117 Impacts7. Impacts of Automation on of Precision Au Alkan Donmez, Johannes A. Soons

7.4 Cost and Benefits of Precision...... 119 Automation has significant impacts on the econ- omy and the development and use of technology. 7.5 Measures of Precision ...... 120 In this chapter, the impacts of automation on precision, which also directly influences science, 7.6 Factors That Affect Precision ...... 120 technology, and the economy, are discussed. As 7.7 Specific Examples and Applications automation enables improved precision, precision in Discrete Part Manufacturing ...... 121 also improves automation. 7.7.1 Evolution of Numerical Control Following the definition of precision and the and Its Effects on Machine Tools factors affecting it, the relationship between pre- and Precision ...... 121 cision and automation is described. This chapter 7.7.2 Enablers to Improve Precision concludes with specific examples of how automa- of Motion ...... 122 tion has improved the precision of manufacturing 7.7.3 Modeling and Predicting processes and manufactured products over the last Machine Behavior and Machining .. 122 decades. 7.7.4 Correcting Machine Errors...... 122 7.7.5 Closed-Loop Machining (Automation-Enabled Precision) .... 123 7.1 What Is Precision? ...... 117 7.7.6 Smart Machining...... 124 7.2 Precision as an Enabler of Automation ... 118 7.8 Conclusions and Future Trends ...... 124 7.3 Automation as an Enabler of Precision ... 119 References ...... 125

7.1 What Is Precision? Precision is the closeness of agreement between a se- In this definition, the specified conditions describe ries of individual measurements, values or results. For whether precision is associated with the repeatabil- amanufacturingprocess,precisiondescribeshowwell ity or the reproducibility of the measurement process. A Part the process is capable of producing products with iden- Repeatability is the closeness of agreement between re- tical properties. The properties of interest can be the sults of successive measurements of the same quantity dimensions of the product, its shape, surface finish, carried out under the same conditions. These repeata- 7 color, weight, etc. For a device or instrument, precision bility conditions include the measurement procedure, describes the invariance of its output when operated observer, instrument, environment, etc. Reproducibil- with the same set of inputs. Measurement precision is ity is the closeness of the agreement between results of defined by the International Vocabulary of Metrology measurements carried out under changed measurement as the [7.1]: conditions. In computer science and mathematics, pre- cision is often defined as a measure of the level of detail ...closeness of agreement between indications ob- of a numerical quantity. This is usually expressed as the tained by replicate measurements on the same or number of bits or decimal digits used to describe the similar objects under specified conditions. quantity. In other areas, this aspect of precision is re-

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ferred to as resolution: the degree to which nearly equal with the true value due to the infinite amount of infor- values of a quantity can be discriminated, the smallest mation required to describe the measurand completely. measurable change in a quantity or the smallest con- To the extent that it leaves room for interpretation, the trolled change in an output. incomplete definition of the measurand introduces un- Precision is a necessary but not sufficient condition certainty in the result of a measurement, which may for accuracy. Accuracy is defined as the closeness of or may not be significant relative to the accuracy re- the agreement between a result and its true or intended quired of the measurement; for example, suppose the value. For a manufacturing process, accuracy describes measurand is the thickness of a sheet of metal. If this the closeness of agreement between the properties of thickness is measured using a micrometer caliper, the the manufactured products and the properties defined result of the measurement may be called the best esti- in the product design. For a measurement, accuracy is mate of the true value (true in the sense that it satisfies the closeness of the agreement between the result of the definition of the measurand.) However, had the mi- the measurement and a true value of the measurand – crometer caliper been applied to a different part of the the quantity to be measured [7.1]. Accuracy is affected sheet of material, the realized quantity would be differ- by both precision and bias. An instrument with an in- ent, with a different true value [7.2]. Thus the lack of correct calibration table can be precise, but it would information about where the thickness is defined intro- not be accurate. A challenge with the definition of ac- duces an uncertainty in the true value.Atsomelevel, curacy is that the true value is a theoretical concept. every measurand or product design has such an intrinsic In practice, there is a level of uncertainty associated uncertainty.

