Standard Reference Specimens in Quality Control of Engineering Surfaces

In the quality control of engineering surfaces, we aim to understand and maintain a good relationship between the manufacturing process and surface function. This is achieved by controlling the surface texture. The control process involves: 1) learning the functional parameters and their control values through controlled experiments or through a long history of production and use; 2) maintaining high accuracy and reproducibility with measurements not only of roughness calibration specimens but also of real engineering parts. In this paper, the characteristics, utilizations, and limitations of different classes of precision roughness calibration specimens are described. A measuring procedure of engineering surfaces, based on the calibration procedure of roughness specimens at NIST, is proposed. This procedure involves utilization of check specimens with waveform, wavelength, and other roughness parameters similar to functioning engineering surfaces. These check specimens would be certified under standardized reference measuring conditions, or by a reference instrument, and could be used for overall checking of the measuring procedure and for maintaining accuracy and agreement in engineering surface measurement. The concept of “surface texture design” is also suggested, which involves designing the engineering surface texture, the manufacturing process, and the quality control procedure to meet the optimal functional needs.     

Optimizing tool selection

Selecting optimal cutting tools that can answer to the performance criteria of manufacturing economics (quality, productivity, cost, etc) is an important step in planning the manufacture of components. Achieving this, however, is difficult because of the many constraints involved in the tool selection process. This paper describes a method for determining a theoretical optimal combination of cutting tools given a set of 3D volumes or 2D profiles. Optimal tools are selected by considering residual material that is inaccessible to oversized cutters and the relative clearance rates of cutters that can access these regions of the selected machining features. The current implementation described does not give exact results because several machining parameters have been ignored during the selection process, such as tool path length, plunge rates, etc. However, the experimental studies carried out to verify the theoretical results suggest that while these factors may influence the absolute values calculated, in general, their influence on the relative ranking of the tools is insignificant. The results presented here suggest that the 'correct' combination of tools could significantly reduce machining times. Consequently, the paper concludes with a discussion of how modifications to typical tool path generation routines in commercial CAM systems could improve productivity.     

A New Approach for Selection of Optimal Process Parameters in Abrasive Water Jet Cutting

This paper presents a new approach, based on the principles of fuzzy logic and Genetic Algorithm (GA) for selection of optimal process parameters in Abrasive Water Jet (AWJ) cutting of granite to any predetermined depth, using multi-criteria optimization technique. The proposed approach suggests the best combination of process parameters such as water jet pressure, jet traverse rate and abrasive flow rate for cutting granite material to any predetermined depth. GA, in combination with the model built based on fuzzy approach, generates several sets of process parameters satisfying the objective of achieving the desired depth of cut. These sets of parameters are subjected to multi-criteria optimization procedure which suggests a set of process parameters that can minimize the cost of production by increasing the rate of production and reducing the consumption of abrasives, maintaining the desired depth of cut within the specified limits. The proposed approach is validated with suitable experiments conducted on Paradiso granite.     

Effect of process parameters and optimization of CO2 laser cutting of ultra high-performance polyethylene

The aim of this work is to relate the cutting edge quality parameters (responses) namely: upper kerf, lower kerf, ratio of the upper kerf to lower kerf and cut edge roughness to the process parameters considered in this research and to find out the optimal cutting conditions. The process factors implemented in this research are: laser power, cutting speed and focal point position. Design of experiment (DoE) was used by implementing Box-Behnken design to achieve better cut qualities within existing resources. Mathematical models were developed to establish the relationship between the process parameters and the edge quality parameters. Also, the effects of process parameters on each response were determine. Then, a numerical optimization was performed to find out the optimal process setting at which the quality features are at their desired values. The effect of each factor on the responses was established and the optimal cutting conditions were found.     

