Process Layout and Integration into the Process Chain

Abstract

A process, such as the manufacturing of a component by cutting or abrasive machining is system-technically defined as the transformation of input variables of a system into corresponding output variables in the sense of system engineering.

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

A process, such as the manufacturing of a component by cutting or abrasive machining is system-technically defined as the transformation of input variables of a system into corresponding output variables in the sense of system engineering. At this, the transformation represents a development relative to the reference criterion “time”. The cutting process is already described as a system in Sect. 1.4.

This chapter begins with explaining definitions and correlations around the term “process chains”. On that basis, the theoretical basics of process and process chain layout are described by means of simple examples. Section 15.5 “process monitoring” demonstrates ways to monitor the process quality and take influence on the machining process.

15.1 Basics of Process Chain Layout

To understand the approach in process chain layout, it is initially necessary to delimit the terms “process”, “process chain element” and “process chain” contentwise. The PEC model shown in Fig. 15.1 gives an overview of the terms used in the following.

Fig. 15.1

Definition of the term process by the PEC model

The PEC model is divided into three levels. At this, the process (P) represents the smallest and thus indivisible unit of a process chain. Input and output variables are part of a process. A manufacturing operation such as, for example the generation of a hole with a tool, is called a process.

The process chain element (E) is defined as a sequential line up of single processes. A parallel configuration of processes to a process chain element is not permissible. The term process element is also used as a synonym for the term process chain element. For example, a process element is the workpiece machining on a machine, such as the grinding of all the main and pin bearings of a crank shaft.

If more than one process element is necessary to manufacture a product, their line-up is called process chain (C). The single process elements are either put into sequential or parallel order or a mix of both. Thus, a process chain can be defined as an orderly line up of process elements with the target of transferring certain transformation objects from an input condition to a corresponding output condition. In the sense of production technology, transformation objects are raw or half-finished parts, but in general, they can also be other material and immaterial objects such as energies, information or services.

15.1.1 Technological Interfaces

For the layout of a process chain, it is important to know about the hand-over variables between the process elements. At the same time, the hand-over variables represent output and input variables of two subsequent process elements (n) and (n + 1). Some examples for hand-over variables of production-oriented processes are:

• measurements and oversize,

• surface structure or

• temperature of a component.

The totality of the hand-over variables between two process elements is the hand-over condition. In production-oriented systems, this condition is called technological interface. Figure 15.2 illustrates technological interfaces between process element (n − 1) and process element (n), as well as between process element (n) and process element (n + 1).

Fig. 15.2

Technological interfaces within a process chain

Process elements are displayed in a timely correlation. Per process element, the process time t_n incurs. Technological interfaces, however, can only be assigned to one point of time. A corresponding time interval does not exist. The total of all sequential processes of a process chain makes up the total time t_tot.

15.1.2 Process Chain Layout

Improved cutting material, more efficient and more accurate machines as well as increased requirements towards a cost-efficient, rapid and environmentally sound manufacturing contribute to the fact that cutting processes have further developed to a high degree. Especially the processes of hard finishing with geometrically defined cutting edge can be named here as examples. They can make a considerable contribution for the shortening of process chains and thus to the reduction of processing times and to the reduction of production costs.

To optimise existing process chains by using new technologies or to configure new process chains optimally, a process chain layout is carried out. The tasks to be passed through for this purpose are subdivided into two steps, which depend on each other [BRA08]. At the beginning of the process chain layout, the processes are identified, which allow the generation of the product in their order, which is to be determined. This step is called process chain design. Based on this, it is possible to configure the process parameters and technological interfaces.

The target of the process chain design is to identify suitable processes so as to allow a most efficient production. The assessment factors are, for example, economically-logistic target variables such as processing times, work cycle times and machining costs. Complex process chains, however, rapidly let the production planner reach his limits due to the high number of design variants. This problem can be confronted with the use of process chain simulation. The technological basis of a process chain simulation are the locally optimised single processes such as sawing, turning/milling, hardening or finishing. The result of a simulation cycle gives an indication concerning the profitability of the reviewed process chain. Rationalisation potential for the process chain can be exploited by redesigning.