7.2 Precision as an Enabler of Automation Historically, precision is closely linked to automa- quette de Gribeauval promoted the use of standardized tion through the concept of parts interchangeability. parts for key military equipment such as gun carriages In more recent times, it can be seen as a key en- and muskets. He realized that interchangeable parts abler of lean manufacturing practices. Interchangeable would enable faster and more efficient manufacturing, parts are parts that conform to a set of specifications while facilitating repairs in the field. The development that ensure that they can substitute each other. The was enabled by the introduction of two-dimensional concept of interchangeable parts radically changed the mechanical drawings, providing a more accurate ex- manufacturing system used in the first phase of the pression of design intent, and increasingly accurate Industrial Revolution, the English system of manu- gauges and templates (jigs), reducing the craftsman’s facturing. The English system of manufacturing was room for deviations while allowing for lower skilled based on the traditional artisan approach to making labor. In 1778, master gunsmith Honoré Blanc pro- aproduct.Typically,askilledcraftsmanwouldmanu- duced the first set of musket locks completely made facture an individual product from start to finish before from interchangeable parts. He demonstrated that the moving onto the next product. For products consisting locks could be assembled from parts selected at random. atA Part of multiple parts, the parts were modeled, hand-fitted, Blanc understood the need for a hierarchy in measure- and reworked to fit their counterparts. The craftsmen ment standards through the use of working templates had to be highly skilled, there was no automation, and for the various pieces of the lock and master copies to 7.2 production was slow. Moreover, parts were not inter- enable the reconstruction of the working templates in changeable. If a product failed, the entire product had to the case of loss or wear [7.3]. The use of semiskilled be sent to an expert craftsman to make custom repairs, labor led to strong resistance from both craftsmen and including fabrication of replacement parts that would fit the government, fearful of the growing independence their counterparts. of manufacturers. In 1806, the French government re- Pioneering work on interchangeable parts occurred verted back to the old system, using the argument that in the printing industry (movable precision type), clock workers who do not function as a whole cannot produce and watch industry (toothed gear wheels), and ar- harmonious products. mories (pulley blocks and muskets) [7.3]. In the mid Thomas Jefferson, a friend of Blanc, promoted to late 18th century, French General Jean Baptiste Va- the new approach in the USA. Here the ideas led to

Springer Handbook of Automation 1 Nof (Ed.) • ! Springer 2009 Impacts of Automation on Precision 7.4 Cost and Benefits of Precision 119 the American system of manufacturing. The Ameri- Precision remains a key requirement for automa- can system of manufacturing is characterized by the tion. Precision eliminates fitting and rework, enabling sequential application of specialized machinery and automated assembly of parts produced across the globe. templates (jigs) to make large quantities of identical Precision improves agility by increasing the range of parts manufactured to a tolerance (see, e.g., [7.4]). tasks that unattended manufacturing equipment can Interchangeable parts allow the separation of parts pro- accomplish, while reducing the cost and time spent duction from assembly, enabling the development of on production trials and incremental process improve- the assembly line. The use of standardized parts fur- ments. Modern manufacturing principles such as lean thermore facilitated the replacement of skilled labor manufacturing, agile manufacturing, just-in-time manu- and hand tools with specialized machinery, resulting facturing, and zero-defect manufacturing cannot exist in the economical and fast production of accurate without manufacturing processes that are precise and parts. well characterized. The American system of manufacturing cannot ex- Automated agile manufacturing, for example, is de- ist without precision and standards. Firstly, the system pendent upon the solution of several precision-related requires a unified, standardized method of defining technical challenges. Firstly, as production machines nominal part geometry and tolerances. The tolerances become more agile, they also become more complex, describe the maximum allowed deviations in actual yet precision must be maintained or improved for each part geometry and other properties that ensure proper of the increasing number of tasks that a machine can functioning of the part, including interchangeability. perform. The design, maintenance, and testing of these Secondly, the system requires a quality control system, machines becomes more difficult as the level of agility including sampling and acceptance rules, and gauges increases. Secondly, the practice of trial runs and itera- calibrated to a common standard to ensure that the tive accuracy improvements is not cost-effective when parts produced are within tolerance. Thirdly, the system batch sizes decrease and new products are introduced requires manufacturing processes capable of realizing at increasing speeds. Instead, the first and every part parts that conform to tolerance. It is not surprising that have to be produced on time and within tolerance. Ac- the concept of interchangeable parts first came into cordingly, the characterization and improvement of the widespread use in the watchmakers’ industry, an area precision of each manufacturing process becomes a key used to a high level of accuracy [7.5]. requirement for competitive automated production.

