Machine Tool Reliability

Chapter 1

Introduction

Reduced cost of production, timely delivery and high quality of products are the prime objectives for manufacturing industries. Breakdowns of production machinery or machine tools affect the manufacturer’s ability to meet the goals of Cost, Time and Quality (CTQ). One of the studies suggests that the economic loss due to an unexpected stoppage in industry can be as high as US $70,000 to US $420,000 per day [1]. Application of reliability engineering tools and techniques to machine tools for improving the manufacturing system performance is therefore a vital area of study.

The machine tool industry is one of the supporting pillars for the competitiveness of the entire manufacturing sector since it produces capital goods which in turn may produce manufactured goods. Customers of machine tool manufacturers (termed as users in this book) are, in many cases, vendors to other customers and have commitments to meet. Breakdowns of machine tools may jeopardize their ability to meet these commitments and also cost a lot of money to the users in terms of poor quality, slower production, downtime, etc. Since poor reliability and improper maintenance of a machine tool greatly increase the life cycle cost to the users, many machine tool users have changed their purchase criteria for a machine tool from initial acquisition cost to Life Cycle Cost (LCC) or Total Cost of Ownership (TCO).

As reliability engineering plays an important role in reducing the LCC of machine tools, this book will be equally appealing to machine tool manufacturers and users.

The book covers both the manufacturer’s and user’s viewpoint of machine tool reliability. Decisions made during the design phase of a product have the largest impact on the life cycle cost of a system. The inherent failure and repair characteristics of components and assemblies are frozen with the selection of the machine tool configuration at the design stage. Therefore, the maintenance requirements of the machine tools are also fixed at the design stage itself. For example, a higher reliability component may require a lower replacement frequency for the same operating profile compared to a lower reliability component. Therefore, machine tool manufacturers need to consider the reliability and maintenance aspects at the design stage itself. On the other hand, the cost effectiveness of machine tools at the user’s end also depends on the shop-floor level operations planning decisions, i.e., scheduling, inventory, quality control, etc. These shop-floor level operations planning decisions have interaction effect with machine tool reliability and maintenance. Therefore, machine tool users need to consider the reliability and maintenance aspects during operations planning. The goal of this book is to provide a consolidated volume on various dimensions of machine tool reliability and its implications from the manufacturer’s and user’s point of view.

The introductory chapter of the book describes basic reliability terms and defines machine tool failures. The importance of machine tool reliability from the manufacturers’ and users’ point of view is also discussed.

1.1 Basic Reliability Terms and Concepts

This section introduces important reliability terms and concepts which will help the reader in following the rest of the sections of the book.

Reliability: This is the probability that an item can perform its intended function for a specified interval under stated conditions [2].

In other words, it is the probability of survival over time. To determine the reliability of a particular component or system, an unambiguous and observable description of failure is essential. The machine tool failures are defined in the next section.

If T is a random variable, representing time to failure of the system or component, then reliability can be expressed as:

(1.1) equation

It is contextual here to clearly differentiate the term quality and reliability. If quality is the conformance to the specifications at t = 0, then reliability can be considered as conformance to the specifications at t > 0. However, in this book, reliability is used in the context of the machine tools, while quality is used in the context of the products produced using machine tools.

Failure Rate (Hazard Rate): Failure rate or hazard rate is the instantaneous (at time t) rate of failure [3]. It is the instantaneous failure rate. This index is normally used for non-repairable components. A component of the system may have increasing, decreasing, or constant failure rate. It is further discussed in Chapter 2.

Rate of Occurrence of Failure (ROCOF): This index is often used in place of hazard rate for repairable system. Failures occur as a given system ages and the system is repaired to a state that may be the same as new, or better, or worse. Let N(t) be a counting function that keeps track of the cumulative number of failures a given system has had from time zero to time t. N(t) is a step function that jumps up one every time a failure occurs and stays at the new level until the next failure. The ROCOF is the total number of failures within an item population, divided by the total number of life units expended by that population during a particular measurement period under stated conditions [2].

Every system will have its own observed N(t) function over time. If we observed the N(t) curves for a large number of similar systems and averaged these curves, we would have an estimate of M(t) = the expected number (average number) of cumulative failures by time t for these systems.

Maintenance: All actions necessary for retaining an item in or restoring it to a specified condition [2].

Corrective Maintenance (CM): All actions performed as a result of failure, to restore an item to a specified condition [2]. Corrective maintenance can include any or all of the following steps: localization, isolation, disassembly, interchange, reassembly, alignment and checkout.

Preventive Maintenance (PM): All actions performed to retain an item in a specified condition by providing systematic inspection, detection, and prevention of incipient failures [2].