The development of new process chains as a medium to improve the profitability and quality must not only be applied on single work or production steps, it must be targeted at the total optimum [TÖN07]. The ASI method represented in Fig. 15.3 offers support for the implementation of a holistic process chain layout. The basis is the redesign of production-oriented process chains by adaption, substitution and/or integration of single production steps.

Fig. 15.3

ASI method for process chain design

Adaption is the adjustment of two subsequent processes, e.g., the production of raw parts by forging and the following cutting. In the figure, process B is adapted to processes A and C. In the development of tools and tool machines or as a reaction to altered cost structures, the substitution of a production process can be sensible. One example for such a substitution is the substitution of a grinding process by hard turning. In Fig. 15.3, process B is substituted by the further developed process D. The integration of manufacturing steps shortens the operation sequence. This is often connected with direct process-conditioned cost savings or indirect cost savings based on shortened through-put times and reduced controlling effort. A current example for this is the complete machining of components by integration of different manufacturing processes on a multi-axial turning machine or a machining centre. In the illustration, process B is integrated into the course of production step A.

15.2 Process Model Generation

In technology, the term model stands for an image of reality. The VDI guideline 3633 defines model as a simplified reproduction of a system [VDI3633]. In this context, simplified reproduction means the reduction to only those characteristics relevant for the modelling target. Here, model generation is to be all the necessary steps for the generation of a model.

The analysis of complex technical processes such as cutting processes requires the set-up of process models. For this purpose, all single process steps are abstracted first so as to achieve a reduction of the complexity and a concentration on the main characteristics. Toenshoff defines the term “process model” as an abstract presentation of a process, which serves to link up the causes and effects with each other [TÖP92]. The target of the process model generation is to understand the correlations and interactions within a process better as well as being able to predict resp. optimise future process results. The application of process models is often called “simulation”.

The process model depicts the correlations between input and output variables of a process. It thus describes the static and dynamic behaviour of a process. Figure 15.4 clarifies the correlations between real process and process model. During the model generation, an abstraction of the initial question takes place to begin with. It is fed into the process model. The application of the model supplies an outlook of the behaviour of the real system. Before using the process model, its functioning has to be verified using known input and output variables.

Fig. 15.4

Model generation to predict the behaviour of a real system

The generation of process models can occur in different ways, depending on the application and the information at hand. Toenshoff and Paul distinguish between three types of process models [TÖP92]:

• heuristic models, which can be obtained by means of the sophisticated description of know-how,

• physical/empiric models, which rely on physical, mathematical basics and

• information-oriented data models.

Figure 15.5 illustrates the mutual relations between these types of models.

Fig. 15.5

Levels of model generation

Heuristic models depict know-how, e.g., the process understanding of a machine operator in production. For instance, the experienced operator of a CNC turning machine is often able to draw conclusions for the layout of the machining process from process noises or the shape of the generated chips. This knowledge, however, often gets lost with a change of workplace. On the other hand, the filing and guarantee of a permanent availability of know-how, e.g., from the manufacturing area are of great importance for the success of a company. The supply and maintenance of such knowledge is referred to as knowledge-management. The modelling of know-how occurs by means of simple production rules (if/then relations), class or object structures (ER or UML diagrams) [OES98] or by means of fuzzy quantities (fuzzy logic) [PAU94].

Technological dependencies, for example as between cutting force F_c and cutting power P_c (Sect. 5.1), can be illustrated in mathematical algorithms. The thus resulting process models are determined as physical or empiric models—depending on their type of retrieval [PRO77]. While physical process models are based on physical laws, empiric process models are detected by means of measurements in the process experimentally (Fig. 15.6).

Fig. 15.6

Physical and empiric process model generation

For the generation of physical process models, relevant physical processes are selected and transformed into a qualitatively model, based on the target of the process. The mathematical representation of the model—the physical process model—is detected on the basis of physical laws. It represents the quantitative dependencies between technological input and output variables, the correlation between adjustment variables and work result, for example. Empiric process models are, as mentioned before, determined by measurements in the process experimentally. For example, in the cutting technology, grinding tests are carried out, at which relevant input and output variables are to be recorded. The test results are evaluated, a type of model is selected (correlation function), the coefficients determined and the empiric process model verified by means of further tests. The benefit of physical process models is a process-independent process understanding and the connected simple transferability to altered machining conditions. For cutting processes, however, a purely physical model generation is unrealistic because the existing physical laws are not sufficient and therefore have to be completed by experimental investigations empirically.