7.3 Automation as an Enabler of Precision As stated by Portas, random results are the conse- variations or application of temperature sensors and al- quence of random procedures [7.6]. In general, random gorithms to compensate thermal errors in the instrument results appear to be random due to a lack of under- reading. standing of cause-and-effect relationships and a lack Automation has proven to be very effective in of resources for controlling sources of variability; for eliminating or minimizing variability. Automation re- example, an instrument may generate a measurement duces variability associated with human operation. result that fluctuates over time. Closer inspection may Automation furthermore enables control of instruments, A Part reveal that the fluctuations result from environmental processes, and machines with a bandwidth, complexity, temperature variations that cause critical parts of the in- and resolution unattainable by human operators. While strument to expand and deform. The apparent random humans plan and supervise the operation of machines 7.4 variations can thus be reduced by tighter environmental and instruments, the craftsmanship of the operator is no temperature control, use of design principles and mater- longer a dominant factor in the actual manufacturing or ials that make the device less sensitive to temperature inspection process.

7.4 Cost and Benefits of Precision Higher precision requires increased efforts to reduce tolerances are therefore more difficult to manufacture sources of variability or their effect. Parts with tighter and more expensive to produce. In general, there is

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abeliefthatthereexistsanearlyexponentialrela- pair (e.g., improved interchangeability of parts), and tionship between cost and precision, even when new opportunities for fewer and smaller parts; for ex- equipment is not needed. However, greater precision ample, the improvements in the reliability and fuel does not necessarily imply higher cost when the total efficiency of automobiles have to a large extent been manufacturing enterprise, including the final product, is enabled by increases in the precision of manufacturing examined [7.7, 8]. processes and equipment. The benefits of higher pre- The benefits of higher precision can be separated cision for manufacturing include lower assembly cost into benefits for product quality and benefits for manu- (less selective assembly, elimination of fitting and re- facturing. Higher precision enables new products and work, automated assembly), better interchangeability new product capabilities. Other benefits are better prod- of parts sourced from multiple suppliers, lower in- uct performance (e.g., longer life, higher loads, higher ventory requirements, less time and cost spend on efficiency, less noise and wear, and better appearance trial production, fewer rejects, and improved process and customer appeal), greater reliability, easier re- consistency.

7.5 Measures of Precision To achieve precision in a process means that the out- relation to specified tolerance come of the process is highly uniform and predictable Cp (U L)/6σ , (7.1) over a period of time. Since precision is an attribute = − of a series of entities or process outcomes, statisti- where U is the upper specification limit, L is the lower cal methods and tools are used to describe precision. specification limit, σ is the standard deviation of the dis- Traditional statistical measures such as mean and stan- persion (note that in the above equation 6σ corresponds dard deviation are used to describe the average and to the reference interval of the dispersion for normal dispersion of the characteristic parameters. Interna- distribution; for other types of distribution the reference tional standards and technical reports provide guidance interval is determined based on the well-established sta- about how such statistical measures are applied for un- tistical methods). derstanding of the short-term and long-term process The critical process capability index Cpk also behavior and for management and continuous improve- known as the minimum process capability index,de- ment of processes [7.9–12]. scribes the relationship between the proximity of the Statistical process control is based on a comparison mean process parameter of interest to the specified tol- of current data with historical data. Historical data is erance used to build a model for the expected process behavior, Cpk min(CpkL, CpkU) , (7.2) including control limits for measurements of the output = where of the process. Data is then collected from the process and compared with the control limits to determine if the CpkU (U µ)/3σ (7.3) = − process is still behaving as expected. Process capabil- and atA Part ity compares the output of an in-control process to the CpkL (µ L)/3σ , (7.4) specification limits of the requested task. The process = − capability index, Cp,describestheprocesscapabilityin and µ is the mean of the process parameter of interest. 7.6