Predictive Maintenance: Predictive maintenance or Condition Based Maintenance (CBM) is carried out only after collecting and evaluating enough physical data on performance or condition of equipment, such as temperature, vibration, particulate matter in oil, etc., by performing periodic or continuous (online) equipment monitoring [4].

Maintainability: It is the relative ease and economy of time and resources with which maintenance can be performed. More precisely, it is the probability that an item can be retained in, or restored to, a specified condition within a specified time when maintenance is performed by personnel having specified skill levels, using prescribed procedures and resources, at each prescribed level of maintenance and repair [2].

Availability: Depending on the purpose of analysis, a number of different definitions are used in the literature, some of which are given below [3]:

Instantaneous or Point Availability, A(t): It is the probability that a system will be operational at any random time t. Unlike reliability, the instantaneous availability measure incorporates maintainability information.

Average Availability: It is the proportion of time a system is available for use during a mission. Mathematically, it is calculated as the mean value of the instantaneous availability function over the period (0, T).

(1.2) equation

Steady State Availability: The steady state availability of the system is the limit of the instantaneous availability function as the time approaches infinity.

(1.3) equation

Inherent Availability: It is the steady state availability when considering only the corrective maintenance downtime of the system. It does not include delays due to unavailability of maintenance personnel, unavailability of spare parts, administrative procedures, etc. The inherent availability of a system is a function of the reliability of its components and maintainability, which more or less get defined at the design stage of the equipment.

(1.4)

where MTBF is the mean time between failures and MTTR is the mean time to repair.

Operational Availability: It is a measure of the average availability over a period of time, including all the delays due to unavailability of maintenance personnel, spare parts, administrative procedures, etc. Operational availability is the availability that the customer actually experiences.

(1.5)

where MTBM is the mean time between maintenance, SDT and MDT are the supply and maintenance delays respectively.

Inherent availability and operational availability are used in this book and are discussed further in Chapter 3.

Life Cycle Cost (LCC): It is the sum of acquisition, logistics support, operating, and retirement and phase-out expenses [2].

1.2 Machine Tool Failure

The first step in applying any reliability engineering technique to any system is to clearly define the failures of that particular system. The Society of Automotive Engineering (SAE) defines the failure of production machinery/equipment as: any event due to which the machinery/equipment is not available to produce parts at specified conditions when scheduled, or is not capable of producing parts or performing scheduled operations to specification [5].

However, care should be taken in expressing the failure criteria as different users may have different expectations in terms of the product performance. There may also be a diversity of opinion between machine tool users and manufacturers as to what exactly constitutes a degraded performance or failure. Therefore, while the SAE definition of failure can serve as a guideline, it is necessary that the failure criteria are clearly and quantitatively (wherever possible) defined by the designer, keeping in mind the user’s viewpoint. In this book, failures of machine tools are defined in terms of failure consequences. These consequences express the user’s view of failure under the mutually agreed upon operating conditions between the user and the manufacturer. Whenever failure occurs, it leads to one of the following Failure Consequences (FCs).

Failure Consequence 1 (FC1): failure is detected immediately and the machine has to be stopped.

Failure Consequence 2 (FC2): machine continues to operate, but at a lower production rate than designed (i.e., with increased cycle time).

Failure Consequence 3 (FC3): machine continues to run, but produces more rejections than the normal rejection rate.

In many cases, failure consequences 2 and 3 are detected by the users after a time lag, during which the machine tool runs at a reduced performance level. The last two failure consequences can be considered as the result of partial failures and can be defined as degradation in performance without complete failure [6–8]. Figure 1.1 depicts these failure consequences on a time-performance curve. It clearly indicates the relation of a machine tool failure with the user’s shop-floor performance measures like Availability (A), Performance Rate (PR), Quality Rate (QR) and failure costs.

Figure 1.1 Machine tool failure on time-performance curve

(Reprinted with permission from [9]; Inderscience Publishers).

It was observed during one of the research projects carried out by the authors with a machine tool industry in India that many failure events of machine tools lead to failure consequences 2 and 3 and finally to consequence 1 when detected. Thus, such failure consequences must be considered explicitly by machine tool manufacturers, as well as by users, to reduce the life cycle cost of the machine tools. Table 1.1 provides examples of all three types of failure consequences for a CNC grinding machine.

Table 1.1 Failure consequences and affected performances (Reprinted with permission from [8]; Inderscience Publishers).