Apart from the heuristic and physical/empiric models, Fig. 15.5 illustrates data models as the third level of process model generation. Data structures are determined in data models so as to ensure a holistic description of the entire relevant production-oriented information. Data modelling includes the development of an object-oriented relationship model in the form of class diagrams. These can also be illustrated as sequence, condition or activity diagrams for further use [OES98]. The data models are deposited, i.e., filed in relational, object-relational or purely object-oriented data banks.

15.3 Process Layout Using the Example of “Hard Finishing”

The result of a cutting process is determined by the input, process and output variables. The setting of these variables, considering the mutual dependencies is called process layout. The starting point of a process layout is the determination of target variables. These target variables represent specifications for the result variables generated by the machining process. Generally, we distinguish between quality-relevant, economic and ecological targets of a machining process.

The quality of a cutting process is judged according to the resulting component quality. For this, characteristics are macro-geometry, micro-geometry and surface zone influence. Figure 15.7 shows the quality characteristics of an external grinding process in an exemplary way. Analogically, the relevant characteristics are assigned for the economic and ecological targets. In the design process or production planning, component-specific target values or target intervals are detected for the single characteristics of a process. These targets are illustrated in Fig. 15.8 for an external grinding process in an exemplary way.

Fig. 15.7

Quality characteristics in external grinding

Fig. 15.8

Exemplary target variables for external grinding

In the following, the approach to the layout of machining processes is explained by means of the above described external grinding process. The difficulty of a process layout is the weighting of the target variables compared with each other. What is for example, the significance of the ecological targets of the machining which are the quality-determining ones? A proven and tested method to determine these weightings is the target tree process. In design methodology it is applied to carry out an evaluation of different concept variants [ZAN70]. For the layout of the exemplary grinding process it is used to calculate the mutually corresponding priorities from the weightings of the single target variables.

Figure 15.9 illustrates a target tree for the above mentioned target variables. This one is subdivided into four levels. The target function to be determined is located on the first level. The second level organises the target variables in the group’s quality, economy and ecology. A detailing in partial groups is shown on the third level and the targets variables themselves on the fourth level. Weightings are assigned to each branch of the target tree. The weight of the group quality amounts to 3 in this example, the group economy is weighted with 2 and the group ecology with 1. In practice, this means that for the grinding process to be designed, the quality of the manufactured components is exactly 3 times as important as the ecological target variables. The weightings are carried out the same way on the lower levels.

Fig. 15.9

Determination of priority factors according to the target tree process

Priority factors are determined from the weighting factors for each element of the target tree. For this purpose, the weighting factors of a level are added to begin with. For the second level, this adds up to 3 + 2 + 1 = 6. A priority factor is represented as a fractional rational number. The sum of the priorities is in the denominator of this fraction, the priority of the element concerned in the numerator. So the priority factor for the quality amounts to 3/6, which equals 1/2. In the lower levels the priorities are determined in a similar way. However, the determined single priorities of the third and fourth level additionally have to be multiplied with the priority of the group located above. The sum of the priorities of a level is generated as a test. It should always be 1.

After having defined the priorities of all target variables, the target function for the layout of the machining process is compiled (Eq. 15.1). In target function Z, the standardised variables for the target variable ${\vec{\text{x}}}_{\text{i}}$ enter apart from the priority factors p_xi.

$$ Z = \sum\limits_{i = 1}^{n} {_{{}} \left( {1 - p_{xi} \cdot \left| {_{{}} \overline{{x_{i} }}_{{}} } \right|_{{}} } \right)} $$

(15.1)

The standardisation is necessary so as to be able to compare the target variables with each other independently from their different values and dimensions. As a standardisation interval, the variable area (0.1–0.9) is selected. The approach to the standardisation is clarified in the example in Fig. 15.10:

Fig. 15.10

Standardisation of the target function for the roughness R_z

After the standardisation of all target variables these can be either positive (x > 0) or negative (x < 0). A uniform orientation is necessary, however, to ensure the comparability. Thus, Eq. 15.1 generates the value of the standardised variables.

