Electronics in Advanced Research Industries. Alessandro Massaro

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of the signal amplitude, and ANoise is the RMS of the signal noise. The SNR parameter can be increased by applying AI as in modern computer numerical control (CNC) machine systems, which are improved by accurate control systems based on ANNs predicting for example surface roughness during tool processing by sound and vibratory signal analyses [50, 51]. The sensor data analyses allow, by means of AI, the prediction of machine failures and malfunctions thus increasing the SNR.

      1.2.6 Infrared Thermography in Monitoring Process

      (1.3)equation

      (1.4)equation

      (1.5)equation

      (1.6)equation

      (1.7)equation

      where C + D + E is the total radiated power, σ is the Stefan–Boltzmann constant, εTarget is the emissivity of the object to detect (target), τa is the environment transmittance, TTarget is the temperature of the object, and Ta is the environment temperature.

Schematic illustration of (a) infrared thermal camera signals. (b) AOV and FOV simplified definitions.

      The timing of the measurement acquisition and the movement control of the thermal camera are two relevant factors for object temperature checking. Concerning camera systems, the angle of view (AOV) and the field of view (FOV) in degrees are, respectively, expressed in Figure 1.10b as:

      (1.8)equation

      (1.9)equation

      The AOV is a measurement (in degrees) of how much of an object is viewed through the lens, and is measured horizontally, vertically, or diagonally. The FOV is a measurement of object distance and it requires the knowledge of the distance from the optical center of the lens to the object to detect. In a 3D space the FOV is defined horizontally, vertically, and diagonally.

      1.2.7 Key Parameters in Supply Chain and AI Improving Manufacturing Processes

      AI in manufacturing processes [52, 53] follows the production in different forms, including defect classification, defect prediction, sales prediction, assisted production, RE optimization, automated processing, layout and warehouse optimization, DB security predicting network attacks (cybersecurity risk prediction), logistic optimization, and in general supply chain improvements estimating Key Performance Indicators (KPIs). Important KPIs are:

       Count (the amount of product produced in the factory by the last change of machine or the sum of production of the whole shift or week).

       Rejection ratio (production of waste affects profitability goals).

       Production rate (different speed of machines and processes producing goods).

       Production goal (target values for production outputs).

       Cycle time (the amount of time for the completion of a task).

       Overall equipment effectiveness indicating the efficient utilization of available personnel and machinery.

       Idle time (period in which machines are not operating).

       Life cycle of a product.

KPI goal Description
Safety and environment Number of accidents at work, number of alarms, consumption, waste, recycling of material, etc.
Efficiency Saved materials and resources, saved energy of production lines, improvement of services, maintenance optimization, less production shutdowns, decrease of production time
Quality Percentage of finished product that does not meet the quality criteria, percentage of semi‐finished and raw products that do not meet the quality criteria, size of production losses and waste, internal and external services, etc.
Production plan tracking Production time traceability, logistic layout optimization, improvement of warehouse management, programming efficiently picking processes, etc.
Employee's satisfaction Completed works on time, lost workdays, innovation proposals, product/process innovation proposals, etc.

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