1.3 Machine Tool Reliability: Manufacturer’s View Point

Historically, machine tool designers have done a good job of evaluating the functions and form of products at the design phase. Once the functional design of the machine tool is done, designers generally have multiple alternatives for many of the components/subassemblies that can satisfy the functional requirements of the system. Such alternatives, apart from their cost, also differ in their inherent failure and repair characteristics, like time-to-failure distribution, time-to-repair distribution, failure consequences, etc. For example, a designer may have two alternatives for spindle. viz., motorized and belted spindle. Even though both these alternatives may satisfy the functional requirements of the machine, they will have different failure and repair characteristics. Therefore, each of these alternatives will contribute differently to the reliability performance of the system. Further, Preventive Maintenance (PM) can also be used to improve the reliability performance of the system. However, preventive maintenance again consumes resources and time which could otherwise be used for production, thereby affecting profit. Therefore, from the view point of a machine tool designer, the problem of reliability and maintenance-based design of machine tool finally boils down to selecting the optimal machine tool configuration from the available alternatives for different components/subassemblies by simultaneously considering reliability and maintenance parameters such that the final configuration meets the user’s performance requirements and budget constraints.

However, optimization of reliability and maintenance schedule poses a challenge when users are unable to explicitly express their reliability requirements quantitatively. It was observed during a survey done by the authors that only a few corporate customers express their reliability requirements explicitly in terms of Mean Time Between Failures (MTBF). But, even these users are more concerned about their shop-floor level performance and they judge the reliability of a machine tool based on how well it performs in terms of performance measures like Overall Equipment Effectiveness (OEE), Life Cycle Cost (LCC), Cost Per Piece (CPP), etc. These performance measures are closer to the heart of the users and are affected by the inherent failure and repair characteristics of the machine tool components/subassemblies and the maintenance plans. However, the extent to which the inherent failure as well as repair characteristics and preventive maintenance of machine components/subassemblies affect a user’s performance measures also depends on the user’s cost structure and shop-floor level policies. For example, if a user has alternative machines available to bear the load of a failed machine, then the downtime cost of that machine may not be as significant as in the case where there is no alternative machine available to bear the load of the failed machine [10]. Similarly, if a machine is being used as a stand-alone machine, its downtime cost will be different than that in the case when the same machine is being used in a production line. As can be seen from the examples, the cost structures will be different for each of the cases. Similarly, a tighter quality control policy at the user’s end will detect process shifts due to failure of machine components/subassemblies much earlier, thereby reducing the rejection rate. Thus, the effect of machine failures and maintenances on LCC, OEE, and other performance measures, may be different for different users.

In the case of machine tools, users provide their functional requirements, like cycle time, process capability, material to be machined, etc., to the manufacturers. Based on these requirements, the manufacturer designs the machine tool. In general, the design is for one of the following:

General purpose machine tools

Special purpose machine tools

Customized machine tools

A general purpose machine tool is one which can be used for a wide variety of operations, on a wide range of size of work pieces [11]. Thus, they are designed for a wide range of users. A special purpose machine tool is one which is designed for some specific operations on a limited range of work piece sizes and shapes [11]. These are generally engineered to meet the requirements of a specific user. In a customized machine tool, some of the components/subassemblies, especially structural elements, are standard components, while others are designed based on the specific requirements of a customer. Thus, the machine tool designer has three different functional design scenarios (also referred to as manufacturer’s business scenarios in this book).

While considering reliability and maintenance at the design stage of a machine tool, each of the above three functional design scenarios offers different opportunities and challenges to the designer. For example, a general purpose machine tool design must be able to meet the reliability requirements of a wide range of users. On the other hand, the designer of a special purpose machine tool must be able to capture reliability requirements of a specific user. Therefore, a reliability- and maintenance-based design approach for machine tools must also be able to address the design needs in each of these functional design scenarios. Figure 1.2 depicts the entire concept of reliability- and maintenance-based design of machine tools. Many times, the existing alternatives available to the designer for some of the components/subassemblies may not be able to give satisfactory performance under the specified operating environment of the users. The designer may then need to improve the existing design of such components/subassemblies. For example, one of the main causes of failure of workhead spindle is seal failure, thereby allowing the coolant and chips to enter into the spindle bearing and causing it to fail early. In this case, the designer may have to change the design of the spindle to accommodate a different sealing technology such that it restricts coolant entry or provides better chip separation arrangement.

Figure 1.2 Reliability- and maintenance-based design of machine tools.

Similarly, users may also be interested in improving the reliability performance of their existing machines. Users can do this in two ways:

1. By improving the design of components/subassemblies in collaboration with machine tool manufacturer. The designer can search for a better alternative design for the critical components/subassemblies of the machine.

2. By changing the operating environment, shop-floor policies and cost structure. For example, users may introduce more stringent Statistical Process Control (SPC) procedures to reduce the cost of failures through early detection, thereby reducing the criticality of the failures.

However, any improvement effort needs investment. The designer and users need to make a trade-off between the cost of improvements and benefits from the improvements. Thus, an approach for considering reliability and maintenance at the design stage will also help in making such decisions.

