**3.1. Implementation on iCub robot**

The integration of the ROBOSKIN tactile sensor on iCub, has involved forearms, upper arms, torso and hands (palms and fingertips). The current implementation allows obtaining a number of distinguished taxels equal to 2400. In figure 5 the final result is shown.

**Figure 5.** The iCub covered with artificial skin

## *3.1.1. Implementation on the iCub hands*

6 Will-be-set-by-IN-TECH

(a) (b) (c)

(d) (e) (f)

**Figure 4.** The production steps for the palm of iCub. For a description of each step, please refer to the

• Identification of the part to be covered, see Fig 4(a). If no CAD model is available, obtain

• Manufacturing of the part (or of a cover) with a 3D printer (Eden 3D printer from *Objet*). The resulting parts look for example like in Fig. 4(b). Round holes provide space for the

• Identification and wiring of the mesh of flexible PCBs that is needed to cover the part, see

• Bonding of the PCBs on the part with bi-component glue and the help of a vacuum system,

• Covering the PCBs with silicone foam, see Fig. 4(e). To this aim we employ a specific

• Covering of the silicone foam with a conductive lycra as ground plane, as shown in Fig.

The integration of the ROBOSKIN tactile sensor on iCub, has involved forearms, upper arms, torso and hands (palms and fingertips). The current implementation allows obtaining a

number of distinguished taxels equal to 2400. In figure 5 the final result is shown.

the shape with a 3D laser scanner (as for example for the hands of KASPAR).

CDC chip and the other electronic components which are soldered on the PCB.

text.

Fig. 4(c).

4(f).

see Fig. 4(d).

purpose-built mold for each part.

**3.1. Implementation on iCub robot**

As it is possible to see in figure 4(a), the palm of iCub is made from carbon fiber; since this is a structural part, we decided not to modify it, but instead, we added another cover above the carbon fiber part as a basis for the sensor: it has a thickness of 1.2 *mm* and provides space for the CDC chip and the other electronic components which are soldered on the PCB. The implementation steps are reported in Fig. 4. While for all the iCub parts the standard ROBOSKIN solution for the PCB has been used, for the iCub fingertips it has been necessary to design a specific solution (see figures 7(a), 7(b), 7(c)), since they have small size and round shape (each fingertip is 14.5 mm long and 13 mm wide and high and has a round shape that resembles a human fingertip).

The structure of the fingertip is illustrated schematically in Fig. 6: the inner support of the fingertip is shown in yellow, and the flexible PCB, that is wrapped around, is depicted in green. To mechanically attach the fingertip to the hand, the last phalanx of each digit (shown in red) has a stick that fits inside a hole in the inner support. A screw is used to secure the fingertip and, in addition, the screw fixes a fingernail on top of the fingertip that covers the PCB. The dielectric, made of silicone rubber foam, is depicted in brown; around the foam there is the conductive lycra layer shown in black as well as the AD7147 chip.

**Figure 7.** The production steps for the fingertip iCub

The fingertip production protocol involves the following steps:

**Figure 8.** Fingertip sensor placed on the support used for measurements together with schematic view of the line where measurements have been taken

With respect to the first implementation of the fingertips [18], where it was used a self-made mixture of silicone (CAF4 from *Rhodia-Silicones*) and carbon-black particles (Vulcan XC72 from *Cabot*) as a ground plane, and, as protective layer, silicone glue (Sil-Poxy from *Smooth-On*) sprayed above it, the new implementation made with conductive lycra, has been chosen because it increases the durability of the fingertip to usury due to friction forces appearing during grasp tasks. In order to characterized the behavior of the new fingertip several experiments have been performed.

The characterization setup consists of a cartesian robot (TT-C3-2020 from IAI) which moves one non-conductive probe against the fingertip. The non-conductive probe is fixed at the top of an off-center load cell (AS1 form Laumas) which measures independently the force applied to the fingertip by the probe during the test. A microcontroller records the CDC output and the load cell circuit output. Therefore the Data are stored in a computer by a dedicated graphic user interface made in Matlab.

