Shahid Ikramullah Butt

Recent Research Trends in Genetic Algorithm Based Flexible Job Shop Scheduling Problems

Flexible Job Shop Scheduling Problem (FJSSP) is an extension of the classical Job Shop Scheduling Problem (JSSP). The FJSSP is known to be NP-hard problem with regard to optimization and it is very difficult to find reasonably accurate solutions of the problem instances in a rational time. Extensive research has been carried out in this area especially over the span of the last 20 years in which the hybrid approaches involving Genetic Algorithm (GA) have gained the most popularity. Keeping in view this aspect, this article presents a comprehensive literature review of the FJSSPs solved using the GA. The survey is further extended by the inclusion of the hybrid GA (hGA) techniques used in the solution of the problem. This review will give readers an insight into use of certain parameters in their future research along with future research directions.

Robust Hybrid Normalized Convolution and Forward Error Correction in Image Reconstruction

Frequency domain Normalized Convolution (NC) process is widely performed on images to retrieve and extract valuable information in noisy and distorted environment. Genetic Normalized Convolution (GNC) is carried out for features extraction in an image or features reconstructions in a distorted image. In this paper a hybrid approach is adopted where robust algorithm of convolution based on Normalized Convolution and Genetic Normalized Convolution (GNC) is implemented and performed on a noisy image to reconstruct the original image. Unlike in Normalized Convolution (NC) where it is done at specific positions. Thus random behavior of sampling process is catered in robust Hybrid Normalized Convolution due to the involvement of random sampling pixels criteria and then forward Error Correction, it gives local optimum results in image reconstruction. In robust Hybrid Normalized Convolution approach samples are chosen based on their importance, criteria measured by Phase Congruency and Radial Symmetries algorithms. In the end robust NC and Forward Error Correction analysis is performed and suggested to improve and ease the reconstruction while avoiding data losses and storing less sample in an image finally reaches an local optima.

Mathematical Modeling of Manufacturing Process Plan,Optimization analysis with Stochastic and DSM modeling Techniques

The first job of the manufacturing workforce as they get novel drawings is to carry out the process planning. This task, once finished, usually direct both the organization and manufacturing setups. Process planning in manufacturing setup offers a specific and clear chronological path regarding how the product should be running and fabricated in a manufacturing system. In highly developed manufacturing setups, this would persuade, how the setup will be planned and laid out in grounding for the novel product. In this research work, fundamental process plan is developed for a side plate manufacturing together with all design and user requirements. Mathematically modeling is done using progressive closed loop approach. Research then searches the capabilities of optimization techniques like DSM (dependency structure matrix) and a novel stochastic search to provide the best approximate process planning solution. Finally the global optimization is analysed in both the techniques and one technique reaches at optimum solution.

Tolerance-based Structural Design of Tubular-Structure Loading Equipments

Mechanical loading equipments are wildly used in transportation system. Positioning precision is one of the basic functions of machine tools, and structure design is the key to ensuring accuracy. According to the assembly process of tubular–structure, the paper analyzes motion modes to achieve tubular–structure loading. In terms of assembly tolerance of tubular–structure, a tolerance model to guide structure design of loading equipment is formulated. Based on tolerance analysis of each motion mode, the maximum available size of the tubular–structure is calculated under different linear rolling guide, and the minimum available size of the interior rail in the cover box is worked out under different ball screws, trapezoidal screw threads, worm and worm gears. To meet the requirement of tolerance in tubular–structure assembly, mechanisms for all motions are defined. The design of loading equipment is tested and assessed by experiments, and the result shows the design is highly qualified for its assembly.

Optimizing Availability of a Framework in Series Configuration Utilizing Markov Model and Monte Carlo Simulation Techniques

This research work is aimed at optimizing the availability of a framework comprising of two units linked together in series configuration utilizing Markov Model and Monte Carlo (MC) Simulation techniques. In this article, effort has been made to develop a maintenance model that incorporates three distinct states for each unit, while taking into account their different levels of deterioration. Calculations are carried out using the proposed model for two distinct cases of corrective repair, namely perfect and imperfect repairs, with as well as without opportunistic maintenance. Initially, results are accomplished using an analytical technique i.e., Markov Model. Validation of the results achieved is later carried out with the help of MC Simulation. In addition, MC Simulation based codes also work well for the frameworks that follow non-exponential failure and repair rates, and thus overcome the limitations offered by the Markov Model.

High-definition metrology-based machining error identification for non-continuous surfaces

This article presents a layered decomposition method to decompose the machined surface into sub-surfaces with different components in dissimilar scale to identify machining errors. The high-definition metrology-measured data of the surface is first fitted by triangular mesh interpolation method to separate the surface into two sub-surface components, namely, system error caused and random error caused, respectively, whereas the stability of sub-surface entropy is used as the criteria to determine the refined mesh in case the decomposition exists throughout. Then, the sub-surface of system error is further decomposed by bi-dimensional empirical mode decomposition to get the error components varying in scales: surface roughness, waviness and profile, and as a result to identify the machining errors. Finally, self-correlation analysis is applied to each component to verify the decomposition. The result shows that each decomposed component has a distinctive wavelength, which proves that the method can successfully decompose the comprehensive surface topography into different scale components.