Optimization of the LCD optical film cutting problem

The manufacturing of liquid crystal display (LCD) plays an important role in electronics manufacturing industries, such as televisions, smart phones, pads, laptops, monitors and electronic appliances. Due to the fact that the cost of LCD’s film materials may reach 60–70% of the production cost, appropriate cutting schemes can lead to better raw material utilisation and thus enhance manufacturers’ profitability. Accordingly, the determination of the optimal production plan for cutting different-sized LCD optimal films with the designed cutting angles to fulfil the customers’ orders is a critical issue for the LCD industry. In the past, few studies integrated the different cutting allocation strategies into a synthetic decision. This study proposes a mathematical programming model for the two-stage cutting problem for LCD optical films to determine the optimal cutting allocation strategy with consideration of the costs of raw materials, processing and disposal during the manufacturing process. For large-scale problems in which the runtime for obtaining the optimal solution may grow exponentially as the problem size increases, a heuristic algorithm is also provided to obtain the approximate solution within a reasonable time. Finally, the results of the practical case shows that, compared with the existing fixed-angle cutting method, the proposed approach reduces the total cost by 6.2% due to the better material utilisation. In addition, the processing cost is also reduced due to a decrease in the required materials.     

Optimization of multipass turning operations with genetic algorithms

The paper proposes a new optimization technique based on genetic algorithms for the determination of the cutting parameters in multipass machining operations. The cutting process simultaneously considers multipass rough machining and finish machining. The optimum machining parameters are determined by minimizing the unit production cost subject to practical machining constraints. The cutting model formulated is a non-linear-constrained programming (NCP) problem with 20 machining parameter constraints. Experimental results show that the proposed genetic algorithm-based procedure for solving the NCP problem is both effective and efficient, and can be integrated into an intelligent manufacturing system for solving complex machining optimization problems.     

Optimal adjustment strategies for a process with run-to-run variation and 0–1 quality loss

As short run manufacturing becomes more prevalent, run-to-run components of variation, such as that contributed by set-up error, have greater potential to crucially affect product quality. While efforts should be made to eliminate such between-run variance contributing factors, some will always be present. Here, we assume there is one such factor which we envisage as set-up error that, unless the process is adjusted, remains fixed throughout a run. We develop a single adjustment strategy based on taking a sample of fixed size from the process. If a significant set-up error is indicated, a single compensatory adjustment, equal to the predicted process offset, is executed. The actual procedure depends on process parameters, including adjustment error, run size, and adjustment and sampling costs. The procedure not only specifies the adjustment amount, if any, but the time during the run at which to adjust. The procedure is, optimal among all fixed sample size procedures for the chosen cost function. Besides incorporating adjustment and sampling costs, the cost function is based on a 0-1 loss criterion, where the loss is 0 (1) units per item produced if the process offset caused by set-up error is less than or equal to (greater than) a specified amount. Tables are provided, with examples, illustrating the procedure for representative values of process parameters, costs, and run sizes.     

Analysis of hard turning process:thermal aspects

In manufacturing sector,hard turning has emerged as a vital machining process for cutting hardened steels.Besides many advantages of hard turning operations,one has to implement to achieve close tolerances in terms of surface finish,high product quality,reduced machining time,low operating cost and environmental friendly characteristics.In the study,three dimensional(3D) computer aided engineering(CAE) based simulation of hard turning by using commercial software DEFORM 3D has been compared to the experimental results of stresses,temperatures and tool forces in machining of AISI D3 and AISI H13 steel using mixed ceramic inserts(CC6050).In the following analysis,orthogonal cutting models are proposed,considering several processing parameters such as cutting speed,feed and depth of cut.An exhaustive friction modelling at the tool-work interface is carried out.Work material flow around the cutting edge is carefully modelled with adaptive re-meshing simulation capability of DEFORM 3D.The process simulations are performed at constant feed rate(0.075 mm/r) and cutting speed(155 m/min),and analysis is focused on stresses,forces and temperatures generated during the process of machining.Close agreement is observed between the CAE simulation and experimental values.     