The characterization protocol was the following:

8 Will-be-set-by-IN-TECH

PCB. The dielectric, made of silicone rubber foam, is depicted in brown; around the foam there

(a) (b) (c)

(d) (e) (f)

• The flexible PCB is wrapped around an inner support that was printed with a 3D printer. (see figures. 7(a), 7(b), 7(c)). As we are using an I2C serial bus, only 4 wires have to be connected to the PCB (Vcc, ground, serial data line and serial clock). They travel along the side of the fingers to small boards at the back of the hand. These boards relay the data from all five fingertips (and the four triangular modules in the palm) to one microcontroller

• A first layer, made with a foam elastomer (Smooth-on Soma Foama 15), is deposited over the PCB; mechanical deformation of this soft dielectric material leads to capacitance variations; therefore, it is possible to detect pressures applied on the fingertip, see figure

• The second layer, that is made with conductive lycra, is glued over the silicone foam substrate, allowing the development of a single ground plane above all electrodes placed on the PCB thus enabling the detection of each type of object (conductive and non conductive) within noise reduction. This layer is connected to the digital ground of the

• Finally the third protective layer, made again with Smooth-on Soma Foama 15, is deposited; it is used such as a protection, thus intrinsically increases the lifetime of the

**Figure 7.** The production steps for the fingertip iCub

board, which is located in the forearm of iCub.

CDC by one flat pad on the PCB.

fingertip sensor.

7(d).

The fingertip production protocol involves the following steps:

is the conductive lycra layer shown in black as well as the AD7147 chip.

Only the steady-state responses at external pressures of the sensor have been taken into account during post elaboration. Mean values of the fifty steady-state responses of C1 and C2, at different positions of the probe, are reported from figure 9(a) to figure 9(e). It is possible to observe a non linear trend of the capacitor variation as a function of the applied pressure. Therefore, the least squares method has been used to fit a polynomial model on each steady state response at each position. Figure 10(a) shows an example of the choice of the order of the polynomial function for CDC-C1 in the case of the position 0 mm, while, in figure 10(b), it is shown the norm of residual with respect to the polynomial order, on the basis of which,

**Figure 9.** steady-state response at 0 mm (a), 1.5 mm (b), 3 mm (c), 4.5 (d) mm and 6 mm (e) position point of the straight line of measurement

a 4th polynomial model order has been chosen. Polynomial functions thus obtained, were used to calculate the variation of capacitors C1 and C2 due to a fixed pressure as a function of position along the measurement line. Results are presented from figure 11(a) and figure 11(e), which report the variation of capacitors C1 and C2 as a function of the probe position for five different pressure values from 5kPa to 25 kPa.

**Figure 10.** a) An example of the choice of the order of the polynomial function for CDC-C1 in the case of the position 0 mm; b) Norm of the residual with respect to polynomial order

#### *3.1.2. Implementation on the iCub arms*

10 Will-be-set-by-IN-TECH

(a) (b)

(c)

(d) (e)

**Figure 9.** steady-state response at 0 mm (a), 1.5 mm (b), 3 mm (c), 4.5 (d) mm and 6 mm (e) position

point of the straight line of measurement

For the iCub arms, the skin was integrate directly into the covers, so new covers were designed. The production steps are the same as for the palm and the other iCub parts and are illustrated in fig. 12, for the forearm, and fig. 13, for the upper arm. On the two iCub arms we have 1464 contact points: 6 patches (61 triangular module) and 7 MTBs for each arm.

### *3.1.3. Implementation on the Torso of iCub*

For covering the iCub Torso, five patches has been used, obtaining 528 contact points. In figure 15 it is possible to see the front of the torso covered with the glued patches and the silicone foam substrate (figure15(a)), the torso with the conductive lycra substrate on top (figure 15(b)) and finally the back of the torso cover with the 4 MTBs that are needed for the five patches (figure 15(c)).


**4.4.1 Resistance against decay fungi**

As gamma radiation causes break-up of cellulose to shorter chains, which are water-soluble, and that leads to an "opening of additional microcracks", in which water molecules can easily penetrate. Consequently, gamma irradiated wood is also more accessible to enzymes of wood decaying fungi.

Only ten days after incubation, it was clearly visible, that irradiated specimens were more overgrown, than control steam sterilised controls, which indicates a higher susceptibility to biodegradation of gamma irradiated wood compared to non-irradiated wood (Hasan, 2006a; Hasan *et al.*, 2008; Figure 13).

Fig. 13. Specimen pairs in testing flasks 10 days after exposure to brown-rot fungus *Gloeophyllum trabeum* (AC, KCC, KDC – non-irradiated controls, BC – specimen irradiated with 30 kGy, CC – 90 kGy, DC – specimen irradiated with dose of 150 kGy).

