Availability Of Manufacturing Systems Research Articles

Optimizing production while reducing machinery lockout/tagout circumvention possibilities

Purpose – The optimization of production imposes a review of facility maintenance policies. Accidents during maintenance activities are frequent, sometimes fatal and often associated with deficient or absent machinery lockout/tagout. Lockout/tagout is often circumvented in order to avoid what may be viewed as unnecessary delays and increased production costs. To reduce the dangers inherent in such practice, the purpose of this paper is to propose a production strategy that provides for machinery lockout/tagout while maximizing manufacturing system availability and minimizing costs. Design/methodology/approach – The joint optimization problem of production planning, maintenance and safety planning is formulated and studied using a stochastic optimal control methodology. Hamilton-Jacobi-Bellman equations are developed and studied numerically using the Kushner approach based on finite difference approximation and an iterative policy improvement technique. Findings – The analysis leads to a solution that suggests increasing the “comfortable” inventory level in order to provide the time required for lockout/tagout activities. It is also demonstrated that the optimization of lockout/tagout procedures is particularly important when the equipment is relatively new and the inventory level is minimal. Research limitations/implications – This paper demonstrates that it is possible to integrate production, maintenance and lockout/tagout procedures into production planning while keeping manufacturing system cost objectives attainable as well as ensuring worker safety. Originality/value – This integrated production and maintenance policy is unique and complements existing procedures by explicitly accounting for safety measures.

IPro-GA: an integrated prognostic based GA for scheduling jobs and predictive maintenance in a single multifunctional machine

In highly competitive environment, manufacturing system availability has become a critical issue. For this reason, predictive maintenance must be properly integrated in the production scheduling in order to take into account the wear and tear of the equipment to prevent it from the failure risk. In this context, we investigate the problem of a single multifunctional machine subjected to predictive maintenance based on Prognostic Health Management (PHM). We propose a new interpretation of PHM outputs to define the machine degradation corresponding to the processing of every task. We design a genetic algorithm that we called IPro-GA with the objective of minimizing the total interventions cost. Computational results show the efficiency of our scheme with an average deviation of about 0.1% over a lower bound.

Verification of functional constraints for safe product driven control

This paper deals with dynamic reconfiguration of DES control within the context of manufacturing system to answer product variability or production resources availability. More precisely, it focuses on product-driven control (PDC) that embeds decisional and adaptation capabilities within the product. PDC can be defined in a static or dynamic way: in the first case, all switchable manufacturing trajectories are predefined and implemented in the product control while, in the second case, PDC can be seen as a non-deterministic and “on the fly” choice among control trajectories to fit with the manufacturing system availability and the product state. In this last case, PDC safety is ensured thanks to a filtering approach that enables to maintain PDC within a state space that satisfy functional and safety constraints whatever the control generated by the reconfiguration process is. This paper focuses on the definition and verification of a minimal set of constraints that ensure the sufficiency and the non blocking properties of the constraints. A case study illustrates the approach.

Monitoring manufacturing system availability

The monitoring of production line availability on a day-to-day basis and for continuous improvement is approached from a control charting point of view. Control limits are developed along with the length of time between monitoring periods to control accuracy. This provides a convenient way to monitor the availability measure at any desired level of accuracy.

Modeling manufacturing dependability

In this paper, an analytical approach for the availability evaluation of cellular manufacturing systems is presented, where a manufacturing system is considered operational as long as its production capacity requirements are satisfied. The advantage of the approach is that constructing a system level Markov chain (a complex task) is not required. A manufacturing system is decomposed into two subsystems, i.e. machining system and material handling system. The machining subsystem is in turn decomposed into machine cells. For each machine cell and material handling subsystem, a Markovian model is derived and solved to find the probability of a subset of working machines in each cell, and a subset of the operating material handling carriers that satisfies the manufacturing capacity requirements. The overall manufacturing system availability is obtained using a procedure presented in the paper. The novelty of the approach is that it incorporates imperfect coverage and imperfect repair factors in the Markovian models. The approach is used to evaluate transient and steady-state performance of three alternative designs based on an industrial example. Detailed discussion of the results and the impact of imperfect coverage and imperfect repair on the availability of the manufacturing system is presented. Possible extensions of the work and software tools available for model analysis are also discussed.

Linear Scoring Models In Evaluating Improvements For Manufacturing System Availability

A significant obstacle to justifying design changes for improved availability is the difficulty of assessing the impact of improvements on reliability and/or maintainability. Without operating experience, it is difficult to secure credible estimates of these parameters, and often creative ideas for improved design go unexplored because a satisfactory cost-benefit justification was not provided. This paper describes an application of linear scoring models for evaluating design improvements in manufacturing system availability. The methodology is illustrated by an application in reliability improvement with high-precision grinding equipment used in the computer industry.