7.6 Factors That Affect Precision In case of manufacturing processes, there are many fac- ambient temperature changes over time or temperature tors that affect the precision of the outcome. They are gradients in space cause changes in performance of associated with expected and unexpected variations in manufacturing equipment, which in turn causes vari- environment, manufacturing equipment, and process as ation in the outcome [7.13, 14]. Similarly, variations well as the operator of the equipment; for example, in workpiece material such as local hardness varia-

Springer Handbook of Automation 1 Nof (Ed.) • ! Springer 2009 Impacts of Automation on Precision 7.7 Specific Examples and Applications in Discrete Part Manufacturing 121 tions, residual stresses, deformations due to clamping tions due to thermal deformations, static and dynamic or process-induced forces contribute to the variations in compliances, influences of foundations, and ineffective critical parameters of finished product. Process-induced maintenance are the contributors to the variations in variations include wear or catastrophic failures of cut- product critical parameters. Finally, variations caused ting tools used in the process, thermal variations due by the operator of the equipment due to insufficient to the interaction of coolant, workpiece, and the cutting training, motivation, care or information needed con- tool, as well as variations in the set locations of tools stitute the largest source of unexpected variations and used in the process (e.g., cutting tool offsets). In the therefore impact on the precision of the manufacturing case of manufacturing equipment, performance varia- process.

7.7 Specific Examples and Applications in Discrete Part Manufacturing The effect of automation on improving of precision generation of machine tools introduced capabilities to of discrete part manufacturing can be observed in move the cutting tool along a path by tracing a tem- many applications such as improvements in fabrica- plate using mechanical or hydraulic mechanisms, thus tion, assembly, and inspection of various components reducing reliance on operator competence [7.16]. On for high-value products. In this Section, one specific the other hand, creating accurate templates was still perspective is presented using the example of ma- amainobstacletoachievingcost-effectiveprecision chine tools as the primary means of precision part manufacturing. fabrication. Around the late 1940s the US Air Force needed more precise parts for its high-performance (faster, 7.7.1 Evolution of Numerical Control and Its highly maneuverable, and heavier) aircraft program Effects on Machine Tools and Precision (in the late 1940s the target was around 0.075 mm). There was no simple way to make wing± panels to The development of numerically controlled machines meet the new accuracy specifications. Manufacturing represents a major revolution of automation in manu- research community and industry had come up with facturing industry. Metal-cutting machine tools are used asolutionbyintroducingnumericalcontrolautoma- to produce parts by removing material from a part tion to general-purpose machine tools. In 1952, the blank,ablockofrawmaterial,accordingtothefinal first numerically controlled three-axis milling machine desired shape of that part. In general, machine tools utilizing a paper tape for programmed instructions, consist of components that hold the workpiece and vacuum-tube electronics, and relay-based memory was the cutting tool. By providing relative motion between demonstrated by the Servomechanism Laboratory of these two, a machine tool generates a cutting tool path the MIT [7.17]. This machine was able to move three which in turn generates the desired shape of the work- axes in coordinated fashion with a speed of about piece out of a part blank. In early-generation machine 400 mm/min and a control resolution of 1.25 µm. The tools, the cutting tool motion is controlled manually automation of machine tools was so effective in im- (by crank wheels rotating the leadscrews), therefore the proving the accuracy and precision of complex-shaped A Part quality of the workpiece was mostly the result of the aircraft components that by 1964 nearly 35 000 nu- competence of the operator of the machine tool. Be- merically controlled machine tools were in use in the fore the development of numerically controlled machine USA. 7.7 tools, complex contoured parts were made by drilling Automation of machine tools by numerical control closely spaced holes along the desired contour and led to reduction of the need for complex fixtures, tool- then manually finishing the resulting surface to obtain ing, masters, and templates and replaced simple clamps, aspecifiedsurfacefinish.Thisprocesswasverytime resulting in significant savings by industry. This was consuming and prone to errors in locating the holes, most important for complex parts where human error which utilized cranks and leadscrews to control the or- was likely to occur. With numerical control, once the thogonal movements of the work table manually; for control program was developed and checked for accu- example, the best reported accuracy of airfoil shapes racy, the machine would work indefinitely making the using such techniques was 0.175 mm [7.15]. Later same parts without any error. ±