In addition, in Eq. 15.1 it must be observed which values of the standardised target variables ${\vec{\text{x}}}_{\text{i}}$ lead to an improvement of the target function Z. If evaluations criteria are mainly used as in the example from Fig. 15.8, which have an advantageous effect for smaller values, (example: roughness or costs), the standardised and weighted target variables are to be subtracted from 1. If, on the contrary, evaluation criteria are considered which have an advantageous effect for higher values (example: efficiency factor), the term ${\text{p}}_{\text{xi}} \left| {{\text{x}}_{\text{i}} } \right|$ from Eq. 15.1 is not to be subtracted from 1. The result of the target function is called evaluation code Z. The machining result is better, the higher Z.

In the so-generated target function, technological models (so-called basic model) are introduced to describe the target variables. For example, the following model is used for the roughness R_z illustrated in Fig. 15.10 [CZE00]:

$$ R_{z} = c_{0} \cdot v_{ft}^{{c_{1} }} \cdot f_{r}^{{c_{2} }} \cdot v_{c}^{{c_{3} }} \left[ {\mu m} \right] $$

(15.2)

The constants and exponents c₀ to c₃ required to determine the roughness R_z are model-specific values and can adopt different variables according to the combination of workpiece, tool and machine.

15.4 Process Chain Layout Using the Example “Gear Production”

In practice, cutting machining processes cannot be regarded alone. Generally, the machining occurs in several subsequent partial steps as so-called multi-step process conduct. This multi-step process conduct (example: roughing and finishing a function area) represents the layout of a process chain element according to the PEC model. The layout of different process chain elements to each other is called process chain layout. The target of a multi-step process conduct is a preferably high correlation of the machining result with previously determined target variables. For example, a high material removal rate in the roughing process allows a more rapid material removal. In contrast, high surface qualities as well as dimension and shape accuracies are achieved with a lower material removal rate and consequently lower finishing forces.

The following example (Fig. 15.11) demonstrates the effects of two-step grinding on the workpiece surface zone. Generally, the surface zone state is characterised by residual stress course, hardness and structure condition. It is very significant for highly-loaded components. A faultless functional behaviour of the components is only achievable by reaching certain quality targets. At this, it is especially important to avoid residual tensile stresses in the surface zone of the terminated components, so as to counteract crack formation among other. Figure 15.11 shows how critical residual tensile stresses caused by grinding can be avoided for an external plunge grinding process by means of multi-step machining.

Fig. 15.11

Residual stress course at multi-step process conduct

The high material removal rate of roughing causes a relatively high thermal influence in the workpiece surface zone. The resulting residual tensile stresses can be removed by adapting the oversize in the finishing process. In addition, a mainly mechanical influence of the workpiece surface occurs due to the low material removal rate in the finishing process. This causes residual compressive stresses, which mostly balance the residual tensile stresses remaining in the deeper layers of the workpiece surface and often has a positive effect on the component fatigue life.

Complex technical components, e.g., gears, are generated by different manufacturing processes. They develop complex process chains consisting of different process chain elements such as forming, cutting and hardening. A process chain layout is carried out so as to obtain an optimum result from such process chains. In the following paragraph the necessary process for this purpose will be explained using the example of a gear production.

The top of Fig. 15.12 illustrates a conventional process chain for the manufacturing of gears for automobile transmissions. The single process chain elements are locally optimised concerning time and costs. The interfaces of the process elements are impenetrable for an iterative information flow towards the layout of the process chain.

Fig. 15.12

Process chain design by substitution and integration of single processes

The bottom part of Fig. 15.12 shows a further developed process chain where the generation of raw parts, cutting soft machining and heat treatment have been substituted by precision forging with integrated heat treatment. The background of substitution is the intention to exploit the economic advantages of forging, which occur at high lot sizes. In addition, the integrated heat treatment supersedes the repeated heating before hardening, which considerably reduces the energy need of the process chain. The process chain layout and optimisation takes place considering economic-logistic targets and technological interactions between the process chain elements. At this, the approach is according to the “method for the positioning of technological interfaces” (design of technological interfaces—DTI-method) [BRA08, DEB03].

During the process chain layout, two questions have to be answered as a matter of principle:

• 1. Where in the process chain do technological interfaces occur?

• 2. What level are technological hand-over parameters planned at?