Multi-attribute optimization of end milling epoxy granite composites using TOPSIS

Epoxy granite composites are identified and recognized as better materials for machine tool applications due to inherent damping properties. However, end milling of these composites has not been explored much. Milling of epoxy granite composites presents a number of problems, namely, cutting forces and surface roughness appear during machining. This research work focuses on end milling of epoxy granite composite specimens using high-speed steel end mill cutter by varying the cutting conditions such as spindle speed and feed with a uniform depth of cut and selection of optimal machining parameters. The experimental runs of 27 different trials were carried out and three different attributes such as thrust force, tangential force, and surface roughness were analyzed. This research work presents a sequential procedure for machining parameters selection. Selection of optimal machining parameters is done on the basis of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method.     

Simulation analysis of thermal state of conducting elements with narrowings made by use of laser micromachining

Localized laser microtechnologies like microremelting, microalloying or material removal allow manufacturing of submilimeter scale elements with accuracy sufficient for applications in electronics and electrical engineering. In this respect, traditional mechanical cutting of miniature fuse links did not give satisfactory results. Here we present our new, laser-based approach to creation of conductive elements for new generation of fuses. This paper contains our results of manufacturing process and computer model-based simulations of thermal state of obtained fuse links. In particular the proper power densities for laser microcutting of holes in fuse links with Nd:YAG laser (355 nm) and fiber laser (1070 nm) were determined. Simulation analysis combined with experiments allowed estimation of the best values of laser process parameters for producing fuse links with good functional properties. Simulation of the thermal state of fuses (made in optimal conditions) has indicated their disintegration due to predicted short circuit current.     

Optimal selection of cutting parameters in multi-tool milling operations using a genetic algorithm

In the milling of large monolithic structural components for aircraft, 70–80% of the total cut volume is removed using high-speed roughing operations. In order to achieve the economic objective (i.e. optimal part quality in minimal machining time) of this process, it is necessary to determine the optimal cutting conditions while respecting the multiple constraints (functional and technological) imposed by the machine, the tool and the part geometry. This work presents a physical model called GA-MPO (genetic algorithm based milling parameter optimisation system) for the prediction of the optimal cutting parameters (namely, axial depth of cut (a _p), radial immersion (a _e), feed rate (f _t) and spindle speed (n)) in the multi-tool milling of prismatic parts. By submitting a preliminary milling process plan (i.e. CL data file) generated by CAM (computer-aided manufacturing) software, the developed system provides an optimal combination of process parameters (for each machining feature), respecting the machine–tool–part functional/technological constraints. The obtained prediction accuracy and enhanced functional capabilities of the developed system demonstrate its improved performance over other models available in the literature.     

Acoustic emission analysis for tool wear monitoring in face milling

Monitoring the condition of the cutting tool in any machining operation is very important since it will affect the workpiece quality and an unexpected tool failure may damage the tool, workpiece and sometimes the machine tool itself. Advanced manufacturing demands an optimal machining process. Many problems that affect optimization are related to the diminished machine performance caused by worn out tools. One of the most promising tool monitoring techniques is based on the analysis of Acoustic Emission (AE) signals. The generation of the AE signals directly in the cutting zone makes them very sensitive to changes in the cutting process. Various approaches have been taken to monitor progressive tool wear, tool breakage, failure and chip segmentation while supervising these AE signals. In this paper, AE analysis is applied for tool wear monitoring in face milling operations. Experiments have been conducted on En-8 steel using uncoated carbide inserts in the cutter. The studies have been carried out with one, two and three inserts in the cutter under given cutting conditions. The AE signal analysis was carried out by considering signal parameters such as ring down count and RMS voltage. The results show that AE can be effectively used to monitor tool wear in face milling operation.     

