Journal of Advanced Manufacturing Systems

The practice of lean production has been documented in detail by production and operations management specialists world-wide [B. B. Flynn, S. Sakakibara, R. G. Schroeder, K. A. Bates and E. J. Flynn, Empirical research methods in operations management, Journal of Operations Management 9(2) (1990) 250–284]. Theoretically, on the other hand, there has been little research that define and quantify what it means to be a lean manufacturer. A typological definition of what it means to be lean has in part been resolved by a model initially developed by Shah and Ward [Defining and developing measures of lean production, Journal of Operations Management 25(4) (2007) 785–805]. This definition was used to determine the underlying concepts of lean production in small to medium size manufacturers (SMMs). While most of the factors found in the Shah and Ward [Defining and developing measures of lean production, Journal of Operations Management 25(4) (2007) 785–805] study remained valid, the model developed for SMMs displayed some new factors.

In the present study, aluminum alloy (Al/3.25Cu/8.5Si) composites reinforced with fly ash particles was fabricated using stir casting technique. Fly ash particles of three different size ranges 53–75, 75–103 and 103–125$\mu$m of 3, 6 and 9 weight percentages was reinforced in aluminum alloy. The effect of peak current, pulse on time, and pulse off time on surface roughness (SR), material removal rate (MRR) and tool wear rate (TWR) of electric discharge machining (EDM) was investigated. A central composite design using response surface methodology (RSM) was selected for conducting experiments, and mathematical models were developed using Design Expert V7.0.0 software. Analysis of variance (ANOVA) technique was used to check the significance of the models developed. Peak current was the major factor influencing the EDM of aluminum fly ash composites. The MRR, TWR, and SR of aluminum fly ash composites were also influenced by the size of the fly ash particles.

Systems health monitoring is essential to guaranteeing the safe, efficient, and reliable operation of engineering systems. Integrated systems health management methodologies include fault diagnosis mechanism. Diagnosis involves detecting when a fault has occurred, isolating the true fault, and identifying the true damage to the system. This important issue is even harder when the systems to be diagnosed are dynamic and spatially distributed systems with their successively increasing complexity. For such systems, a single diagnostic entity having a model of the whole system approach is inappropriate. Whereas a distributed approach of multiple diagnostic agents can offer a solution. An overall systematic solution for these issues could be obtained by an artificial intelligent mechanism called the multi-agent system (MAS). This paper presents a MAS model for fault diagnosis based on logical theory of diagnosis. In this approach, each local diagnostic agent has knowledge above its subsystem and an abstract view of the neighboring subsystems and it is able to determine the local minimal diagnoses that are consistent with global diagnoses. The multi-agent models are simulated in Java Agent Development Framework and are applied to the preheated cement cyclone in the workshop of SCIMAT clinker.