A Novel Idea for Optimizing Condition-Based Maintenance Using Genetic Algorithms and Continuous Event Simulation Techniques

Effective maintenance strategies are of utmost significance for system engineering due to their direct linkage with financial aspects and safety of the plants’ operation. At a point where the state of a system, for instance, level of its deterioration, can be constantly observed, a strategy based on condition-based maintenance (CBM) may be affected; wherein upkeep of the system is done progressively on the premise of monitored state of the system. In this article, a multicomponent framework is considered that is continuously kept under observation. In order to decide an optimal deterioration stage for the said system, Genetic Algorithm (GA) technique has been utilized that figures out when its preventive maintenance should be carried out. The system is configured into a multiobjective problem that is aimed at optimizing the two desired objectives, namely, profitability and accessibility. For the sake of reality, a prognostic model portraying the advancements of deteriorating system has been employed that will be based on utilization of continuous event simulation techniques. In this regard, Monte Carlo (MC) simulation has been shortlisted as it can take into account a wide range of probable options that can help in reducing uncertainty. The inherent benefits proffered by the said simulation technique are fully utilized to display various elements of a deteriorating system working under stressed environment. The proposed synergic model (GA and MC) is considered to be more effective due to the employment of “drop-by-drop approach” that permits successful drive of the related search process with regard to the best optimal solutions.

Nonlinear Material Behavior Analysis under High Compression Pressure in Dynamic Conditions

Gun chamber pressure is an important parameter in proofing of ammunition to ensure safety and reliability. It can be measured using copper crushers or piezoelectric sensor. Pressure calculations in copper crusher method are based on linear plastic deformation of copper after firing. However, crusher pressure deformation at high pressures deviates from the corresponding values measured by piezoelectric pressure transducers due to strain rate dependence of copper. The nonlinear deformation rate of copper at high pressure measurements causes actual readings from copper crusher gauge to deviate from true pressure values. Comparative analysis of gun chamber pressure was conducted for 7.62 × 51 mm ammunition using Electronic Pressure, Velocity, and Action Time (EPVAT) system with piezoelectric pressure transducers and conventional crusher gauge. Ammunitions of two different brands were used to measure chamber pressure, namely, NATO standard ammunition and non-NATO standard ammunition. The deformation of copper crushers has also been simulated to compare its deformation with real time firing. The results indicate erratic behavior for chamber pressure by copper crusher as per standard deviation and relative spread and thus prove piezo sensor as more reliable and consistent mode of peak pressure measurement. The results from simulation, cost benefit analysis, and accuracy clearly provide piezo sensors with an edge over conventional, inaccurate, and costly method of copper crusher for ballistic measurements due to its nonlinear behavior.

Intelligent Machine Vision Based Modeling and Positioning System in Sand Casting Process

Advanced vision solutions enable manufacturers in the technology sector to reconcile both competitive and regulatory concerns and address the need for immaculate fault detection and quality assurance. The modern manufacturing has completely shifted from the manual inspections to the machine assisted vision inspection methodology. Furthermore, the research outcomes in industrial automation have revolutionized the whole product development strategy. The purpose of this research paper is to introduce a new scheme of automation in the sand casting process by means of machine vision based technology for mold positioning. Automation has been achieved by developing a novel system in which casting molds of different sizes, having different pouring cup location and radius, position themselves in front of the induction furnace such that the center of pouring cup comes directly beneath the pouring point of furnace. The coordinates of the center of pouring cup are found by using computer vision algorithms. The output is then transferred to a microcontroller which controls the alignment mechanism on which the mold is placed at the optimum location.

A Systematic Approach to Quality Oriented Product Sequencing for Multistage Manufacturing Systems

Product sequencing is one way to reduce cost and improve product quality for multistage manufacturing systems (MMS). However, systematically evaluating the influence of product sequence on quality performance for MMS is still a challenge. By considering the rate of incoming conforming product, manufacturing system quality transition between batch to batch, and quality propagation along stages, this paper investigates the appropriate batch policies and product sequencing for MMS so that satisfied quality performance can be achieved. A model to analyze the relationship between the product sequencing and quality performance is conducted just by using the quality inspection data and the complex engineering knowledge used in the variation method is avoided. Based on Markov Chain processes methodology, quality performance is modeled as a function of transition states jointly determined by multistage condition, product sequencing, incoming part quality, and propagation of the rate of conforming products among multistage. Quality related batch strategies are discussed for optimal quality performance. Two kinds of quality efficiency are put forward to facilitate the modeling and the discussion. The results of the model will lead to guidelines for quality management in multistage manufacturing systems.