Optimization of processing parameters in induction hardening using response surface methodology

In this paper, an effective procedure of response surface methodology (RSM) has been utilized for finding the optimal values of process parameters while induction hardening of AISI 1040 under two different conditions of the material i.e., rolled and normalized. Various process parameters, such as feed rate (speed at which the induction coil moves, which is measured in mm/sec), current, dwell time (time after which heat intensity starts to heat work piece in seconds) and gap between the work piece and induction coil have been explored by experiments. Hardness at two different conditions has been considered as performance characteristic. The experiment plan was based on rotatable, central composite design (CCD). The experimental results showed that the proposed mathematical models suggested could describe the performance indicators within the limits of the factors being investigated. The obtained optimal process parameters have been predicted and verified by confirmation experiments. Microstructure and SEM (scanning electron microscope) analyses were also done for justification of the work.     

Optimal concurrent product design and process planning based on the requirements of individual customers in one-of-a-kind production

One-of-a-kind production is a new manufacturing paradigm for producing customised products based on the requirements of individual customers while maintaining the quality and efficiency of mass production. This research addresses the issues in optimal concurrent product design and process planning based on the requirements of individual customers. In this work, a hybrid AND-OR graph is developed to model the variations of design configurations/parameters and manufacturing processes/parameters in a generic product family. Since different design configurations and parameters can be created from the same customer requirements, and each design can be further achieved through alternative manufacturing processes and parameters, co-evolutionary genetic programming and numerical optimisation are employed to identify the optimal product design configuration/parameters and manufacturing process/parameters. A case study is introduced to identify the optimal design configuration/parameters and manufacturing process/parameters of custom window products of an industrial company to demonstrate the effectiveness of the developed method.     

Precision Deterministic Machining of Polymethyl Methacrylate by Single-Point Diamond Turning

Polymethyl methacrylate (PMMA) is widely used as substrate material for optical fabrication in infrared and visual applications. The single-point diamond turning (SPDT) being one of the deterministic precision machining technologies needs to be explored for the manufacturing of the optical components as it is capable of providing the required characteristics such as accuracy, quality, and repeatability. Therefore, it becomes imperative to study the role of influential factors in affecting the machining characteristics of PMMA. The present work is an experimental outcome of precision deterministic machining of PMMA with SPDT. The five input factors of depth of cut, tool overhang, tool nose radius, rotational speed of spindle, and cutting feed rate are considered for machining a flat profile. Surface roughness (Ra), waviness error (Wa), and profile error (Pt) are three output parameters. The process is optimized individually for Ra, Wa, and Pt by Taguchi method. Subsequently, Ra, Wa, and Pt are optimized simultaneously by grey relation to obtain an optimal solution which identifies rotational speed of the spindle, depth of cut, and cutting feed rate as significant parameters. Ra as 11.9 nm, Wa as 0.0289 µm, and Pt as 0.285 µm are obtained as minimum values. Effect of coolant on transmission of light is also studied.     

Part family formation for GT applications based on fuzzy mathematics

Group Technology (GT) is one of the key issues in a successful implementation of flexible manufacturing systems ( FMSs). The objective of GT is, through the use of a part-family (PF) formation scheme, to reduce unnecessary variation proliferation. A part family is a group of parts presenting similar geometry and/or requiring a similar production process. Traditional schemes such as classification and coding and production flow analysis do not consider uncertainty or impreciseness in PF formation. In order to incorporate the uncertainty which is inherent in the measurement of similarities between parts, fuzzy mathematics is employed in this research. Two different approaches of fuzzy cluster analysis, fuzzy classification and fuzzy equivalence, are introduced in the process of part-family formation. In addition, a dynamic part-family assignment procedure is presented using the methodology of fuzzy pattern recognition to assign new parts to existing PFs. A computer program is developed, and several rotational parts from a local company have been tested with satisfactory results. In this paper the theoretical foundation is detailed, along with real world examples.     