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7.7.2 Enablers to Improve Precision of changing thermal conditions within the structures of Motion as well as in the production environment still result in undesired variations, leading to reduced precision of Numerically controlled machine tools rely on sensors products. that detect positions of each machine component and Specifically, machine tools are composed of mul- convert them into digital information. Digital position tiple slides, rotary tables, and rotary joints, which are information is used in control units to control actua- usually assembled on top of each other, each designed tors to position the cutting tool properly with respect to to move along a single axis of motion, providing ei- the workpiece being cut. The precision of such motion ther a translational or a rotational degree of freedom. is determined by the resolution of the position sensor In reality, each moving element of a machine tool has (feedback device), the digital control algorithm, and the error motions in six degrees of freedom, three transla- mechanical and thermal behavior of the machine struc- tions, and three rotations (Fig. 7.1). Depending on the tural elements. Note that, contrary to manual machine number of axes of motion, a machine tool can there- tools, operator skill, experience, and dexterity are not fore have as many as 30 individual error components. part of the determining factors for the precision of mo- Furthermore, the construction of moving slides and tion. With proper design and environmental controls, it their assemblies with respect to each other introduce has been demonstrated that machine tools with numeri- additional error components such as squareness and par- cal control can achieve levels of precision on the order allelism between axes of motion. of 1 µmorless[7.18, 19]. Recognizing the significant benefits of automation provided by numerical control in eliminating random 7.7.3 Modeling and Predicting procedures and thus random behavior, in the last five Machine Behavior and Machining decades many researchers have focused on understand- ing the fundamental deterministic behavior of error In most material-removal-based manufacturing pro- motions of machine tools caused by geometric and ther- cesses, the workpiece surfaces are generated as a time mal influences such that they can be compensated by record of the position of the cutting tool with re- numerical control functions [7.20–22]. With the ad- spect to the workpiece. The instantaneous position of vances of robotics research in the 1980s, kinematic the tool with respect to the workpiece is generated modeling of moving structures using homogeneous by the multiple axes of the manufacturing equipment transformation matrices became a powerful tool for moving in a coordinated fashion. Although the intro- programming and controlling robotic devices [7.23]. duction of numerical control (NC)andlatercomputer Following these developments and assuming rigid- numerical control (CNC)removedthemainsourceof body motions, a general methodology for modeling variation in part quality – manual setups and operations geometric machine tool errors was introduced using –thecomplexstructuralnatureofmachinesprovid- homogeneous transformation matrices to define the re- ing multi-degree-of-freedom motion and the influence lationships between individual error motions and the resulting position and orientation of the cutting tool with respect to the workpiece [7.24]. Kinematic mod- z-axis els were further improved to describe the influences of Vertical straightness atA Part of x-axis the thermally induced error components of machine tool Pitch y-axis Yaw motions [7.25, 26]. Horizontal straightness 7.7 of x-axis 7.7.4 Correcting Machine Errors

Automation of machine tool operation by computer numerical control and the modeling of machine tool Roll systematic errors led to the creation of new hardware and software error compensation technologies enabling improvement of machine tool performance. Machine er- x-axis ror compensation in the form of leadscrew pitch errors Linear displacement has been available since the early implementations of Fig. 7.1 Six error components of a machine slide CNC.Suchleadscrewerrorcompensationiscarriedout