Changes in Selected Properties of Wood Caused by Gamma Radiation 297

Fig. 15. Appearance of irradiated and autoclaved specimens after 16 weeks of exposure to

**90 kGy**

**0 kGy**

The difference in ML between specimens irradiated with 30 and 90 kGy and autoclaved ones is slight and not significant after 4 weeks of incubation, while specimens irradiated with 150 kGy had significantly greater ML. During further incubation differences in ML between autoclaved and irradiated specimens increased but also between irradiated groups. During the all incubation period ML increased with radiation dose. Since *P. placenta* was more virulent and more aggressive fungus compared to *G. trabeum*, the obtained results are

ML [%]

Fig. 16. Mass loss (ML) of autoclaved and irradiated specimens during exposure to fungus *Poria placenta*: A) 4 weeks incubation; B) 8 weeks incubation; C) 12 weeks incubation; D) 16 weeks incubation; (irradiated specimens n=7; G=0 kGy - autoclaved controls n=21).

ML [%]

0 30 90 150

0 30 90 150

G [kGy]

**0 kGy**

**150 kGy**

G [kGy]

± s ± 95% confidence interval **D)**

± s ± 95% confidence interval **B)**

fungus *Poria placenta*.

**0 kGy**

**30 kGy**

logical (Figure 16).

ML [%]

ML [%]

0 30 90 150

0 30 90 150

G [kGy]

G [kGy]

± s ± 95% confidence interval **C)**

± s ± 95% confidence interval **A)**

#### **Resistance against brown-rot fungi**

Visible difference in appearance (irregular shape and darker colour) between irradiated and autoclaved specimen after 16 weeks of exposure to the fungus *G. trabeum* indicated a significantly higher decay of the irradiated specimens (Hasan, 2006; Hasan *et al.*, 2008). After 12 and 16 weeks of exposure to the fungus *G. trabeum*, average ML of irradiated and control specimens was more than 25 %, which proved that the fungus was virulent (EN 113: 1996). The difference in ML between irradiated and autoclaved specimens was slight and not significant after 4 weeks of incubation, except specimens irradiated with 150 kGy, whose ML was greatest. The difference in ML between controls and specimens irradiated with 30 kGy reached maximum after 8 weeks of incubation, while other two irradiated groups had no significant difference in ML comparing to control. During further incubation time the differences in ML between irradiated and control specimens decreased and after 16 weeks became insignificant (Figure 14).

Fig. 14. Mass loss (ML) of autoclaved and irradiated specimens during exposure to fungus *Gloeophyllum trabeum*: A) 4 weeks incubation; B) 8 weeks incubation; C) 12 weeks incubation; D) 16 weeks incubation; (irradiated specimens n=7; G=0 kGy - autoclaved controls n=21).

Fungus *P. placenta* causes brown rot with broad and deep cracks. Clearly visible difference in appearance (cracks and irregular shape) between irradiated and autoclaved specimens after 16 weeks of incubation has been shown in Figure 15.

Visible difference in appearance (irregular shape and darker colour) between irradiated and autoclaved specimen after 16 weeks of exposure to the fungus *G. trabeum* indicated a significantly higher decay of the irradiated specimens (Hasan, 2006; Hasan *et al.*, 2008). After 12 and 16 weeks of exposure to the fungus *G. trabeum*, average ML of irradiated and control specimens was more than 25 %, which proved that the fungus was virulent (EN 113: 1996). The difference in ML between irradiated and autoclaved specimens was slight and not significant after 4 weeks of incubation, except specimens irradiated with 150 kGy, whose ML was greatest. The difference in ML between controls and specimens irradiated with 30 kGy reached maximum after 8 weeks of incubation, while other two irradiated groups had no significant difference in ML comparing to control. During further incubation time the differences in ML between irradiated and control specimens decreased and after 16 weeks

ML [%]

Fig. 14. Mass loss (ML) of autoclaved and irradiated specimens during exposure to fungus *Gloeophyllum trabeum*: A) 4 weeks incubation; B) 8 weeks incubation; C) 12 weeks incubation; D) 16 weeks incubation; (irradiated specimens n=7; G=0 kGy - autoclaved controls n=21).

Fungus *P. placenta* causes brown rot with broad and deep cracks. Clearly visible difference in appearance (cracks and irregular shape) between irradiated and autoclaved specimens

ML [%]

0 30 90 150

0 30 90 150

G [kGy]

G [kGy]

± s ± 95% confidence interval **D)**

± s ± 95% confidence interval **B)**

**Resistance against brown-rot fungi**

became insignificant (Figure 14).

0 30 90 150

0 30 90 150

after 16 weeks of incubation has been shown in Figure 15.

G [kGy]

G [kGy]

± s ± 95% confidence interval **C)**

± s ± 95% confidence interval **A)**

ML [%]

ML [%]

Fig. 15. Appearance of irradiated and autoclaved specimens after 16 weeks of exposure to fungus *Poria placenta*.

