Companies compete greatly with each other today, so they need to focus on innovation to develop their products and make them competitive. Lean product development is the ideal way to develop product, foster innovation, maximize value, and reduce time. Set-Based Concurrent Engineering (SBCE) is an approved lean product improvement mechanism that builds on the creation of a number of alternative designs at the subsystem level. These designs are simultaneously improved and tested, and the weaker choices are removed gradually until the optimum solution is reached finally. SBCE implementations have been extensively performed in the automotive industry and there are a few case studies in the aerospace industry. This research describe the use of trade-off curve as a lean tool to support SBCE process model in CONGA project, using NASA simulation software version 1.7c and CONGA demonstration program (DEMO program) to help designers and engineers to extract the design solution where it exists according to the customer requirement and to extract alternative nearest solutions from the previous project that meet customer requirement to achieve low noise engine at an aerospace company and also extract the infeasible region where the designers cannot make any prototype in this region before manufacturing process begin, that will lead to reducing rework, time and cost.

This paper reports the utilization of computer vision and backlight techniques to determine the surface roughness of a workpiece under a variety of process parameters. A CCD (Charge-Coupled Device) camera was used to capture the image of the edge of the workpiece of the turned components using backlight technology to provide an edge roughness profile. The image was processed using SRVISION software developed in MATLAB to extract the profile of the workpiece and calculated the arithmetic average value of roughness (Ra) and root mean square roughness (Rq). The experiments are carried out with AISI 1045 (medium carbon steel), using various feed rates and cutting speeds, comparison is then made of the surface roughness values achieved through the conventional stylus probe method and the image processing technique. The comparison indicates that the vision method provides precise and consistent results with a correlation up to 0.99 with the traditional stylus method. The mean variations in Ra and Rq between the two methods were just 1.65 and 1.433 percent, respectively. As the vision method is a non-contact procedure, it can be significant potential for application without damaging the machined surfaces in the in-process inspection of the components as well as aids monitoring of the components in a shorter period. ABSTRAK: Kajian ini menggunakan visual komputer dan teknik cahaya belakang bagi memperoleh kekasaran permukaan sesuatu bahan pada pelbagai proses parameter. Kamera jenis CCD (Peranti Terganding-Cas) telah digunakan bagi memperoleh imej tepi bagi komponen yang dipusing menggunakan teknologi cahaya belakang bagi menghasilkan profil imej tepi yang jelas. Imej ini diproses menggunakan perisian SRVISION MATLAB bagi menghasilkan profil bahan dan purata kiraan kekasaran permukaan (Ra) dan punca purata kuasa dua kekasaran permukaan (Rq). Eksperimen dijalankan menggunakan AISI 1045 (besi karbon pertengahan), menggunakan pelbagai kadar suapan dan kelajuan potongan. Perbandingan kemudian dibuat pada nilai kekasaran permukaan yang diperoleh melalui kaedah prob jarum stilus konvensional dan melalui teknik pemprosesan imej. Perbandingan menunjukkan kaedah visual memberikan ketepatan dan dapatan konsisten yang munasabah dengan korelasi sehingga 0.99 dengan kaedah prob jarum stilus tradisi. Purata variasi pada nilai Ra dan Rq antara dua kaedah adalah sebanyak 1.65 dan 1.433 peratus, masing-masing. Adapun kaedah visual adalah prosedur tanpa-sentuh, ianya sesuai dijalankan tanpa merosakkan permukaan mesin dalam proses penilaian komponen, juga membantu mengawasi komponen dalam waktu singkat.

The quality of machine components surfaces plays an important impact on their functional performance. Product performance may be restricted by changes to surface integrity, which includes changes to roughness, hardness, and microstructure. In this research, the impact of cutting variables in CNC turning under the conventional cooling condition on surface hardness of Duplex Stainless Steel. Cutting variables under conventional cooling, including cutting speed, feed, and depth of cut, have been optimized utilizing Taguchi's L9 orthogonal array designed with three stages of turning variables. The optimal variable stages and the degree of signifi cance of the cutting variables, respectively, were determined utilizing the analysis of means (ANOM) and analysis of variance (ANOVA). Eff ectiveness tests with optimum stages of variables were done to prove the viability of optimization by utilizing Taguchi. It has been found that the maximum surface hardness is most strongly aff ected by the feed 71.29%, followed by the depth of cut 12.1%, and fi nally the cutting speed 11.61%.

The large number of failure in electrical power plant leads to the sudden stopping of work. In some cases, the necessary reserve materials are not available for maintenance which leads to interrupt of power generation in the electrical power plant unit. The present study, deals with the determination of availability aspects of generator in unit 5 of Al-Dourra electric power plant. In order to evaluate this generator's availability performance, a wide range of studies have been conducted to gather accurate information at the level of detail considered suitable to achieve the availability analysis aim. The Weibull Distribution is used to perform the reliability analysis via Minitab 17, and Artificial Neural Networks (ANNs) by approaching of Feed-Forward, Back-Propagation. Operating data from the years 2015–2017 were used to calculate the availability by traditional method (Weibull distribution) and train the ANNs, while data from the year 2018 of operation were used to verify the model. The study implies that the ANN may be able to forecast the availability of the generator with a correlation coefficient (R) 0.99874 and a Mean Square Error (MSE) 5.6937E-06 between the availability predicted by ANN and Weibull distribution output.

Flow-production systems whose pieces are connected in a row may not have maintenance scheduling procedures fixed because problems occur at different times (electricity plants, cement plants, water desalination plants). Contemporary software and artificial intelligence (AI) technologies are used to fulfill the research objectives by developing a predictive maintenance program. The data of the fifth thermal unit of the power station for the electricity of Al Dora/Baghdad are used in this study. Three stages of research were conducted. First, missing data without temporal sequences were processed. The data were filled using time series hour after hour and the times were filled as system working hours, making the volume of the data relatively high for 2015-2016-2017. 2018 was utilized as a test year to assess the modeling work and validate the experimental results. In the second step, the artificial neural networks approach employs the python program as an AI, and the affinity ratio of real data using the performance measurement of the mean absolute error (MAE) was 0.005. To improve and reduce the value of absolute error, the genetic algorithm uses the python program and the convergence ratio became 0.001. It inferred that the algorithm is efficient in improving results. Thus, the genetic algorithm provided better results with fewer errors than the neural network alone. This concludes that the shown network has superior performance over others and the possibility of its long-term predictions for 2030. A Sing time series helped detect future cases by reading and inferring system data. The development of appropriate work plans will lower internal and external expenses of the systems and help integrate other capabilities by giving correct data sources of raw materials, costs, etc. To facilitate prediction for maintenance workers, an interface has been created that facilitates users to apply them using the python program represented by entering the times, an hour, a day, a month, a year, to predict the type and place of failure.