Bhupesh Kumar Lad

Optimal maintenance schedule decisions for machine tools considering the user's cost structure

This paper proposes a model for obtaining optimal preventive repair and replacement intervals of a machine tool subassembly considering the users cost structure and the effect of major overhauls. Corrective actions are considered as minimal, whereas preventive repair and major overhauls are considered as imperfect in this paper. The objective is to obtain optimal repair/replacement decisions for the machine tool subassembly such that the expected life-cycle cost contribution over the whole life of the system is minimised. The proposed strategy improves over other existing strategies since it simultaneously considers the effects of the users cost structure and major overhauls, while optimising the preventive repair and replacement intervals. A numerical illustration is provided to demonstrate the general application of the proposed approach.

Integrated reliability and optimal maintenance schedule design : a Life Cycle Cost based approach

Customers of industrial system/machines are becoming increasingly focused on life cycle performance of products for their purchase decisions. Manufacturers of such type of products, for example machine tools, are experiencing increased pressure from customers to deliver customised products with documented reliability, maintainability, maintenance and support characteristics and also with minimum environmental impacts. In this article, first the problem has been explored in the context of Indian machine tool industry and also from literature perspective. Some of the important issues are listed and research steps are identified to address these issues.

A parameter estimation method for machine tool reliability analysis using expert judgement

This paper aims at providing a parameter estimation method for the machine tool reliability analysis to overcome the problem of unavailability of a well-defined failure data collection mechanism. It uses the knowledge and experience of maintenance personnel to obtain the parameters of lifetime distribution of the repairable as well as non-repairable components/subassemblies. It is further developed for the cases where the knowledge available with the expert is with reference to the preventive repair/replacement policy used in the field. In case of imperfect repairs, the methodology also helps in estimating the value of restoration factor. The goodness of the proposed methodology at a given accuracy level in expert judgements are tested against the maximum likelihood estimates of the parameters. It is concluded that the expert judgement method provides a satisfactory alternative to statistical methods when no or very few historical time to failure data points are available.

Optimal Reliability Design of a System

Reliability is one of the most important attributes of performance in arriving at the optimal design of a system since it directly and significantly influences the system’s performance and its life cycle costs. Poor reliability would greatly increase life-cycle costs of the system, and reliability based design must be carried out if the system is to achieve its desired performance. An optimal reliability design is one in which all possible means available to a designer have been explored to enhance the reliability of the system with minimum cost under the constraints imposed on the development of a system.

Distributed maintenance planning in manufacturing industries

Abstract The combination of sensors and computing infrastructure is becoming increasingly pervasive on the industry shop-floor. Such developments are enabling the automation of more and more industrial practices, and are driving the need to replace conventional planning techniques with schemes that can utilize the capabilities of Cyber-Physical Systems (CPS) and Industrial Internet of Things (IIoT). The future is a place where intelligence is endowed to every entity on the shop floor, and to realize this vision, it is necessary to develop new schemes that can unlock the potential of decentralized data observation and decision-making. Maintenance planning is one such decision-making activity that has evolved over the years to make production more efficient by reducing unplanned downtime and improving product quality. In this work, a distributed algorithm is developed that performs intelligent maintenance planning for identical parallel multi-component machines in a job-shop manufacturing scenario. The algorithm design fits intuitively into the CPS-IIoT paradigm without exacting any additional infrastructure, and is a demonstration of how the paradigm can be effectively deployed. Due to the decentralized nature of the algorithm, its runtime scales with complexity of the problem in terms of number of machines; and the runtime for complex cases is of only a few minutes. The supremacy of the devised algorithm is demonstrated over conventional centralized heuristics such as Memetic Algorithm and Particle Swarm Optimization.

A novel integrated tool condition monitoring system

A tool condition monitoring (TCM) system is vital for the intelligent machining process. However, literature has mostly ignored the interaction effect between product quality and tool degradation and has devoted less attention to the criterion of integrated diagnostics and prognostics to cutting tools. In this paper, we aim to bridge the gap and make an attempt to propose a novel integrated tool condition monitoring system based on the relationship between product quality and tool degradation. First, a cost efficient experimentation concerning high-speed CNC milling machining was implemented. Subsequently, a comprehensive correlation investigation was performed; revealing strong positive relationship exists between product quality and tool degradation. Mapping this relationship, an integrated TCM system pertaining to diagnostics and prognostics was proposed. Herein, the diagnostic reliability was enhanced by researching on the use of a multi-level categorization of degradation. The prognostic competence was enhanced by formulating it explicitly for the tools critical zone as a function of tool life. The system is integrated in a manner that, whenever the degradation curve of the tool reaches the critical zone, prognostics module is triggered, and remaining useful life is assessed instantaneously. To enhance the performance of this system, it is modeled employing support vector machine with optimal training technique. The proposed system was validated based on the experimental data. An extensive performance investigation showed that the proposed system provides a robust problem-solving framework for the intelligent machining process.

