Basavraj S. Kothavale

Bio: Basavraj S. Kothavale is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Fin & Knurling. The author has an hindex of 3, co-authored 16 publications receiving 60 citations. Previous affiliations of Basavraj S. Kothavale include College of Engineering, Pune.

Reliability analysis of CNC turning center based on the assessment of trends in maintenance data: A case study

Abstract: Purpose The paper presents reliability, maintainability and life cycle cost (LCC) analysis of a computerized numerical control (CNC) turning center which is manufactured and used in India. The purpose of this paper is to identify the critical components/subsystems from reliability and LCC perspective. The paper further aims at improving reliability and LCC by implementing reliability-improvement methods. Design/methodology/approach This paper uses a methodology for the reliability analysis based on the assessment of trends in maintenance data. The data required for reliability and LCC analysis are collected from the manufacturers and users of CNC turning center over a period of eight years. ReliaSoft’s Weibull++9 software has been used for verifying goodness of fit and estimating parameters of the distribution. The LCC of the system is estimated for five cost elements: acquisition cost, operation cost, failure cost, support cost and net salvage value. Findings The analysis shows that the spindle bearing, spindle belt, spindle drawbar, insert, tool holder, drive battery, hydraulic hose, lubricant hose, coolant hose and solenoid valve are the components with low reliability. With certain design changes and implementation of reliability-based maintenance policies, system reliability is improved, especially during warranty period. The reliability of the CNC turning center is improved by nearly 45 percent at the end of warranty period and system mean time between failure is increased from 15,000 to 17,000 hours. The LCC analysis reveals that the maintenance cost, operating cost and support costs dominate the LCC and contribute to the tune of 87 percent of the total LCC. Research limitations/implications The proposed methodology provides an excellent tool that can be utilized in industries, where safety, reliability, maintainability and availability of the system play a vital role. The approach may be improved by collecting data from more number of users of the CNC turning centers. Practical implications The approach presented in this paper is generic and can be applied to analyze the repairable systems. A real case study is presented to show the applicability of the approach. Originality/value The proposed methodology provides a practical approach for the analysis of time-to-failure and time-to-repair data based on the assessment of trends in the maintenance data. The methodology helps in selecting a proper approach of the analysis such as Bayesian method, parametric methods and nonparametric methods.

Selection of time-to-failure model for computerized numerical control turning center based on the assessment of trends in maintenance data

Abstract: This article provides a generalized framework for selection of time-to-failure model based on the assessment of trends in failure and repair time data. This framework is based on modifications of e...

Failure Modes and Effects Analysis (FMEA) of Computerized Numerical Control (CNC) Turning Center

Abstract: The intensity growth of manufacturing industry in India is increasing the demand of CNC turning centers. The producers of CNC turning centers are trying to fulfil this demand. The quality and reliability of such CNC turning centers is critical for manufacturers and users to meet the market demand. In this context, it is required to identify the potential failures and minimize their occurrence probability. Failure modes and effects analysis (FMEA) is a robust methodology used to study the risk and reliability of various systems. In this paper, FMEA methodology is used to analyze the risk involved in the operation of CNC turning centers. The objective of the analysis was to identify critical failure modes, components and sub-systems. The study uses field failure data from more than 50 CNC turning centers and more than five industrial experts. FMEA results are further compared to the failure rates obtained from the field failure data. The results of the FMEA analysis are useful for system reliability improvement and optimizing maintenance. Electrical and electronic components have higher failure rates compared to mechanical components. CNCS, MT, XZAS, HS, ChS and SS are found to be critical sub-systems.

Life cycle cost analysis of a computerized numerical control machine tool: a case study from Indian manufacturing industry

Abstract: Life cycle cost (LCC) analysis is one of the key parameters in designing a sustainable product or system. The application of life cycle costing in the manufacturing industries is still limited due to several factors. Lack of understanding of LCC analysis methodologies is one of the key barriers. This paper presents a generalized framework for LCC analysis of repairable systems using reliability and maintainability principles.,The developed LCC analysis framework and stochastic point processes are applied for the analysis of a typical computerized numerical control turning center (CNCTC) and governing equations for acquisition cost, operation cost, failure cost, support cost and net salvage value are developed. The LCC of the CNCTC is evaluated for the renewal process (RP) and minimal repair process (MRP) approach.,The LCC analysis of the CNCTC reveals that, the acquisition cost is only 7.59% of the LCC, whereas the operation, failure and support costs dominate and contribute nearly 93% of the LCC. The LCC per day for RP requires additional US$ 1.03 than that for MRP. The detailed LCC analysis of the CNCTC identifies the critical components of CNCTC and these components are: spindle motor, spindle motor cooling fan, spindle belt, drawbar, spindle bearing, oil seals, hydraulic hose, solenoid valve, tool holder, lubrication pump motor system, lubrication hose, coolant pump motor system, coolant hose, supply cables, drive battery.,The developed framework of LCC of a repairable system can be applied to any other repairable systems with the appropriate modifications. LCC analysis of CNCTC reveals that the procurement decision of a product or system should be based on LCC and not only on the acquisition cost. The optimum utilization of consumables such as cutting tools, coolant, oil and lubricant can save operation cost. Thus, use of high-efficiency electric motors and the usage of recommended consumables can prolong the life of several components of a system. Therefore, due consideration and attention to these parameters at product design stage itself will decrease failure and support cost and ultimately its LCC.