The difference in ML between specimens irradiated with 30 and 90 kGy and autoclaved ones is slight and not significant after 4 weeks of incubation, while specimens irradiated with 150 kGy had significantly greater ML. During further incubation differences in ML between autoclaved and irradiated specimens increased but also between irradiated groups. During the all incubation period ML increased with radiation dose. Since *P. placenta* was more virulent and more aggressive fungus compared to *G. trabeum*, the obtained results are logical (Figure 16).

Fig. 16. Mass loss (ML) of autoclaved and irradiated specimens during exposure to fungus *Poria placenta*: A) 4 weeks incubation; B) 8 weeks incubation; C) 12 weeks incubation; D) 16 weeks incubation; (irradiated specimens n=7; G=0 kGy - autoclaved controls n=21).

Changes in Selected Properties of Wood Caused by Gamma Radiation 299

rot and brown rot causing fungi. Significantly greater mass loss of gamma irradiated wood than autoclaved wood has been established in the beginning of incubation to whiterot fungi. During further incubation, the differences diminished. In contrast, higher irradiation resulted in higher degree of cellulose depolymerisation, what makes wood significantly more susceptible to biodegradation by brown-rot fungi. Degradation of gamma-irradiated wood is greater and faster due to easier accessibility of simpler

Part of the research on the influence of gamma radiation onto wood properties was supported by the COST Action E37 in the frame of a Short-Term Scientific Mission (STSM) awarded to Marin Hasan, PhD. Parts of the experimental work were carried out at the Federal Research Centre for Forestry and Forest Products (BFH), Hamburg, Germany, which was the STSM host institution, and whose hosting is gratefully acknowledged. The authors extend their particular thanks to BFH and to Ruđer Bošković Institute in Zagreb, Croatia for gamma radiation provision and to its scientists for sharing their wide-ranging

Ardica, S., Calderaro, E., Cappadona, C. (1984) Radiation pretreatments of cellulose

Bakraji, E. H, Salman, N, Al-kassiri, H. (2001) Gamma-radiation-induced wood-plastic

Bakraji, E.H., Salman, N., Othman, I. (2002) Radiation-induced polymerization of acrylamide

Bogner, A. (1993) Modifikacija površine bukovine radi poboljšanja lijepljenja. Doktorska disertacija. Šumarski fakultet, Zagreb. pp. 141. (in Croatian language). Bogner, A., Ljuljka, B., Grbac, I. (1997) Improving the glued joint strength by modifying the beech wood (*Fagus sylvatica* L.) with gamma rays. Drvna industrija 47(2): 68-73. Brischke, C., Rapp, A.O., Welzbacher, C.R. (2006a) High-energy multiple impact (HEMI) –

Brischke, C., Welzbacher, C.R., Rapp, A.O. (2006b) Detection of fungal decay by high-energy

Brischke, C., Welzbacher, C.R., Huckfeldt, T. (2008) Influence of fungal decay by different

multiple impact (HEMI) testing. Holzforschung 60 (2): 217-222.

materials for the enhancement of enzymatic hydrolysis – II. Wood chips, paper, grain straw, hay, kapok. Instituto di Applicazioni e Impianti Nucleari, University

composites from Syrian tree species. Radiation Physics and Chemistry. 61(3): 137-

within Okoume (Aucoumea klaineana pierre). Radiation Physics and Chemistry.

test – Part 1: A new tool for quality control of thermally modified timber. Document No. IRG/WP 06-20346, International Research Group on Wood

basidiomycetes on the structural integrity of Norway spruce wood. Holz als Roh-

carbohydrates to fungi.

**6. Acknowledgements**

**7. References**

141.

scientific expertise on gamma radiation.

of Palermo, Italy.

64(4): 277-281.

Protection, Tromsø, Norway.

und Werkstoff 66: 433-438.

#### **Resistance against white-rot fungi**

Considering the patterns and mechanisms of decay of white-rot fungi, in the beginning of decay they utilise simple carbohydrates – the ones incurred by gamma radiation. During further incubation, white rot fungi mainly utilise lignin, and they use radical mechanisms to degrade lignin. Broken (somehow modified) lignin accompanied with radicals incurred in wood by gamma radiation could negatively interfere with these decay patterns. Therefore no significant difference in ML caused by white-rot fungi between autoclaved and irradiated specimens was determined (Despot *et al.*, 2006; Hasan *et al.* 2006a).

In contrary to white rot, brown rot decay mechanisms, particularly decay mechanism of *P. placenta,* are less radical dependent, higher radiation doses do not influence the decay patterns of this brown rot fungus. In contrast, higher irradiation resulted in higher degree of depolymerisation, what makes wood significantly more susceptible to dacay (Despot *et al.* 2006, 2007; Figure 17).

