THE DATA REDUCTION USING MATLAB FOR DIFFERENT SPECKLE IMAGES FORM SMALL SURFACES ROUGHNESS

The so-called laser speckles are bright spots and dark spots formed when a coherent ray is incident on a rough surface which scattered randomly in all directions, the interference of these scattered rays form these bright and dark spots (Laser Speckles).In this paper we are concerned with the formation of Objective speckles calculations. Using MATLAB the image can be converted into binary object (0 and 1) as the speckle spots intensities are dark and bright, respectively.To simplify the calculations, two processes (transform and predictive) may be used, and according to the loss of many data for the using of predictive process, the transform process is considered.The calculations are based on the evaluation on small roughness of surfaces in range 0.1 – 1 μm, on the same footing the contrast was considered in the range from zero to one.Fraunhofer diffraction Unfortunately, no calculations in this field had been done from other researchers.

Predictive Process Adjustment by Detecting System Status of Vacuum Gripper in Real Time during Pick-Up Operations

In manufacturing systems, pick-up operations by vacuum grippers may fail owing to manufacturing errors in an object’s surface that are within the allowable tolerance limits. In such situations, manual interference is required to resume system operation, which results in considerable loss of time as well as economic losses. Although vacuum grippers have many advantages and are widely used in the industry, it is highly difficult to directly monitor the current machine status and provide appropriate recovery feedback for stable operation. Therefore, this paper proposes a method to detect the success or failure of a suction operation in advance by analyzing the amount of outlet air pressure in the Venturi line. This was achieved by installing an air pressure sensor on the Venturi line to predict whether the current suction action will be successful. Through empirical experiments, it was found that downward movements in the z-axis of the vacuum gripper can easily rectify a faulty gripper suction operation. Real-time monitoring results verified that predictive process adjustment of the pick-up operation can be performed by modifying the z-position of the vacuum gripper.

A Holistic, Model-Predictive Process Control for Friction Stir Welding Processes Including a 1D FDM Multi-Layer Temperature Distribution Model

Friction press joining is an innovative joining process for bonding plastics and metals without additives in an overlap configuration. This paper presents for the first time a model-based approach for designing a multi-variable model predictive control (MPC) for friction press joining. For system modeling, a differential equation based on the heat flows was proposed and modeled as a torque-dependent function. With this model, it is possible to consider cross-effects between the axial force and the friction zone temperature. With this theoretical approach, adaptive model-predictive process control was implemented and validated for different material combinations (EN AW-6082-T6; EN AW-2024-T3; PE-HD; PA6-GF30; PPS-CF). It could be shown that the MPC has excellent control accuracy even when model uncertainties are introduced. Based on these findings, a 1D Finite Differential Method multi-layer model was developed to calculate the temperature in the plastic component, which is not measurable in situ (r = 0.93). These investigations demonstrate the high potential of the multi-variable MPC for plastic-metal direct joining.

System integration for predictive process adjustment and cloud computing-based real-time condition monitoring of vibration sensor signals in automated storage and retrieval systems

As automation and digitalization are being increasingly implemented in industrial applications, manufacturing systems comprising several functions are becoming more complex. Consequently, fault analysis (e.g., fault detection, diagnosis, and prediction) has attracted increased research attention. Investigations involving fault analysis are usually performed using real-time, online, or automated techniques for fault detection or alarming. Conversely, recovery of faulty states to their healthy forms is usually performed manually under offline conditions. However, the development of intelligent systems requires that appropriate feedback be provided automatically, to facilitate faulty-state recovery without the need for manual operator intervention and/or decision-making. To this end, this paper proposes a system integration technique for predictive process adjustment that determines appropriate recovery actions and performs them automatically by analyzing relevant sensor signals pertaining to the current situation of a manufacturing unit via cloud computing and machine learning. The proposed system corresponds to an automated predictive process adjustment module of an automated storage and retrieval system (ASRS). The said integrated module collects and analyzes the temperature and vibration signals of a product transporter using an internet-of-things-based programmable logic controller and cloud computing to identify the current states of the ASRS system. Upon detection of faulty states, the control program identifies corresponding process control variables and controls them to recover the system to its previous no-fault state. The proposed system will facilitate automatic prognostics and health management in complex manufacturing systems by providing automatic fault diagnosis and predictive recovery feedback.

Business Process Models in the Context of Predictive Process Monitoring

Predictive process monitoring is a subject of growing interest in academic research. As a result, an increased number of papers on this topic have been published. Due to the high complexity in this research area a wide range of different experimental setups and methods have been applied which makes it very difficult to reliably compare research results. This paper's objective is to investigate how business process models and their characteristics are used during experimental setups and how they can contribute to academic research. First, a literature review is conducted to analyze and discuss the awareness of business process models in experimental setups. Secondly, the paper discusses identified research problems and proposes the concept of a web-based business process model metric suite and the idea of ranked metrics. Through a metric suite researchers and practitioners can automatically evaluate business process model characteristics in their future work. Further, a contextualization of metrics by introducing a ranking of characteristics can potentially indicate how the outcome of experimental setups will be. Hence, the paper's work demonstrates the importance of business process models and their characteristics in the context of predictive process monitoring and proposes the concept of a tool approach and ranking to reliably evaluate business process models characteristics.