Springer Handbook of Automation 1 Nof (Ed.) • ! Springer 2009 Impacts of Automation on Precision 7.7 Specific Examples and Applications in Discrete Part Manufacturing 123 using error tables in the machine controller. When exe- cuting motion commands, the controller accesses these a) Software-based error compensation tables to adjust target positions used in motion servo CNC Position algorithms (feedforward control). The leadscrew error command Position control Drive compensation tables therefore provide one-dimensional software motor error compensation. Modern machine controllers have more sophisticated compensation tables enabling two- or three-dimensional error compensation based on preprocess measurement of error motions. For more Error Position general error compensation capabilities, researchers Error calculation Position feedback have developed other means of interfacing with the con- computer device trollers. One approach for such an interface was through hardware modification of the communication between the controller and the position feedback devices [7.27]. b) Hardware-based error compensation In this case, the position feedback signals are diverted CNC to an external microcomputer, where they are counted Position command Position control Drive to determine the instantaneous positions of the slides, software motor and corresponding corrections were introduced by mod- ifying the feedback signals before they are read by the machine controller. Similarly, the software approaches Error to error compensation were also implemented by inter- facing with the CNC through the controller executive Error calculation Real-time Position feedback software and regular input/output (I/O)devices(such error as parallel I/O)[7.28]. Generic functional diagrams computer corrector device depicting the two approaches are shown in Fig. 7.2a and b. Position Real-time error compensation of geometric and thermally induced errors utilizing automated features of Fig. 7.2a,b Hardware and software error compensation ap- machine controllers has been reported in the literature to proaches: (a) software-based error compensation and (b) hardware- improve the precision of machine tools by up to an order based error compensation of magnitude. Today’s commercially available CNCs employ some of these technologies and cost-effectively the customers. Automation of machining, machine er- transfer these benefits to the manufacturing end-users. ror correction, and part inspection processes have led to new quality control strategies in which real-time control 7.7.5 Closed-Loop Machining of processes is possible based on real-time information (Automation-Enabled Precision) about the machining process and equipment and the resulting part geometries. Beyond just machine tool control through CNC, In the mid 1990s, the Manufacturing Engineering automation has made significant inroads into manu- Laboratory of the National Institute of Standards and A Part facturing operations over the last several decades. Technology demonstrated such an approach in a re- From automated inspection using dedicated measur- search project called Quality In Automation [7.29]. ing systems (such as go/no-go gauges situated next Aquality-controlarchitecturewasdevelopedthatcon- 7.7 to the production equipment) to more flexible and sisted of three control loops around the machining general-purpose inspection systems (such as coordi- process: real-time, process-intermittent, and postpro- nate measuring machines) automation has improved the cess control loops (Fig. 7.3). quality control of manufacturing processes, thereby en- The function of the real-time control loop was abling more precise production. to monitor the machine tool and the machining pro- Automation has even changed the paradigm of tra- cess and to modify the cutting tool path, feed rate, ditional quality control functions. Traditionally, the and spindle speed in real time (based on models de- function of quality control in manufacturing has been veloped ahead of time) to achieve higher workpiece the prevention of defective products being shipped to precision. The function of the process-intermittent con-

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However, machining systems still require relatively Quality controller long periods of trial-and-error processing to produce Data base agivennewproductoptimally.Machinetoolsstilloper- ate with NC programs, which provide the design intent On machine Sensors of a product to be machined only partially at best. They probes Machine tool have no information about the characteristics of the controller material to be machined. They require costly periodic Process Real maintenance to avoid unexpected breakdowns. These Post-process Pre-process intermittent time Machine deficiencies increase cost and time to market, and re- tool duce productivity. Coordinate Smart machining systems are envisioned to be capa- measuring ble of self-recognition, monitoring, and communication machine of their capabilities; self-optimization of their opera- tions; self-assessment of the quality of their own work; and self-learning for performance improvement over time [7.30]. The underlying technologies are currently Fig. 7.3 Amultilayeredquality-controlarchitectureforimplement- being developed by various research and develop- ing closed-loop machining ment organizations; for example, a robust optimizer developed at the National Institute of Standards and trol loop was to determine the workpiece errors caused Technology demonstrated a way to integrate machine by the machining process, such as errors caused by tool tool performance information and process models with deflection during machining, and to correct them by their associated uncertainties to determine the optimum automatically generating a modified NC program for operating conditions to achieve a particular set of ob- finishing cuts. Finally, the postprocess control loop was jectives related to product precision, cycle time, and used to validate that the machining process was under cost [7.31–33]. New sets of standards are being de- control and to tune the other two control loops by de- veloped to define the data formats to communicate tecting and correcting the residual systematic errors in machine performance information and other machine the machining system. characteristics [7.34, 35]. New methods to determine material properties under machining conditions (high 7.7.6 Smart Machining strain rates and high temperatures) were developed to improve the machining models that are used in ma- Enabled by automation, the latest developments in chining optimization [7.36]). New signal-processing machining are leading the technology towards the re- algorithms are being developed to monitor the condi- alization of autonomous, smart machining systems. As tion of machine spindles and predict failures before described in the paragraphs above, continuous improve- catastrophic breakdowns. It is expected that in the next ments in machining systems through NC and CNC as 5–10years smart machining systems will be available well as the implementations of various sensing and in the marketplace, providing manufacturers with cost- control technologies have responded to the continu- effective means of achieving high-precision products atA Part ous needs for higher-precision products at lower costs. reliably.