Fig. 17. Correlation between Mass loss (ML) of autoclaved and irradiated specimens and incubation time to fungus: A) *Poria placenta*; B) *Trametes versicolor*.

However, it has to be taken into consideration that depolymerisation of wood components by gamma irradiation is one aspect that could explain differences in changed "natural" durability of pine wood and needs to be considered for wood durability testing.

#### **5. Summary**

Gamma radiation at a level of 30 to 150 kGy causes irreversible and permanent changes in chemical and mechanical properties of wood. Using the HEMI test method it was possible to detect small but significant changes in structural integrity and brittleness of wood caused by gamma radiation. With increasing radiation dose the total amount of water-soluble sugars increased linearly, while the maximum swelling seems to be unchanged.

The method of sterilisation has a considerable influence on the natural durability of pine wood. However, wood sterilisation by gamma radiation has different influence on white rot and brown rot causing fungi. Significantly greater mass loss of gamma irradiated wood than autoclaved wood has been established in the beginning of incubation to whiterot fungi. During further incubation, the differences diminished. In contrast, higher irradiation resulted in higher degree of cellulose depolymerisation, what makes wood significantly more susceptible to biodegradation by brown-rot fungi. Degradation of gamma-irradiated wood is greater and faster due to easier accessibility of simpler carbohydrates to fungi.


**Acknowledgements**

Our research effort has been inspired and guided by the foundational work of **James E. Smith** (Emeritus Professor, University of Wisconsin, and Adjunct Professor, Carnegie Mellon University). We are indebted to Jim's work and guidance. Two other PhD students in NCAL (Neuromorphic Computer Architecture Lab), **Shreyas Chaudhari** and **Prabhu Vellaisamy**, along with many MS students in CMU's 18-743 "*Neuromorphic Computer Architecture and Processor Design*" course, have made and are still making significant contributions to this research effort.


Step 3: bDGA EXC

The final step of the 165Er isolation procedure reduced trace metal impurities while concentrating the product into a small volume, low acidity solution suitable for radiolabeling using a commercially available bDGA resin. The loading (5 M HNO3, 5 mL) and rinsing (3 M HNO3, 15 mL) solution concentrations were chosen due to literature studies showing high erbium and low transition metal (Fe, Co, Ni, Cu, Zn) affinities on a similar DGA resin [48]. A small volume rinse (0.5 M HNO3, 2 mL) decreased column acidity and minimized subsequent 165Er elution volume. Following this loading and rinsing routine, 97 ± 2 % of the loaded 165Er was recovered in 0.01 M HCl (1.2 ± 0.2 mL) (*n* = 18). Smaller elution volumes were attainable by fractionation of the 0.01 M HCl elution solution, with the optimized (*n* = 5) elution profile shown in Table 3.

**Table 3.** Optimized 165Er elution profile bDGA EXC column.


**1. Introduction**

Quality control issue is one of the cardinal factors in product manufacturing. It entails profiling areas where quality is to be maintained and enforced. In manufacturing parlance, quality could be described as process and protocol of the needful to ensure that the product is maintained at highest peak value. However, quality has been a watch factor that has resulted in industrial productivity birthed by technological innovation. [1] submitted that defining quality from different perspectives depends on the individual philosophical point of view. Whichever perspective adopted among several perspectives available, the authentic definition is the one that was premised around definition of American Society of Quality. American Society of Quality defines quality as an embodiment of totality of manufacturing essence which is positioned to satisfy an implied need or consumers' product need and expectation [1]. Also, [2] viewed quality as an identity of production essence while [3] described quality as collection of different hall-mark of production optimizations, expressed in implementable units. Therefore, it worth a while controlling the process that lead to formulation of quality, then controlling cost at different stage of production would be very easy. The important nature of quality control in industrial manufacturing therefore necessitates the establishment of control system during industrial production process.

ii. Examine the Drivers of Effective Quality Control System Monitoring in

iii. Study Issues and Challenges Involved In Quality Control Systems in

*Adapting Disruptive Applications in Managing Quality Control Systems in Intelligence…*

iv. Profile Critical Factors Influencing Adaptation of Effective Intelligent

In this section review of concept was carried out, and constructs were gleaned from the objectives and the aim and the title of this study. Therefore the review covers the following area: quality control, areas of disruption in quality monitoring and intelligent manufacturing. It includes the following: intelligent manufacturing system, quality control in manufacturing industry, industry 4.0 application disruption in quality assurance monitoring and challenges in quality control system.