The answer to the first question occurs during the process chain design. As tools for this, a process chain simulation with subsequent application of the ASI method is described in Sect. 15.1. In connection with the DTI method, this first phase of the process chain optimisation is called “qualitative positioning” (see below). The problem of the second question can be illustrated for the exemplary process chain for the manufacturing of gears as follows:

• Generation of a high component quality by means of precision forging and a resulting lower effort for the hard finishing or

• Generation of a lower component quality by means of precision forging at a higher effort in hard finishing.

The target is to determine an optimum between these two layout variants. For this purpose, the technological hand-over values at the interfaces of the process chain have to be analysed and quantitatively configured. The DTI method describes this phase as “quantitative positioning”. The necessary approach is explained below. The following Fig. 15.13 illustrates a schematic image of the approach when positioning technological interfaces.

Fig. 15.13

Qualitative and quantitative positioning of technological interfaces

Target of the qualitative positioning of technological interfaces is the process chain design using suitable manufacturing technologies. At this, an increase in the productivity and profitability is always aspired. This is achieved, e.g., by reducing the production steps and the technological interfaces within the process chain. The reduction of the number of production steps aims for a decrease of the throughput time and thus of the manufacturing costs. In addition, a reduction of the interfaces decreases the computing time for the layout of the hand-over variables to the technological interfaces.

To carry out the qualitative positioning, an analysis of the production steps and cycles required for production is carried out. Based on this, manufacturing processes which allow a preferably efficient component production are identified. An evaluation of the single production processes acts in accordance with the degree of added value, for example. So during the process chain design, attention must be paid to combine processes with a low degree of added value with ones that considerably increase the workpiece value (→process integration). As tools for the subsequent process chain design, the ASI method (see above) or methods of process substitution, process combination, process elimination and process exchange recommended by Koenig [KÖN87] are used. The result is different process chain variants based on alternative manufacturing processes.

Figure 15.14 illustrates different technological variants for the design of the process chain for the production of gears with high accuracy requirements. In all three variants demonstrated, the input material is a rod-shaped semi-finished product.

Fig. 15.14

Alternative process chains for the manufacturing of gears

The quantitative layout of technological interfaces describes the dimensioning of the technological hand-over values between the single process steps. The process models for all process elements are determined based on physical and empiric models (see above). The next step is the definition of the prevailing boundary conditions for each production process. The resulting functions (specific process model) are merged to target functions for the layout of single technological variables. The determination of the target functions and the identification of weightings for each target variable occur according to the target tree process (Sect. 15.3).

First, the detected target functions for the single production steps are weighted and then merged to a total target function. The calculation of the target function produces the degree of layout of the process chain under the given boundary conditions. Varying the boundary conditions for the process chain (e.g., cycle time) or for single processes (e.g., amount of cutting fluid) can influence the layout result of the process chain positively or negatively. The determination of the basic conditions and the weights of the target variables depend decisively on the know-how of the planner.

Process chains consisting of few subsequent process steps can easily take the planner to his limits, if the process chain is to be designed holistically at an optimum. Not only the multiple process parameters of all production processes, also the mutual correlations seem to make a layout based on the cognitive tasks of the planner impossible. The linkage of the single process models of the entire process chain for an optimum determination of the interfaces do not allow the deterministic identification of a solution in an acceptable period, not even with modern data processing systems. Therefore, one must resort to heuristic solution methods (e.g., genetic algorithms) or methods such as “design of experiments” DoE [DEH09]. At this, the use of simulation systems allows an automated application of the test plans and under stochastic conditions, finding a sufficiently good solution for an optimum dimensioning of the technological interfaces.

In the following, an example for the layout of a technological interface is explained. For this purpose, the process chain for the production of highly-loaded gears for automobile transmissions by means of precision forging is consulted [DER07]. In this new process no soft machining of the gearing takes place. Between the process elements precision forming and hard machining, research has shown that the workpiece oversize and its alteration in serial production are the main hand-over variables between the process steps. It has decisive influence on the economic targets, i.e., the production costs of the gears. To demonstrate these correlations, Fig. 15.15 illustrates the different wear behaviour of the forging dies and the grinding wheel.

