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The purpose of this paper is to describe an innovative new technology for assembling lens elements to electronic image capture modules with high‐image quality.

Applying robotic technology, innovative high‐precision optical sensing of focus and quick UV curing of fastening adhesive to accurately combine lens with sensor using 5 degree of freedom alignment.

Automation using five degrees of motion freedom can more accurately assemble digital camera systems than previous 1 degree of freedom approaches. The approach is also more cost effective.

Electronic camera systems can be assembled more quickly and accurately than with previous methods and with overall cost savings. Companies may also find that other high‐accuracy devices can be assembled more precisely and cheaper using quick UV cure adhesives and vision systems for accurate placement.

Digital camera/image systems will be more accurate and less costly, increasing the number of applications to which they can be applied.

Quality control and part inspection add no monetary value to a product, yet are essential processes for manufacturers who want to maintain product quality. Mass‐produced custom parts require processes that are able to perform high frequency of inspection, whilst providing rapid response to unanticipated changes in parameters such as throughputs, dimensions and tolerances. Frequent inspection of these parts significantly impacts inspection times involved. A method of reducing the impact of high‐frequency inspection on production rates is needed. This paper addresses these issues.

This paper involves the research, design, construction, assembly and implementation of an automated apparatus, used for the visual inspection of moving custom parts. Inspection occurred at user‐defined regions of interest (ROIs). Mechatronic Engineering principles are used to integrate sensor articulation, image acquisition and image‐processing systems. The apparatus is tested in a computer‐integrated manufacturing (CIM) cell for quantifying results.

Specified production rates are maintained whilst performing high frequencies of inspection, without stoppage of parts along the production line.

The limitations of these results lie in the fact that they are suited only to the speed of the CIM cell. Higher inspection rates may be achieved, and changes in the design may be required in order to make the apparatus more suitable to industrial applications.

The paper shows that it is possible to maintain high standards of quality control without significantly affecting production rates.

Current research does not focus on maintaining production rates whilst inspecting custom parts. The use of ROI inspection for moving custom parts is a relatively new concept.

The aim of this paper is to introduce and to evaluate the performance of a multiple frequency complex impedance reconstruction for fabric-based EIT pressure sensor. Pressure mapping is an important and challenging area of modern sensing technology. It has many applications in areas such as artificial skins in Robotics and pressure monitoring on soft tissue in biomechanics. Fabric-based sensors are being developed in conjunction with electrical impedance tomography (EIT) for pressure mapping imaging. This is potentially a very cost-effective pressure mapping imaging solution in particular for imaging large areas. Fabric-based EIT pressure sensors aim to provide a pressure mapping image using current carrying and voltage sensing electrodes attached on the boundary of the fabric patch.

Recently, promising results are being achieved in conductivity imaging for these sensors. However, the fabric structure presents capacitive behaviour that could also be exploited for pressure mapping imaging. Complex impedance reconstructions with multiple frequencies are implemented to observe both conductivity and permittivity changes due to the pressure applied to the fabric sensor.

Experimental studies on detecting changes of complex impedance on fabric-based sensor are performed. First, electrical impedance spectroscopy on a fabric-based sensor is performed. Secondly, the complex impedance tomography is carried out on fabric and compared with traditional EIT tank phantoms. Quantitative image quality measures are used to evaluate the performance of a fabric-based sensor at various frequencies and against the tank phantom.

The paper demonstrates for the first time the useful information on pressure mapping imaging from the permittivity component of fabric EIT. Multiple frequency EIT reconstruction reveals spectral behaviour of the fabric-based EIT, which opens up new opportunities in exploration of these sensors.

The purpose of this paper is to present a calibrator to determine the best grid size of a Smart Surface. The Smart Surface is a micro‐electro mechanical systems (MEMS) whose goal is to sort micro‐parts.

The possible micro‐parts are rotated and translated, and their characteristics are stored in a database. Afterwards, when such a micro‐part is laid off the Smart Surface, its characteristics are compared to database values. Simulations show that some grid sizes are better than others in terms of success in part recognition.

The tests performed on all groups of three out of four models show that a sensors grid of (35, 35) is an appropriate parameter for the Smart Surface.

The authors plan to work on a more general case, using any kinds of parts.

The work allows the automation of the process of sorting micro‐parts, in assembly lines for example.

Few works exist for part recognition on very small parts and for choosing the best discretization scale.

The purpose of this paper is to view the flow concentration of the flowing material in a pipeline conveyor.