The world of manufacturing environment has changed drastically in recent times, on account of industrialization. Also paradigm in production process, design and implementation has shifted in the direction of application of new generation applications, the applications could be found in design, monitoring and marketing industries. The new application has capability to accommodate high volume product processing, complex system and flexible schedule and sequence. The new system is referred to as intelligent system. The word intelligence comes from the new packages that comes with electronic tools that are now in popular use in industrial manufacturing [9]. The authors described intelligent system as electronic and automation replacement of traditional mechanical functions with new applications that uses sensors and sensitive Nano-tubes applications. In another clime, it is referred to as automatic system which found integration in design and monitoring system as

Intelligent system is highly used in monitoring process during industrial manufacturing of products. Intelligent production systems are operated as a calibrated design and monitoring system, they are used in sequential monitoring of production system. They are used to monitor highly complex manufacturing system in order to achieve flexible manufacturing for instance, [10–12] submitted that intelligent system has enabled the use of sensor, optimization parameters, laser beam application and laser beam machine process. This according to [10] described the introduction of intelligent manufacturing system as a step ahead of traditional manufacturing system. The claim lies in the fact that intelligent manufacturing has capability of self-analyzing, self-learning, complexity apprehensions and large data storage. The self -analyzing attribute of systems of intelligent component is rooted in attribute of intelligent application that consist of the use of sensors. The sensors are mounted on tools and machine for quality control attribute in areas such as sequencing, intelligent scheduling, intelligent control and maintenance.

Moreover, in intelligent manufacturing, scheduling and sequencing are "sinqua-non" there is interrelationship between the two concepts. The concepts tend to reoccurs throughout the production process because of its high utility. Also, [13] posited that intelligent algorithm was designed to assist in parametric calibration of some intelligent applications used in manufacturing industries. Algorithms are used

Intelligent Manufacturing

*DOI: http://dx.doi.org/10.5772/intechopen.93979*

Intelligent Manufacturing

Manufacturing Process.

**2.1 Intelligent manufacturing system**

**2. Literature review**

pointed out in [9, 10].

**61**

Quality control system according to [3, 4] ensures emergence of quality product as output of manufacturing process. Quality control system enables the engagement of different techniques and process to ensure quality production output. Some of the techniques according to [4–6] has yielded tremendous results in the past and still remain relevant in the scope of industrial production and manufacturing till date.

However, some of the techniques and tools are gradually becoming obsolete and yielding reduced performance in term of output, therefore there is a need for gradual replacement of old methods with automation techniques, in order to sustain the tempo of productivity. This fact necessitate research in the area of quality assurance in industrial manufacturing. Adventure for development of new quality assurance system and methods of production that is automation based lead to evolution of Industry 4.0, which has since then changed the industrial manufacturing game [6]. Introduction of industry 4.0 with artificial intelligence into the manufacturing system has brought about replacement of old mechanical based method with new and smarter machine technique with automated system that uses sensors, this according to expert has changed procedures often used in quality checks in manufacturing sector. However, artificial intelligence has brought up application of robotics in industrial application and also the use of applications that has been empowered with sensors for automation capability. Smarter machine according to [5, 7] has led to enhanced productivity, improved quality standard and products.

Finally, in [5, 8] it was alluded that innovation of smarter machines, sensor enable machine has been a major addition from industry 4.0 technological disruption, that brought about intelligent manufacturing, it is on this premise that this study investigated system and process of adapting disruption technology in quality control monitoring to be able to achieve results oriented intelligent manufacturing.

#### **1.1 Aim and objectives**

#### **1.2 Aim**

The aim of the study is to carry out a study on application of disruptive application in managing quality system in intelligent manufacturing with a view to improving manufacturing process in organizations.

#### **1.3 Objectives**

There is a need to articulate objectives of the study, the objectives were synthesis from the gaps and emerging thoughts from literatures consulted. Therefore the following objectives are used in this study. They are to:

i. Investigate the state of disruption in quality monitoring in industrial manufacturing

*Adapting Disruptive Applications in Managing Quality Control Systems in Intelligence… DOI: http://dx.doi.org/10.5772/intechopen.93979*

## **2. Literature review**

In this section review of concept was carried out, and constructs were gleaned from the objectives and the aim and the title of this study. Therefore the review covers the following area: quality control, areas of disruption in quality monitoring and intelligent manufacturing. It includes the following: intelligent manufacturing system, quality control in manufacturing industry, industry 4.0 application disruption in quality assurance monitoring and challenges in quality control system.