7.8 7.8 Conclusions and Future Trends Automation is a key enabler to achieve cost-effective, lead to high-quality products are only made possible by high-quality products and services to drive society’s ahighdegreeofautomationofthemanufacturingpro- economical engine. The special duality relationship be- cesses. This is one of the reasons for the drive towards tween automation and precision (each driving the other) more manufacturing automation even in countries with escalate the effectiveness of automation in many fields. low labor costs. The examples provided in this chapter In this chapter this relationship was described from can easily be extended to other economic and techno- arelativelynarrowperspectiveofdiscretepartfabri- logical fields, demonstrating the significant effects of cation. Tighter tolerances in product components that automation.

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Recent trends and competitive pressures indicate and technical capabilities, producers are developing that more knowledge has been generated about pro- more complex, high-value products with smaller num- cesses, which leads to the reduction of apparent bers of components and subassemblies. This trend leads nonsystematic variations. With increased knowledge to even more automation with less cost.

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7.1 ISO/IEC Guide 99: International Vocabulary of 7.16 D.B. Dallas: Tool and Manufacturing Engineers Metrology – Basic and General Concepts and As- Handbook,3rdedn.(SocietyofManufacturingEn- sociated Terms (VIM) (International Organization for gineers, McGraw-Hill, New York 1976) Standardization, Genewa 2007) 7.17 J.F. Reintjes: Numerical Control: Making a New Tech- 7.2 ISO/IEC Guide 98: Guide to the Expression of nology (Oxford Univ. Press, New York 1991) Uncertainty in Measurement (GUM) (Interna- 7.18 R. Donaldson, S.R. Patterson: Design and con- tional Organization for Standardization, Genewa struction of a large vertical-axis diamond turning 1995) machine, Proc. SPIE 27th Annu. Int. Tech. Symp. In- 7.3 C. Evans: Precision Engineering: An Evolutionary strum. Disp., Vol. 23 (Lawrence Livermore National View (Cranfield Univ. Press, Cranfield 1989) Laboratory, 1983), Report UCRL-89738 7.4 D.A. Hounshell: From the American System to Mass 7.19 N. Taniguchi: The state of the art of nanotechnol- Production, 1800–1932: the Development of Manu- ogy for processing of ultraprecision and ultrafine facturing Technology in the United States (Johns products, Prec. Eng. 16(1), 5–24 (1994) Hopkins Univ. Press, Baltimore 1984) 7.20 R. Schultschick: The components of volumetric ac- 7.5 D. Muir: Reflections in Bullough’s Pond; Economy curacy, Ann. CIRP 25(1), 223–226 (1972) and Ecosystem in New England (Univ. Press of New 7.21 R. Hocken, J.A. Simpson, B. Borchardt, J. Lazar, England, Lebanon 2000) C. Reeve, P. Stein: Three dimensional metrology, 7.6 J.B. Bryan: The benefits of brute strength, Prec. Eng. Ann. CIRP 26,403–408(1977) 2(4), 173 (1980) 7.22 V.T. Portman: Error summation in the analytical cal- 7.7 J.B. Bryan: Closer tolerances – economic sense, Ann. culation of the lathe accuracy, Mach. Tool. 51(1), 7–10 CIRP 19(2), 115–120 (1971) (1980) 7.8 P.A. McKeown: Higher precision manufacturing and 7.23 R.P. Paul: Robot Manipulators: Mathematics, Pro- the British economy, Proc. Inst. Mech. Eng. 200(B3), gramming, and Control (MIT Press, Cambridge 1981) 147–165 (1986) 7.24 M.A. Donmez, C.R. Liu, M.M. Barash: A general- 7.9 ISO/DIS 26303-1: Capability Evaluation of Machining ized mathematical model for machine tool errors, Processes on Metal-Cutting Machine Tools (Inter- modeling, sensing, and control of manufacturing national Organization of Standardization, Genewa processes, Proc. ASME Winter Annu. Meet., PED, 2008) Vol. 23 (1986) 7.10 ISO 21747: Statistical Methods – Process Perfor- 7.25 R. Venugopal, M.M. Barash: Thermal effects on the mance and Capability Statistics for Measured Quality accuracy of numerically controlled machine tools, Characteristics (International Organization for Stan- Ann. CIRP 35(1), 255–258 (1986) dardization, Genewa 2006) 7.26 J.S. Chen, J. Yuan, J. Ni, S.M. Wu: Thermal error mod- 7.11 ISO 22514-3: Statistical Methods in Process Man- eling for volumetric error compensation, Proc. ASME agement – Capability and Performance – Part 3: Winter Annu. Meet., PED, Vol. 55 (1992) Machine Performance Studies for Measured Data on 7.27 K.W. Yee, R.J. Gavin: Implementing Fast Probing and

