred to as decision logic unit, DLU) that intakes the high-level strategic market decisions and constraints together with feedback of the current manufacturing system state (WIP, production, and inventory levels) and optimally manages the distributed controllers is introduced. The DLU architecture with its three layers and their different functionalities is discussed showing how they link the higher management level to the operational level to satisfy the required demand. A case study for an automatic PCB assembly factory is implemented to demonstrate the applicability of the whole approach. In addition, a comparative cost analysis study is carried out to compare between the developed agile MPC system and classical-inventory- and capacity-based MPC policies in response to different demand patterns. Results showed that the developed agile MPC policy is as cost effective as the inventory-based MPC policy in demand patterns with steady trends, as cost effective as capacity-based MPC in turbulent demand patterns, and far superior than both classical MPC polices in mixed-demand patterns

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

Dynamic modelling of reconfigurable manufacturing planning and control systems using supervisory control

This research is concerned with studying the dynamic performance of reconfigurable Manufacturing Planning and Control (MPC) systems. Such goal requires two main tasks. The first task is to develop a dynamic MPC system model that has the ability to reconfigure to different MPC policies. The second task is to design a supervisory control unit that has as input the high level strategic market decisions and constraints together with a feedback of the current manufacturing system state and then select the optimal suitable operation mode or policy at these conditions. This paper addresses the first task of the proposed research and presents and analyses a dynamic reconfigurable MPC model. The response of the developed model to sudden demand changes under different parameters settings is analyzed. In addition, the stability limits of the system are also studied. The results give a better understanding of the dynamics of reconfigurable MPC systems and the different trade-off decisions required when selecting an MPC policy and the limits for parameters settings. These results represent the first step towards designing the supervisory control unit which will be responsible for managing the reconfiguration of the whole system

A Control Approach to Explore the Dynamics of Capacity Scalability in Reconfigurable Manufacturing Systems

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

The State-of-the-Art and Prospects of Learning Factories

AbstractChangeability of manufacturing systems is an important enabler for offering large variety of competitive products to satisfy customers’ requirements. Learning factories, as teaching and research environments, can play a key role in developing new solutions for changeability, transferring them to the industry and using them in educating engineers. The results of a survey of existing learning factories and their characteristics are presented. Their use in research, teaching and industrial projects is analyzed. A novel scheme to classify those systems with regard to their design, products and their changeability characteristics is outlined. Conclusions about the future of learning factories are drawn