Simultaneous selection of reliability design and level of repair for fleet systems

Reliability significantly influences system’s performance and its life cycle costs. Poor reliability would greatly increase life cycle costs of the systems, and reliability based design must be carried out if the system is to achieve its desired performance. Additionally, optimal level of repair is determined to reduce life cycle costs of the fleet systems. Traditionally for such systems, reliability design and level of repair analysis is done sequentially. It is hypothesized in this research that such decisions have interaction effects and hence simultaneous optimization of reliability design and level of repair would improve the life cycle performance of the system. Present paper aims to develop a decision framework for simultaneous selection of reliability design and level of repairs for fleet systems.

Investigating the value of integrated operations planning: A case-based approach from automotive industry

During the last decade, many researchers have focused on joint consideration of various operations planning aspects like production scheduling, maintenance scheduling, inventory control, etc. Such joint considerations are becoming increasingly important from the point of view of current advancement in intelligent manufacturing, also known as Industry 4.0. Under the concept of Industry 4.0, advanced data analytics aim to remove human intervention in decision-making. Thus, the managerial level coordination of decisions taken independently by various departments will be out of trend. Therefore, developing an approach that optimises various operations planning decisions simultaneously is essential. Available literature on such joint considerations is more of the exploratory in nature and is limited to simplistic production environments. This necessitates the investigations of value of integrated operations planning for wide range of manufacturing scenarios. Present paper adopts a case-oriented approach to investigate the value of integrated operations planning. First, an integrated approach for simultaneously determining job sequencing, batch-sizing, inventory levels and preventive maintenance schedule is developed. The approach is tested in a complex production environment of an automotive plant and substantial economic improvement was realised. Second, a comprehensive evaluation is performed to study the robustness and implications of proposed approach for various production scenarios. Results of such pervasive performance investigations confirm the value of proposed approach over conventional approaches.

Integrated production and maintenance planning for parallel machine system considering cost of rejection

Production scheduling and maintenance planning have interdependencies but been often considered and optimized independently in practice and in the literature. Furthermore, product quality has direct relationship with maintenance planning. This paper proposes an integrated approach for production scheduling and maintenance planning for parallel machine system considering the effect of cost of rejection. The approach aims to determine optimal production schedule and maintenance plan such that overall operations cost is minimized. A simulation-based optimization approach is used to solve the problem. A numerical investigation is performed to illustrate the approach. The integrated approach shows between 0.6 and 35.8% improvement in term of overall operations cost over independent approach for various scenarios. The results indicate that simultaneous consideration of production scheduling and maintenance planning results into better system performance.

An integrated strategy for fleet maintenance planning

Purpose Conventionally, fleet maintenance decisions are made based on the level of repair (LOR) analysis. A general assumption made during LOR analysis is the consideration of the lifetime distribution with constant failure rate (CFR). However, industries do use preventive maintenance (PM) to extend the life of such components, which in turn may affect the LOR decisions such as repair/move/discard. The CFR assumption does not allow the consideration of effect of PM in LOR analysis. The purpose of this paper is to develop a more practical LOR analysis approach, considering the time-dependent failure rate (TDFR) of components and the effect of PM. Design/methodology/approach In the proposed methodology, first, a detailed life cycle model considering the effect of various parameters related to LOR and PM is developed. A simulation-based genetic algorithm approach is then used to obtain an integrated solution for LOR and PM schedule decisions. The model is also evaluated for the various cases of quality of maintenance measured in terms of degree of restoration. Findings The results, from the illustrative example for a multi-indenture and multi-echelon fleet maintenance network, show that the proposed integrated strategy leads to better LCC performance compare to the conventional approach. Additionally, it is identified that the degree of restoration also affects the PM schedule as well as LOR decisions of the fleet system. Therefore, consideration of TDFR is important to truly optimize the LOR decisions. The proposed approach can be applied to fleet of any equipment. Research limitations/implications The approach is illustrated using a hypothetical example of an industrial system. A more complex system structure in terms of number of machines, types of machines (identical vs non-identical), number of echelons, possible repair actions at various echelons, etc. may be present for a particular industrial case. However, the approach presented is generic and can be extended to any system. Moreover, the aim of the paper is to highlight the importance of the considering PM and quality of maintenance in LOR decision making. Originality/value To the best of the authors’ knowledge, this is the first work which considers the effect of PM and quality of maintenance on LOR analysis. Consideration of TDFR and imperfect maintenance while optimizing LOR decisions is a complex problem. Thus, the work is of high significance from the research point of view. Also, most of the real life fleet systems use PM to extend the life of the equipment. Thus, present paper is a more practical approach for LOR analysis of such systems.