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The use of titanium based alloys in aerospace and biomedical applications make them an attractive choice for research in micro-machining. In this research, low speed micro-milling is used to analyze machinability of Ti-6Al-4V alloy as low speed machining setup is not expensive and it can be carried out on conventional machine tools already available at most machining setups. Parameters like feed per tooth, cutting speed and depth of cut are selected as machining variables and their effect on burr formation is analyzed through statistical technique analysis of variance to determine key process variables. Results show that feed per tooth is the most dominant factor in burr formation (81 % contribution ratio). The effect of depth of cut was found to be negligible. It was also observed that micro-milling at optimum process parameters showed minimum burr formation. In terms of burr formation, as compared to high speed machining setup, better results were achieved at low speed machining setup by varying machining parameters.

Aiming at providing an effective mechanism for representation, organization, and reuse of fixture design knowledge, this paper puts forward an intelligent methodology for fixture design based on knowledge component technology. First, the ontology models of fixture elements, the workpiece, and the fixture design case are constructed to organize the statical design knowledge related to each of them. Then, the fixture design process-related knowledge is modeled by knowledge components corresponding to fixture layout planning process and fixture structure design process. Hence, the hierarchy structure of the fixture design knowledge reuse is formed. Under the framework of the fixture layout knowledge component, the association analysis method is used for generating reasoning rules and then the rule-based reasoning (RBR) method is used for the determination of the workpiece-locating mode and the initiative candidate of the locating datum. Further, an entropy weight fuzzy comprehensive evaluation method is employed to select the most suitable locating datum from the candidates. Under the framework of the fixture structure design knowledge component, the selection of fixture elements, element dimension drive, and auto-assemble are fulfilled by the combination of RBR and case-based reasoning (CBR). The method realizes the effective organization and efficient use of fixture design knowledge and improves the efficiency of fixture design. It has been verified by a case of airplane part machining fixture design.

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.

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.

This article deliberates that thin-walled parts are more easily deformed in the machining process, due to low stiffness, which not only creates elevated machining errors and reduces machining precision. This paper presents a systematic methodology to analyze and control the machining errors caused by machining deformation. With the minimum modulus principle, the kinematics model of the workpiece-fixture system is established and contact force (including friction force) between the fixture components and workpiece are calculated, and further, a model to optimize the clamping force is proposed in maintaining the stability of the workpiece-fixture system. Then, the numerical results obtained from the kinematics model are applied as boundary conditions on the finite element model of the workpiece-fixture system; thus, the deformation value is calculated. Then, the machining error model caused by deformation is used to transfer the deformation value to machining errors. The neural network is employed to establish the highly nonlinear relations between the machining error and the cutting parameter, which facilitates the establishment of the optimization model of cutting parameters to improve machining efficiency and ensure the machining precision. Finally, a case study is used to verify the effectiveness of the proposed method.

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.

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.

Six Sigma is being Implemented all over the World as a successful Quality Improvement Methodology. This article provides a description of Six Sigma Project implemented at Local manufacturing Company. The Company Manufactures Pump Casings where it was receiving high nonconformance rate that resulted in to Rejection of Product. This study deals with Six Sigma DMAIC methodology implementation and gives a frame work of how the non-conformance rate was first monitored and then brought in to acceptance limits. A complete Coverage of the statistical analysis performed during the study is given and results are shown to describe that how Six Sigma helped the Project members to Improve Quality of Pump casings at manufacturing facility. Keywords: Statistical Process Control (SQC), Process Capability Indices, Six Sigma, Variable control chart, Pareto charts, Standard deviation, Critical to Quality (CTQ), Analysis of Variance (ANOVA), DMAIC, Total quality Management (TQM)

The finite element method (FEM) has been used to model high speed turning processes with orthogonal cutting conditions. In most of the situations, continuous chip formation is used to analyze the turning process due to its stability and allowing many conditions to simplify the process. However with the increasing applications of high speed turning, serrated chip formation is becoming a more common phenomenon in metal cutting. Serrated chips usually occur in machining of difficult to cut materials at or above a threshold speed. An updated Lagrangian formulation has been used in this study which works with element deletion technique based on a failure criterion. The Johnson Cook strain-hardening thermal-softening material model is used to model serrated chip formation. In addition high speed turning experiments were conducted on AISI H13 tubes using PCBN to analyze serrated chip phenomenon. The chips were analyzed after surface treatment using scanning electron microscope. It has been found that the length of cuts in the chip increases with the cutting speed and the chip changes from serrated to discontinuous. Different process variables like cutting forces, chip morphology, stress, strain and temperature distributions are predicted at different process parameters using FEM. The results show cyclic variation in the cutting forces at high cutting speeds due to varying chip load.

Six Sigma is being Implemented all over the World as a successful Quality Improvement Methodology. Many Companies are now days are using Six Sigma as an Approach towards zero defects. This article provides a practical case study regarding the implementation of Six Sigma Project in a Welding Facility and discusses the Statistical Analysis performed for bringing the welding processes in the desired sigma Limits.DMAIC was chosen as potential Six Sigma methodology with the help of findings of this Methodology, Six Sigma Team First Identified the critical Factors affecting the Process Yield and then certain Improvement Measures were taken to improve the Capability of Individual welding Processes and also of Overall Welding Facility. Cost of Quality was also measured to Validate the Improvement results achieved after Conducting the Six Sigma Project.

The deformation error for the machining of thin-walled part has a great influence on the machining quality of the part. However the traditional methodologies are still unsuccessful in studying the influence between the workpiece-fixture system’s stiffness and machining errors of part. In this paper a framework to curb the machining errors based on the deformation of workpiece-fixture system is provided. By the quasic static force analysis of workpiece, a new method to compute the stiffness of workpiece-fixture system is presented and the contact analysis in Finite Element Analysis ?FEA?and multi-nonlinear calculation are avoided. Further, the analytic model for machining errors caused by the deformation of workpiece-fixture system is given. Based on the FEA and neural networks, cutting parameters have been optimized under the given machining errors caused by the deformation of workpiece-fixture system?so that the errors can be controlled. Finally, a case study has been used to validate the proposed model.

Information modeling for tiered activities in CAPP is studied for the generation of manufacturing plan. Based on the systematic information modeling methodology, objects information activities and their relationship in manufacturing planning (MP) have been analyzed. The object-oriented systems analysis (OSA) approach is used to describe the static and dynamic characteristics of information involved in MP. Therefore, the correlative relationships within the MP from part design to manufacturing plan can be properly described. A tri-level decision-making mechanism is employed to formulate the manufacturing planning model. So, the computer aided manufacturing planning (CAMP) under certain manufacturing resources has been realized. Finally, a case study of manufacturing planning is presented to verify the feasibility of the method.

The rapidity, precision and availability of the man-hour decision play an important role in manufacturing, especially for the production cost and task plan. Traditional ways lead to low efficiency and poor precision. To solve this problem, an algorithm based on dynamic parameters and rules analysis is prompt. According to dynamic sign and parameters extended information, rules used to calculate man-hour are created dynamically. With the method of SQL and parameter replacement, rules can be analysed efficiently. To improve usability, dynamic parameter model is built with VRML technology. By mapping to the process features, visual preferences are achieved. The achievement in this paper is applied in a tobacco machine enterprise.