This paper is dedicated to investigate the abrasive water-jet (AWJ) cutting parameters of hard-to-cut materials represented by Armox shielding steel plate of 7.6 mm thick. Experiments were carried out in machining Armox in order to investigate the possibility of using the AWJ process for machining process. Process variables such as water jet travers speed, water jet pressure, stand-off distance, and abrasive flow rate have been investigated to study the effect of each on the AWJ cutting process parameters. Cutting parameters such as the profiles of machined surfaces, kerf geometries and material removal rate were investigated.The experimental results indicate that the traverse speed is a significant parameter on the surface roughness. It was also observed that the kerf taper ratio and surface roughness increase with increasing traverse speed in chosen conditions. Moreover, it shows that surface roughness, and the material removal rate are widely affected by the abrasive flow rate. The increase of abrasive flow rate yields the material removal rate but decreases the surface roughness. Stand-off distance and jet pressure almost had no effect on both surface roughness and material removal rate.

Surface defects represent a major threat for product quality and its function that require proper inspection. Variety of surface defects makes their inspection more complicated, costly and requires longer time. Reliance on human inspection can lead also to less consistent results due to the variance in expertise and human error. For those reasons, traditional inspection methods less fit to fast automated manufacturing systems. Employing computer vision techniques in vision-based Inspection systems (VBI) can lead to developing better systems that match modern manufacturing systems in terms of speed, automation, higher productivity, less dependency human experience and cost optimization. In this research, an automated vison-based inspection system (CAI-2) is developed for detection and classification of surface defects encountered in metal parts using Digital Image Processing (DIP) techniques. CAI-2 receives the image of the part under inspection as an input, detects and generates automatically the type, number and location of existing surface defects. Six types of surface can be detected using the proposed method including Cracks, dents, fretting, flaking, rust, and smearing. The accuracy and effectiveness of the developed model were evaluated against skilled inspectors by measuring the values of inspection time, recall, precision and f-measure parameters values. Experimental results proved competitive accuracy and efficiency of the proposed inspection model compared to traditional inspection methods.

In this investigation, an experimental study in cylindrical wire EDM machining parameters followed by statistical analysis is presented for cross-feed turning process. At first, the features of rotary spindle are presented. The spindle has been mounted on a conventional four-axis wire EDM machine to provide the workpiece rotation in order to generate free form cylindrical geometries. Several experiments are conducted to investigate the influence of six design factors: the depth of cut (a), gab (g), spindle rotational speed (n), pulse time-on (ܶ ), wire feed speed (ܵ ௪ ) and interval time (ܶ ) on the material removal rate (MRR) and surface roughness (Ra) as an indicators of the efficiency and cost-effectiveness of the process. Stainless steel k316 is one of the difficult-to-machine material, was used in this study. An L18 (2 ଵ ×3 ହ ) Taguchi standard orthogonal array is chosen for the design of experiments (DOE) due to the number of factors and their levels in the investigation. Mini tab Software version 16 was used to determine the main effects of the process parameters. Analysis of variance (ANOVA) was performed to find the dependent variables that effect the machining characteristics, Regression analysis is performed to find out the relationship between the different factors and responses, S/N ratio analysis is used to establish the optimum condition.

Laser beam cutting is one of the major applications of lasers in sheet metal working. In this investigation, an experimental study in CO 2 laser cutting process is presented. The aim of this research is to investigate the effect of the laser cutting process variables on the cutting-edge quality parameters. A sheet of stainless steel with a standard grade of 316, 2 mm thickness was chosen as a workpiece material. Several experiments were conducted to investigate the influence of four input variables: laser power (P), traverse speed (v), assist gas pressure (p) and focal plane position (F) on the three most important performance parameters, namely: upper kerf width (UKW), lower kerf width (LKW), and the average surface roughness (R a ). Minitab software was used to determine the main effects of the process variables on the performance parameters. Signal to noise ratio analysis (SN) was used to determine the optimum process variables in their operating rage. This investigation would provide a good demonstration for the most significant input variables on the cutting-edge quality parameters, which will be used for solving related industrial problems. KEYWORDSLaser beam cutting, stainless steel 316, Average surface roughness, Upper kerf width, Lower kerf width.---Fig. 3. Plot of the effect of the different parameters on the surface roughness R a , (The mean value = 0.802, Standard Deviation = 0.354). 132 PT

Automated inspection has become an essential requirement in automated manufacturing system. The advances in computer vision and image processing contributed in enhancing developing better vision-based inspection systems that enhanced the efficiency of automated manufacturing systems. In this paper, a new vision-based inspection model for automatic inspection of assembly parts is presented. The developed model can perform different inspection functions including: measurement, counting, checking the presence of part/ features, assembly direction and proper surface coating. The model receives the part images as an input and automatically generates the inspection results and the acceptance or removal of the part either for rework or rejection.