#### **2.1 Intelligent manufacturing system**

The world of manufacturing environment has changed drastically in recent times, on account of industrialization. Also paradigm in production process, design and implementation has shifted in the direction of application of new generation applications, the applications could be found in design, monitoring and marketing industries. The new application has capability to accommodate high volume product processing, complex system and flexible schedule and sequence. The new system is referred to as intelligent system. The word intelligence comes from the new packages that comes with electronic tools that are now in popular use in industrial manufacturing [9]. The authors described intelligent system as electronic and automation replacement of traditional mechanical functions with new applications that uses sensors and sensitive Nano-tubes applications. In another clime, it is referred to as automatic system which found integration in design and monitoring system as pointed out in [9, 10].

Intelligent system is highly used in monitoring process during industrial manufacturing of products. Intelligent production systems are operated as a calibrated design and monitoring system, they are used in sequential monitoring of production system. They are used to monitor highly complex manufacturing system in order to achieve flexible manufacturing for instance, [10–12] submitted that intelligent system has enabled the use of sensor, optimization parameters, laser beam application and laser beam machine process. This according to [10] described the introduction of intelligent manufacturing system as a step ahead of traditional manufacturing system. The claim lies in the fact that intelligent manufacturing has capability of self-analyzing, self-learning, complexity apprehensions and large data storage. The self -analyzing attribute of systems of intelligent component is rooted in attribute of intelligent application that consist of the use of sensors. The sensors are mounted on tools and machine for quality control attribute in areas such as sequencing, intelligent scheduling, intelligent control and maintenance.

Moreover, in intelligent manufacturing, scheduling and sequencing are "sinqua-non" there is interrelationship between the two concepts. The concepts tend to reoccurs throughout the production process because of its high utility. Also, [13] posited that intelligent algorithm was designed to assist in parametric calibration of some intelligent applications used in manufacturing industries. Algorithms are used in Asia and European industrial manufacturing sectors in permutation control of scheduling and sequencing operation. Scheduling operations involved scheduling of machine operations, allocation of resources of money, man, human and machine among others. [12, 14] in scheduling operations, parameters setting is of utmost importance. Some of the parameters was discovered by Johnson in 1965, scheduling was classified by Johnson into two categories; the flow shop and job shop. In concurrent intelligent scheduling, the flow shop utilizes Toyota production system in a way that the system could accommodate large volume of work per time, this view was supported in [14–17].

reliability is also another measure introduced in ensuring quality in product development [22] presented quality control methods that could be used to enhanced productivity and safety [23]. Worked on systematic ways of presenting reliability analysis of control system, that, it has tendency of improving product reliability, strategies and implementation. System and reliability analysis is for the purpose of quality control during product design and manufacturing and control stage. The system was described in [22] as consist of mathematical model that can be used to describe relationship between product quality and control method. Product reliability process design include process conception, identification of product

*Adapting Disruptive Applications in Managing Quality Control Systems in Intelligence…*

**2.3 Industry 4.0 application disruption in quality assurance monitoring**

automation at various aspect of manufacturing. Generation of traditional

Industrial application in product manufacturing has introduced dimension of

manufacturing has metamorphosized into conventional system, thus, the new generation of industrial application covered different systems of conventional technology. In [24], systems that could enable product supply system to be optimized using key technologies such as cyber system, IoT (Internet of things), data analytics, big data, and expanded database. The key production points could be interlinked and interconnected or networked for effectiveness. Industry 4.0 enables digital interconnectivity among machine and work stations of production system. Similarly, industry 4.0 facilitates the interoperability of man and machine interconnected with digital guides [25]. However, industry 4.0 has different adaptability in different continent both in literary presentations and industrial application. In Japan, industry 4.0 is embellished as "New strategy", China captured it as Made-in-Chine 2025 as contained in {48} while United states according to [24] referred to industry

Industry 4.0 involved is centered on industrial reformation or reindustrialization and information applications contained in [26], this fact was corroborated by [27], which makes availability of quality supervisors and production managers. In nutshell, the component of industry 4.0 application in the portfolio of industrial manufacturing and production according as an inclusive component as viewed by [25, 28–30] and it consist of the following aspects in industrial production: IoT applications, learning factories, internet services, cloud computing and storage, cybernetics, cognitive computing, Artificial intelligence, robotics, data analytics, mechatronics, big data application, and sensor management among others.

Right from the ancient days of product manufacturing, industrial application has

experienced a lot of changes ranging from design parameters configuration of product design implementation. Companies and design expert has labor extensively to come up with perfect system, but there has always been one challenge or the other. Quality control system in intelligent manufacturing gas come with challenges. [31] Deloitte (2014) opined that, there are challenges associated with indus-

Challenge of making reliable forecast is one of the major challenges often encounter in quality control management, however, [32] opined that the use of cyber technology equipment, physical system, artificial intelligence, big data would increase efficiency of running production system. Similarly, ineffective flow of materials and adequate planning has always been the bane of effective production

**2.4 Challenges in quality control system research (quality control**

manufacturing process and robust design [22, 23].