Discrete Parts (International Organization for Stan- Error Compensation on Machine Tools, NISTIR 4447 A Part dardization, Genewa 2008) (The National Institute of Standards and Technology, 7.12 ISO/TR 22514-4: Statistical Methods in Process Man- Gaithersburg 1990) agement – Capability and Performance – Part 7.28 M.A. Donmez, K. Lee, R. Liu, M. Barash: A real-time 7 4: Process Capability Estimates and Performance error compensation system for a computerized nu- Measures (International Organization for Standard- merical control turning center, Proc. IEEE Int. Conf. ization, Genewa 2007) Robot. Autom. (1986) 7.13 ASME B89.6.2: Temperature and Humidity Environ- 7.29 M.A. Donmez: Development of a new quality con- ment for Dimensional Measurement (The American trol strategy for automated manufacturing, Proc. Society of Mechanical Engineers, New York 1973) Manufact. Int. (ASM, New York 1992) 7.14 J.B. Bryan: International status of thermal error re- 7.30 L. Deshayes, L. Welsch, A. Donmez, R. Ivester, search, Ann. CIRP 39(2), 645–656 (1990) D. Gilsinn, R. Rhorer, E. Whitenton, F. Potra: Smart 7.15 G.S. Vasilah: The advent of numerical control 1951– machining systems: issues and research trends, 1959, Manufact. Eng. 88(1), 143–172 (1982) Proc. 12th CIRP Life Cycle Eng. Semin. (Grenoble 2005)

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7.31 L. Deshayes, L.A. Welsch, R.W. Ivester, M.A. Donmez: 7.34 ASME B5.59-1 (Draft): Information Technology for Robust optimization for smart machining system: an Machine Tools – Part 1: Data Specification for Ma- enabler for agile manufacturing, Proc. ASME IMECE chine Tool Performance Tests (American Society of (2005) Mechanical Engineers, New York 2007) 7.32 R.W. Ivester, J.C. Heigel: Smart machining systems: 7.35 ASME B5.59-2 (Draft): Information Technology for robust optimization and adaptive control optimiza- Machine Tools – Part 2: Data Specification for Prop- tion for turning operations, Trans. North Am. Res. erties of Machine Tools for Milling and Turning Inst. (NAMRI)/SME, Vol. 35 (2007) (American Society of Mechanical Engineers, New York 7.33 J. Vigouroux, S. Foufou., L. Deshayes, J.J. Filliben, 2007) L.A. Welsch, M.A. Donmez: On tuning the design of 7.36 T. Burns, S.P. Mates, R.L. Rhorer, E.P. Whitenton, an evolutionary algorithm for machining optimiza- D. Basak: Recent results from the NIST pulse-heated tion problem, Proc. 4th Int. Conf. Inf. Control (Angers Kolsky bar, Proc. 2007 Annu. Conf. Expo. Exp. Appl. 2007) Mech. (Springfield 2007) atA Part 7

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