Fig. 15.15

Wear effect during the forming and grinding process

The left side of the image shows a forging process. At each press stroke, the dies flare due to wear—the generated components become bigger in the course of time. If a tight diameter tolerance of the forged part has been determined (oversize tolerance), only few components can be manufactured with one set of dies. The image on the right shows a grinding process. The used grinding tool wears during the grinding process. The bigger the absolute forging oversize at this (planned machining oversize plus oversize tolerance of the forging process) has been set, the higher the grinding wheel wear per generated component. This is due to the increased workpiece volume, which has to be removed by grinding in the case of a worn set of dies. It becomes clear that the grinding oversize itself has little effect on the layout of the forging process. In fact, it is the oversize increase of the generated half-finished parts caused by the wear of the forging dies, that has a negative effect on the following production steps.

Figure 15.16 illustrates the qualitative courses of the process costs depending on the oversize increase for a precision forming process and a multi-step hard finishing process. The target of the comparison of both curve progressions is the determination of a value for the optimum oversize increase ∆a_opt.

Fig. 15.16

Technological correlations between forming and hard machining

The life time of a forming tool can be prolonged to be a high oversize tolerance. If in contrast, only a very low oversize tolerance is permitted, the process costs for the precision forming process will rise due to lower tool life. For hard finishing, on the other hand, an increase of the process costs occurs due to the increased workpiece oversize after forging, since a higher ∆a is accompanied by an increased oversize a and the process time increases as well as the tool wear. The discontinuous course of the cost function illustrated in image 15.16 results from the multi-step structure (roughing/finishing) of the grinding process.

15.5 Process Monitoring

The process monitoring represents an important task within the framework of manufacturing, so as to achieve the target variables determined in the process and process chain layout. In its course, input, process and result variables are analysed and compared with each other. This is how, on the one hand, the basis for an active intervention in the process via controlling mechanisms and, on the other hand, the basis for the development, evaluation or verification of empiric process models is generated (Sect. 15.2). The process monitoring represents the testing of defined process characteristics and parameters. In Fig. 15.17, the qualitative test (perception) and quantitative test (measurement) are distinguished.

Fig. 15.17

Testing as a basis of process monitoring

A direct perception, for example, is taste of a medium. The appearance, on the other hand, is only perceived indirectly via reflected light. The evaluation of these perceptions is based on implicit, i.e., unstructured know-how. A measurement, however, complies with explicit, i.e., structured and documented knowledge. At this, the compilation of a length by means of a comparative normal represents a direct measurement. Temperatures (e.g., in a thermometer) are deduced from the length expansion of a comparative normal. It is therefore an indirect measurement.

In production technology, especially in cutting, the measurable variables are differentiated after the time of their development. Variables which occur during a process, are called process variables. Variables which exist as a permanent result at the end of a process, are called result variables or output variables (Fig. 15.18). Further information on this topic is delivered by Toenshoff [TÖI01] and Karpuschewski [KAR01].

Fig. 15.18

Signals and variables on process monitoring in the cutting process

A comparison of the measured process and result variables with the corresponding target variables generally provide deviations from the set values. So disturbances are affecting the process. According to whether the deviations resulting from the disturbances are within a determined tolerance, a regulatory intervention in the process will become necessary. The causes for such an intervention can be of stochastic origin, e.g., temperature alterations, but also systematic alterations of the boundary conditions, e.g., grinding wheel wear during the grinding process.

Figure 15.19 introduces different quality control loops. The target of quality control is the reduction of the mentioned disturbances variables on the resulting component quality. The control loops are differentiated according to the type of the underlying measurement variables (process variables or result variables) and the time delay of the intervention in in-process, process-near and superior control. For the execution of the quality control, a process layout takes place first. For this purpose, the primary (e.g., cutting speed) and secondary manipulated variables (e.g., cutting force) have to be determined. As input information for this, existing process models and process data, as contained for example in a quality information system [TÖC96] are used.

Fig. 15.19

Quality control loops for grinding processes

During a machining process, process signals such as forces, powers or sound emission can be recorded by sensoring. These process signals allow the gaining of process variables by means of data reduction and parameter generation. They can be deployed for an in-process control. The adaptive control (AC) for the grinding process can be mentioned as an example [TÖF02].