Optical tomography provides a method to view the cross sectional image of flowing materials in a pipeline conveyor. Important flow information such as flow concentration profile, flow velocity and mass flow rate can be obtained without the need to invade the process vessel. The utilization of powerful computer together with expensive data acquisition system (DAQ) as the processing device in optical tomography systems has always been a norm. However, the advancements in silicon fabrication technology nowadays allow the fabrication of powerful digital signal processors (DSP) at reasonable cost. This allows the technology to be applied in optical tomography system to reduce or even eliminate the need of personal computer and the DAQ. The DSP system was customized to control the data acquisition of 16 × 16 optical sensors (arranged in orthogonal projection) and 23 × 23 optical sensors (arranged in rectilinear projections). The data collected were used to reconstruct the cross sectional image of flowing materials inside the pipeline. In the developed system, the accuracy of the image reconstruction was increased by 12.5 per cent by using new hybrid image reconstruction algorithm.

The results proved that the data acquisition and image reconstruction algorithm is capable of acquiring accurate data to reconstruct cross sectional images with only little error compared to the expected measurements.

The DSP system was customized to control the data acquisition of 16 × 16 optical sensors (arranged in orthogonal projection) and 23 × 23 optical sensors (arranged in rectilinear projections).

This study aims to monitor and guide the assembly process. The operators need to change the assembly process according to the products’ specifications during manual assembly of mass customized production. Traditional information inquiry and display methods, such as manual lookup of assembly drawings or electronic manuals, are inefficient and error-prone.

This paper proposes a projection-based augmented reality system (PBARS) for assembly guidance and monitoring. The system includes a projection method based on viewpoint tracking, in which the position of the operator’s head is tracked and the projection images are changed correspondingly. The assembly monitoring phase applies a method for parts recognition. First, the pixel local binary pattern (PX-LBP) operator is achieved by merging the classical LBP operator with the pixel classification process. Afterward, the PX-LBP features of the depth images are extracted and the randomized decision forests classifier is used to get the pixel classification prediction image (PCPI). Parts recognition and assembly monitoring is performed by PCPI analysis.

The projection image changes with the viewpoint of the human body, hence the operators always perceive the three-dimensional guiding scene from different viewpoints, improving the human-computer interaction. Part recognition and assembly monitoring were achieved by comparing the PCPIs, in which missing and erroneous assembly can be detected online.

This paper designed the PBARS to monitor and guide the assembly process simultaneously, with potential applications in mass customized production. The parts recognition and assembly monitoring based on pixels classification provides a novel method for assembly monitoring.

Internet of Things (IoT) and artificial intelligence are two leading technologies that bought revolution to each and every field of humans using in daily life by making everything smarter than ever. IoT leads to a network of things which creates a self-configuring network. Improving farm productivity is essential to meet the rapidly growing demand for food. In this chapter, the authors have introduced a smart greenhouse by integration of two leading technologies in the market (i.e., Machine Learning and IoT). In proposed model, several sensors are used for data collection and managing the environment of greenhouse. The idea is to propose an IoT and Machine Learning based smart nursery that helps in healthy growing and monitoring of the seed. The structure will be a dome-like structure for observation and isolation of an egg with various sensors like pressure, humidity, temperature, light, moisture, conductivity, air quality, etc. to monitor the nursery internal environment and maintain the control and flow of water and other minerals inside the nursery. The nursery will have a solar panel from which it stores the electricity generated from the sun, a small fan to control the flow of air and pressure. A camera will also be equipped inside the nursery that will use computer vision technology to monitor the health of the plant and will be trained on the past data to notify the user if the plant is diseased or need attention.

This paper aims to illustrate the integration of multiple heterogeneous sensors into a fused filament fabrication (FFF) system and the implementation of multi-sensor data fusion technologies to support the development of a “smart” machine capable of monitoring the manufacturing process and part quality as it is being built.

Starting from off-the-shelf FFF components, the paper discusses the issues related to how the machine architecture and the FFF process itself must be redesigned to accommodate heterogeneous sensors and how data from such sensors can be integrated. The usefulness of the approach is discussed through illustration of detectable, example defects.

Through aggregation of heterogeneous in-process data, a smart FFF system developed upon the architectural choices discussed in this work has the potential to recognise a number of process-related issues leading to defective parts.

Although the implementation is specific to a type of FFF hardware and type of processed material, the conclusions are of general validity for material extrusion processes of polymers.

Effective in-process sensing enables timely detection of process or part quality issues, thus allowing for early process termination or application of corrective actions, leading to significant savings for high value-added parts.

While most current literature on FFF process monitoring has focused on monitoring selected process variables, in this work a wider perspective is gained by aggregation of heterogeneous sensors, with particular focus on achieving co-localisation in space and time of the sensor data acquired within the same fabrication process. This allows for the detection of issues that no sensor alone could reliably detect.

Discusses the use of silhouette imaging in correcting the feeding and orienting of parts in assembly operations. Using sensors that can sense edge points via data transitions, edge detecting algorithms can be employed to orient parts or gauge discrimination. Outlines the advantages and disadvantages of dealing with a binary image in relation to greyscale image analysis. Examines the various sensor technologies that can be used to create an article silhouette and looks at the benefit of using a backlit CCD array system where the silhouette of a transparent or opaque article is required.