*DOI: http://dx.doi.org/10.5772/intechopen.93979*

4.0 as "reindustrialization".

**implementation in**

**63**

try 4.0 digitalization and digital transformation.

The choice of scheduling technique often depends on complexity, desired output and volume of system at hand. There are two conditions under which scheduling could be applied, the flow shop and job shop. Flow Shop in manufacturing and production process refers to high volume system that uses highly standardized equipment to ensure continuous flow of standardized products e.g. refineries, cement company, drinks production. Similarly, Job Shop is a low volume system, which periodically shift from one job to another. The production is often according to consumers'specification and orders are in small units [16, 17]. Moreover, sequencing is about methodical approach to processing loading jobs at work station. It describes the order in which jobs are processed or should be processed at work centers. In traditional scheduling operation the following rules subsists; the widely acceptable rules for scheduling operation in intelligent manufacturing according to [16–18] includes the following: First come First Serve (FCFS): Processing job in the order of arrivals at work center. Shortest Processing Time (SPT): Job are processed based base on length of processing time and Earliest Due Date (EDD): This rule sequences jobs according to their due date. Shortest due date are processed.


**5. Conclusions**

In conclusion, the results of this study showed that maximal physical exercise, regardless of whether it is performed under normoxia or mild normobaric hypoxia (equivalent of 3000 m altitude) caused a similar increase in BDNF concentration in the blood of well-trained athletes. Despite the fact that BDNF has been known to possess a protective effect on the brain, an elevated BDNF level did not protect our participants from cognitive impairment due to acute exposure to hypoxia, because indices of this variable, i.e., CRT and NCR, were worse in hypoxic conditions. All examined potential circulating factors that are known to affect BDNF expression/production (C, nitrite, ET-1, catecholamines, LA) most likely did not affect psychomotor functions in our experimental paradigm.

**Author Contributions:** Conceptualization Z.P., M.C. (Miłosz Czuba), and J.L.; methodology, Z.P., M.C. (Miłosz Czuba), and J.L.; formal analysis, K.P. and M.C. (Miłosz Czuba); investigation, Z.P., M.C. (Małgorzata Chalimoniuk) and M.C. (Miłosz Czuba); data curation, M.C. (Małgorzata Chalimoniuk) and K.P.; writing—original draft preparation, Z.P., M.C. (Małgorzata Chalimoniuk), K.P., M.C. (Miłosz Czuba), J.L.; writing—review and editing, Z.P., M.C. (Miłosz Czuba) and J.L.; visualization, Z.P. and K.P.; supervision, M.C. (Małgorzata Chalimoniuk) and J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** The research was supported by the grant No. 2013/09/B/NZ7/00726 from the National Science Centre of Poland.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.


*Article* **Elimination of Chromium (VI) and Nickel (II) Ions in a Packed Column Using Oil Palm Bagasse and Yam Peels**

**Angel Villabona-Ortíz 1 , Candelaria Tejada-Tovar 1,\* and Ángel Darío González-Delgado 2,\***

**Abstract:** The single-component adsorption of chromium (VI) and nickel (II) on oil palm bagasse (OPB) and yam peels (YP) in a packed bed column was explored and improved using a central 2 2 -star T composite design. The temperature, bed height, and particle size were evaluated, and the optimized response variable was the removal efficiency. The remaining concentration of heavy metals in solution was determined by Ultraviolet–Visible and Atomic Absorption Spectroscopy. It was found that bioadsorbents have a porous structure, with the presence of functional groups such as hydroxyl, carboxyl, and amino, which favor adsorption processes, and that the adsorption mechanisms controlling the process is cation exchange, precipitation, and complexation on the exposed surface of the biomaterials. In the adsorption trials, removal percentages higher than 87% were obtained in all cases, showing better results in the removal of Cr(VI), and that particle size is the most influential factor. Maximum Cr(VI) capacities of 111.45 mg g−1 and 50.12 mg g−1 were achieved on OPB and YP, respectively, while for nickel values of 103.49 mg g−1 and 30.04 mg g−1 were obtained. From the adjustment of the breakthrough curve to the models, it was determined that the model best able to adjust the data was the Thomas model, and the thermodynamic parameters of Cr(VI) and Ni(II) removal suggest that the process on YP is endothermic, while on OPB it is exothermic. In both biomaterials, the process is controlled by spontaneous chemisorption with a great affinity of the active centers for the ions.

**Keywords:** continuous system; metal ions; adsorption mechanisms