The in-process quality control occurs on the basis of process variables. In the process-near and superior quality control, result variables are considered additionally. The target of the process-near quality control is to balance a lack of process quality as quickly as possible. In a best case, this can be regulated from component to component. A superior quality regulation acts during the entire production period of a component. This kind of control is carried out, for example, by collecting process data about different components and production lots. The underlying process data are updated with these data and a new process layout and optimisation takes place. The statistical process control SPC can be mentioned as an example.

Systematic disturbance variables (e.g., grinding wheel wear), as illustrated in Fig. 15.20, are detected and balanced by control loops.

Fig. 15.20

Control loop for the process-near regulation of external plunge grinding

The core element of the illustrated control loop is the process-near quality check, for example in a flexible measuring cell immediately after machining. Target variables of machining such as roughness or the residual stress state of the component represent the target variables in the represented control loop. These are compared with the result variables (actual values) detected in the measuring cell. Via the determined offset, an altered adjustment variable is defined for the next component [CZE00]. In addition, the framework of a process-near control offers the possibility of carrying out a component-specific optimisation of the process chain of the subsequent machining steps. Based on the process models, which have already been used for the process chain layout, the technological interfaces of the following processes can again be configured at an optimum while considering correlations. This is necessary because distributions can lead to component-specific result variables, which differ from the previously considered optimum value determined for the entire process chain, after every machining process. At this, it has to be ensured that a layout of all technological interfaces can be configured individually for each component without causing a delay of the component production. The current methods do not allow this sufficiently yet.

In the illustrated flexible measuring cell (Fig. 15.21), micro-magnetic, laser and scatter light sensors are deployed. The micro-magnetic sensor analyses machining-conditioned alterations in the component surface zone. These are detected by the so-called Barkhausen noise (volatile alteration of the magnetisation), the overlapping permeability (reversible alteration of the magnetisation) and the harmonic analysis (Fourier analysis of the magnet field) [KAR01]. In addition, the measuring cell contains a laser scanner for the compilation of macroscopic quality characteristics (component geometry, concentric runout) and a scatter light sensor for the touch-free registration of the micro-geometries (roughness). The results of the component checking in a flexible measuring cell represent result variables, which are deployed in the process-near or superior quality control (Fig. 15.19).

Fig. 15.21

Layout of a flexible measuring cell for process-near measurement

In summary, the course of a process monitoring in principle can be described as follows. Based on a functional process, an entity (detector) compiles and maps all arising problems. According to whether the underlying data basis consists of explicit knowledge (facts) or implicit knowledge (experience), different analysis processes (measurement or perception) are deployed. Once the problem is phrased, different strategies for problem solving resp. for optimisation are applied depending on the underlying data basis (explicit/implicit). The target of the process monitoring is to regain control over the process, which is problem-afflicted due to a malfunction. If this succeeds lastingly, it becomes necessary to consider setting the process limits more tightly. This means a permanent improvement of the process quality (reduction of the standard deviation σ).

15.6 Questions

• 1. Explain the term process chain element using the PEC model.

• 2. Name the transformation variables concerning the function structure of a technical product.

• 3. What is meant with a technological interface?

• 4. What connection is there between a real process (system) and a process model?

• 5. Name three typical model generation levels.

• 6. How do physical and empiric process models differ?

• 7. Delimit the input, process and output variables of a cutting -process against each other.

• 8. What is meant with the quality criteria of a cutting process?

• 9. Under which three aspects can cutting processes be configured resp. evaluated?

• 10. Name a simple method for the determination of the target function of a grinding process.

• 11. What is the difficulty in detecting the priority codes of a target function?

• 12. Draw the radial stress course of a shaft under bending and explain why residual tensile stresses can have a negative effect in the surface zone.

• 13. What process chain designing measures is the ASI method based on?

• 14. How do conventional and process-comprehensive process chain layouts differ?

• 15. According to the method introduced here, the process chain layout can be divided into two sections. What are they?

• 16. What is meant with qualitative positioning of technological interfaces?

• 17. Why is a quantitative layout of the technological interfaces additionally necessary after the process chain design?

• 18. Testing as a basis of process monitoring occurs according to two different methods. Name them.

• 19. Does the surface zone condition of a component represent a process or a result variable?

• 20. Distinguish the stochastic and systematic errors of a grinding process.

• 21. What does the difference between in-process control and process-near quality control consist in?