1.4 Machine Tool Reliability: User’s View Point

Users of machine tools are other manufacturing industries, which use them for producing consumer or capital goods and are under continuous pressure to meet their customers’ requirements of high quality, low cost and timely delivery of products. Failures of production equipment affect the shop-floor level performance of the users. Thus, the users evaluate the reliability of a machine tool based on how well it performs in their production environment to meet their customers’ requirements. Moreover, shop-floor level performance also depends on the operations policy pertaining to scheduling, maintenance and quality. Furthermore, these three aspects of operations planning are affected by machine tool failures and also have some interaction effect, and hence joint consideration of various policy options pertaining to quality, maintenance and scheduling, along with their effect on the performance of manufacturing systems, are important areas of investigation.

Only in recent years have researchers started to develop approaches that try to simultaneously optimize their parameters [12–16]. For example, older approaches for production scheduling do not consider the effect of machine unavailability due to failures or preventive maintenance activities. Similarly, the older maintenance planning models did not consider the impact of maintenance on due dates to meet customer requirements. However, maintenance effectiveness cannot be measured in a meaningful way without taking into account whether the maintenance addresses the production requirements [17]. Delaying the maintenance actions to meet the production requirement may increase the process variability and risk of machine failure, which in turn may cause higher rejections or downtime losses. Ollila and Malmipuro [18] observed that maintenance has a major impact on efficiency and quality along with equipment availability. In a case study carried out in five Finnish industries, it was shown that well-functioning machinery is a prerequisite to quality products. It was also shown that a lack of proper maintenance is usually among the three most important causes of quality deficiencies.

Due to the operational complexity and the presence of deterministic and stochastic events, obtaining optimal policies for manufacturing systems is both theoretically and computationally difficult. The literature as well as input from industries has clearly indicated the need to explore the problem of joint consideration of these shop-floor operational aspects. Thus, from the point of view of users, reliability of machine tools can be used to model the interaction of various shop-floor level operations policies. Figure 1.3 depicts the concept of interaction of reliability of machine tool and various shop-floor operations planning like scheduling, maintenance and quality control.

Figure 1.3 User’s view of machine tool reliability.

1.5 Organization of the Book

The rest of the book is organized as follows: The second chapter presents a brief overview of the basic reliability mathematics. It presents a discussion on some of the most common lifetime distributions, viz., exponential, Weibull, Normal, etc. In the third chapter, various performance measures for machine tool reliability are discussed and detailed models are developed for each of these measures. These models relate a machine tool’s reliability and maintenance parameters with the user’s cost structure and shop-floor level operational parameters. To be more specific, models for availability, performance rate, quality rate, overall equipment effectiveness, life cycle costs and cost per piece are developed. The chapter also provides a discussion on the use of such models from the user’s and manufacturer’s point of view. Models developed in Chapter 3 rely on time-to-failure distribution parameters for estimating the number of failures for a component in a given time interval. If sufficient filed failure data are available, the designer can use the conventional methods mentioned in Chapter 2 to estimate the time-to-failure distribution parameters. However, in many of the real life situations, designers do not have sufficient field failure data. For such cases, Chapter 4 explores the possibility of utilizing expert knowledge for obtaining time-to-failure distribution parameters. Chapter 5 discusses various maintenance scenarios for machine tools. The following three maintenance scenarios are identified for a machine tool based on the types of preventive maintenance actions and the degree of restoration after a repair:

Perfect corrective and preventive replacement;

Imperfect corrective repair, imperfect preventive repair and perfect replacement;

Minimal corrective repair, imperfect preventive repair, imperfect overhaul and perfect replacement.

For each scenario, a maintenance optimization problem is formulated. For the case of imperfect maintenance, complexities in obtaining the optimal preventive maintenance schedule are reduced by developing some approximate models for estimating the number of failures. For the case of minimal corrective repair, a conditional number of failures model is discussed. This model, apart from regular preventive repair and replacement, also helps the designer in considering the effect of major overhauls on the optimal maintenance schedule decisions. It is demonstrated in the chapter that the optimal maintenance schedule decision also depends on the user’s cost structure and shop-floor policy parameters. In Chapter 6, two methods for reliability-based design of machine tools are provided. The first design methodology allows selection of optimal machine tool configuration based on Life Cycle Cost (LCC) and other performance requirements of the user. The optimal solution is obtained by simultaneously considering reliability and maintenance at the design stage under three different functional design scenarios, viz., general purpose machine tool design, special purpose machine tool design and customized machine tool design. The second design methodology helps the designer in improving the design of the existing system by identifying the critical components/subassemblies. A cost-based Failure Consequence Analysis (FCA) is proposed for this purpose. The proposed methodology can help a machine tool manufacturer in making effective cost-driven decisions while improving the reliability performance of the machine tool. It also provides guidance to the machine tool users in identifying the areas where they can focus to obtain better performance from the machine.

Chapter 7 and Chapter