A Production and Maintenance Planning Model with Restoration Cost Dependent on Detection Delay

In this paper, the joint problem of production planning and maintenance schedule is studied under the realistic assumption that the cost of process restoration is a function of the detection delay and the existence of shortages in the system. The detection delay is defined as the elapsed time since the production process has deteriorated until it is identified by some inspection procedure and repaired. Since production planning and maintenance problems have usually been studied as separate problems, this paper is an attempt to develop a formal framework for the joint problem. We have developed sufficient conditions for the optimality of the commonly used equal-interval maintenance schedule. The conditions are found to be a function of parameters such as the cost of defective items, the mean time for system deterioration, and the form of the restoration cost function. For specific restoration cost functions such as linear and exponential, an efficient solution procedure is presented for the simultaneous determination of the number of maintenance inspections in a production run, the length of the production run and consequently the economic manufacturing quantity, and the maximum level of backorders. A numerical example illustrates the use of this procedure and the differences between the optimal cost obtained by this procedure and the cost obtained by using the classical economic manufacturing quantity model.     

Manufacturing Vaccines: An Illustration of Using PAT Tools for Controlling the Cultivation of Bordetella pertussis

An illustration of the operational consistency of the upstream part of a biopharmaceutical process is given. For this purpose four batch cultivations of Bordetella pertussis have been executed under identical conditions. The batches have been monitored by means of two fundamentally different process sensors. First, common single channel measurements such as temperature, pH, dissolved oxygen (DO), and flow rates are used and second, the multichannel measurements from the NIR (Near Infrared) analyzer. Because of the fundamental differences between the two types of measurements, two models have been developed to evaluate the operational consistency. The last sensor studied is a typical representative of process analyzers which are described in the PAT (Process Analytical Technology) guidance document issued in 2004 by the American Food and Drug Administration (FDA). Data from both sensors have been evaluated by a multivariate tool for data acquisition. This resulted in two different performance models. Again this approach is characteristic for the implementation of PAT for the manufacture of biopharmaceuticals.

With both performance models, we were able to explore the operational consistency of the batches. In addition, the performance models were also able to detect a deviating batch. Further, it was shown that both sensor types gave partly overlapping information since a deviation in the batch profiles of the logged process variables was accompanied by a deviation in the spectral batch profiles.

The performance models are valuable tools in developing advanced monitoring and control systems for biopharmaceutical processes. Using such models, advanced knowledge based systems can be developed to detect abnormal situations in an early stage and remove the cause.

The procedure of data processing described in this article is relatively new in the biopharmaceutical industry. The NIR analyzer and both performance models presented in this article are clear ingredients for better process understanding and process control, as intended in the FDA's PAT Initiative. This initiative is part of the FDA's strategy of cGMP (current good manufacturing practice) for the 21st century and aims at introducing innovations in both the manufacturing of biopharmaceuticals and the development of new biopharmaceuticals.

This study shows the feasibility of two typical PAT tools for controlling the manufacturing of biopharmaceuticals. To the best of our knowledge such feasibility study is not documented up to now in the scientific literature.     

Shear cutting of PET film

Poly (ethylene terephthalate) (PET) film was extruded and then successively biaxially stretched and thermofixed to obtain a high-strength film with stable dimensions. Next, the film was shear cut by two rotating circular knives. In the first part of this paper, a microscopic evaluation of the sheared edges of films, cut under a variety of parameters, i.e. cutting speed, film thickness and knife angle, is given. A particular cutting defect, namely the formation of fibres, is discussed. In the second part, a theory for the shear cutting process of PET film and the related fibre formation is presented. Special attention is paid to the influence of the film manufacturing parameters (applied in this study) on the behaviour of the material during shear cutting.     

Surface roughness predictive modeling: neural networks versus regression

Surface roughness plays an important role in product quality and manufacturing process planning. This research focuses on developing an empirical model for surface roughness prediction in finish turning. The model considers the following working parameters: work-piece hardness (material), feed, cutter nose radius, spindle speed and depth of cut. Two competing data mining techniques, nonlinear regression analysis and computational neural networks, are applied in developing the empirical models. The values of surface roughness predicted by these models are then compared with those from some of the representative models in the literature. Metal cutting experiments and tests of hypothesis demonstrate that the models developed in this research have a satisfactory goodness of fit. It has also presented a rigorous procedure for model validation and model comparison. In addition, some future research directions are outlined.