A review of prognostics and health management of machine tools

Abstract: This paper presents a survey of the applications of prognostics and health management maintenance strategy to machine tools. A complete perspective on this Industry 4.0 cutting-edge maintenance policy, through the analysis of all its preliminary phases, is given as an introduction. Then, attention is given to prognostics, whose different approaches are briefly classified and explained, pointing out their advantages and shortcomings. After that, all the works on prognostics of machine tools and their main subsystem are reviewed, highlighting current open research areas for improvement.

Critical spare parts ordering decisions using conditional reliability and stochastic lead time

Abstract: The decision-making processes have become crucial for organizations immersed on an intensified competitiveness situation. Because of current industrial reality, a continuous improvement of the ability for adding value and enhancing profitability of decisions is needed by firms (Pascual et al, 2009). Companies are frequently required for reducing production costs and increasing asset utilization; consequently, stress on machines is generated. This situation affects reliability and, most important, system throughput. Due to the demanding use of equipment, the stressful effect tends to be critical for the performance of asset intensive industries, e.g.: mining operations, aeronautic, nuclear industry, or defence. One way to improve the system performance is to allocate inventory resources. Stocks can represent about one third of all assets of a typical company (Dfaz & Fu, 1997). Of these assets, spare parts have special relevance for asset intensive industries since extensive system downtime could be produced by the insufficiency of spare part stocks (Louit 2006). As an example, in the airlines business, spares sum up above US$50 billion (Kilpi & Vepsalainen, 2004). The management of spare part stocks requires a balance between shortage costs (costs of shutting down the operations) and overstock costs (financial costs associated with holding a safety stock) (Sarker & Haque, 2000) and ordering costs. Insufficient stocks affect overall performance of physical assets; on the other hand, oversized stocks lead to an inefficient use of resources and can imply high investment costs. Therefore, decisions about spare-stocking policies can become essential in the cost structure of companies. In order to provide an efficient spare management performance, a suitable ordering strategy can be relevant. This situation provides an opportunity to improve decision making methods. A spare part classification scheme becomes necessary in order to set optimal policies for those spares that may affect the system the most and at the least effort. We proposed an ordering decision-aid method, in order to secure the spare management performance into an operational environment that needs continuity to compete into a demanding business context.

Reliability, availability and maintainability analysis of a cement plant: a case study

Abstract: The demand of cement in India is expected to increase rapidly as the government has been giving immense boost to various housing facilities, infrastructure projects, road networks and railway corridors. One of the ways to meet this rise in the demand of cement is to increase the capacity utilization of the existing cement plants by improving their availability. The availability of a cement plant can be improved by avoiding failures and reducing maintenance time through reliability, availability and maintainability (RAM) analysis of its subsystems. The paper aims to discuss this issue.,The data related to time between failure (TBF) and time to repair (TTR) of all the critical subsystems of a cement plant were collected over a period of two years for carrying out RAM analysis. Trend test and serial correlation test were performed on TBF and TTR data to verify whether these data are independent and identically distributed or not. Afterwards, the authors use EasyFit 5.6 professional software to find best-fit distribution of TBF and TTR data and their parameters. The effectiveness of a preventive maintenance policy was evaluated by simulating the real and proposed systems.,The results of the analysis show that the raw mill and the coal mill are critical subsystems of a cement plant from a reliability point of view, whereas the kiln is a critical subsystem from an availability point of view. The analysis shows that the repair time of the cement mill should be reduced for improving the availability of the cement plant. The RAM analysis showed that the capacity of the case study company is 17 percent underutilized due to maintenance-related problems and 15 percent underutilized because of management-related problems.,The study exhibits the usage of RAM analysis in deciding preventive maintenance programs of several cement plant subsystems. Thus, it would serve as a reference for reliability and maintenance managers in deciding maintenance strategies of cement plants as well as in improving their capacity utilization.,The study exhibits the usage of RAM analysis in deciding preventive maintenance programs of several cement plant subsystems. Even more, using a simulation study, the authors show that preventive maintenance of the cement plant beyond a certain level can be disadvantageous as it leads to an increase in downtime and decrease in availability.

A predictive maintenance cost model for CNC SMEs in the era of industry 4.0

Abstract: Within the subject area of maintenance and maintenance management, authors identified a deficiency in studies focussing on the expected value from adopting predictive maintenance (PdM) techniques for machine tools (MTs). Authors identified no studies focussing on presenting a PdM value analysis or cost model specifically for small-medium enterprises (SMEs) operating computer numerically controlled (CNC) MTs. This paper’s novelty is addressing SMEs’ minimal representation in literature by explanatorily collecting data from SMEs within the focal area via surveys, modelling and analysing datasets, then proposes a cost-effective PdM system architecture for SME CNC machine shops that predicts cost savings ranging from £22,804 to £48,585 over a range of 1–50 CNC MTs maintained on a distributed numerically controlled (DNC) network. It affirms PdM’s tangible value creation for SME CNC machine shops with predicted positive impacts on their MT cost and performance drivers. These exploratory research findings corroborate SMEs pooling together to optimise their CNC MT maintenance cost through the recommended system architecture. Finally, it introduces opportunities for further PdM research taking into account SMEs’ perspective. The paper’s industrial application is confirmed from the surveyed SMEs that demonstrated their current utility of PdM; then anonymous positive feedback on the online dashboard, shared with participant companies, confirmed the research results supported SMEs in considering exploring the path to adapting PdM. It is anticipated that beneficiaries of this research will be maintenance managers, business executives and researchers who seek to understand the expected financial and performance impact of adopting PdM for a MT’s overall life cycle costs.