M. S. Shunmugam’s research while affiliated with Indian Institute of Technology Madras and other places

Design and manufacturing of the high-precision meso deep drawing tools, especially in the low mesoscale range, demands close tolerance and high positional accuracy. Variation in environmental temperature affects the dimensions of individual components and final assembly due to thermoelastic deformation. This paper describes an approach to estimate the thermal deformation and positional errors of the tool components using finite element simulation. A shift of 52.74 and 61.79 μm with reference to central plane is observed for the guide push holes on left-hand and right-hand sides, respectively, at 42 °C. The thermal deformation along X-direction is validated through a dedicated measurement system with an expanded uncertainty of 2.8594 µm m−1. Based on thermal error estimation, CNC program is reconstructed to compensate dimensionally during the manufacturing of relevant tool components. A dimensional chain analysis reveals a maximum offset of 18 μm between punch and die axes in the tool assembly. Using the developed meso deep drawing tool, cups of 5.5 mm diameter are deep drawn from 0.8-thick sheet of IS513 low-carbon steel. This paper discusses the thermal deformation modeling, development of software system and experimental results in detail. The increased positional accuracy with desired clearances enhances the quality of the drawn parts, thereby ensuring improvement in the tool performance and its life. The proposed approach can be applied in the development of micro-deep drawing and other forming tools.

Process planning for sheet metal bending involves the determination of a near-optimal bend sequence for a given part. The problem is complex since the search space of possible solutions is factorial with respect to the number of bends. In this paper, a two-stage algorithm is described that allows for the quick identification of a near-optimal bend sequence for a given part and set of tools. In the first stage, a Bend Feasibility Matrix is constructed to map the entire search space by taking a geometric approach to the problem. The matrix helps to quickly establish whether the part can be manufactured using the given set of tools. The second stage uses best-first search (graph) algorithm to identify the bend sequence. During search, infeasible sequences are never evaluated and expensive collision tests are not done since the necessary computations are already done in the first stage. Performance of the proposed algorithm is compared with that of genetic algorithm and it is demonstrated that the best-first search algorithm is better than genetic algorithm (GA) to solve the bend sequencing problem.

The quality of the final product of machining is strongly dependent on our ability to understand, control and suppress the harmful interplay between the machine and machining which leads to chatter. Therefore extraction of the dynamic information of the machine tool is of utmost importance. Unfortunately, these dynamic parameters are known to be varying between static test condition and operational cutting condition. Operational Modal Analysis (OMA) is a suite of powerful output only analysis techniques which obviates the above problem by performing modal extraction during actual cutting operation. This work uses a novel workpiece design which when flat milled, produces the necessary broadband white exciting force input for the milling machine. The present work also proposes a novel iterative technique which discriminates between structural response modes and tooth passing harmonics which are inherently present due to the rotating spindle. The harmonic indicator does this detection by exploiting the separation in response of the feed and transverse directions to form a differenced mean power discriminator spectrum. Performance of this indicator against a few other well-known discriminants is compared. Thereafter, the complete automated modal decomposition methodology is applied on a legacy vertical milling machine to assess the efficacy of the proposal.

Electrical discharge machining is used in the machining of complicated shapes in hardened molds and dies. In rough die-sinking stage, attempts are made to enhance material removal rate with a consequential reduction in cycle time. Powder mix and ultrasonic assistance are employed in the electrical discharge machining process to create gap conditions favoring material removal. In the present work, experiments are carried out on hardened D3 die steel using full-factorial design based on three levels of voltage, current and pulse on time. The gap phenomena in graphite powder-mixed and ultrasonic-assisted rough electrical discharge machining are studied using a detailed analysis of pulse shapes and their characteristic trains. Two new parameters, namely, energy expended over a second (E) and performance factor (PF) denoting the ratio of energy associated with sparks to total discharge energy, bring out gap conditions effectively. In comparison with the conventional electrical discharge machining for the selected condition, it is seen that the graphite powder mixed in the dielectric enhances the material removal rate by 20.8% with E of 215 J and PF of 0.227, while these values are 179.8 J and 0.076 for ultrasonic-assisted electrical discharge machining with marginal reduction of 3.9%. Cross-sectional images of workpieces also reveal the influence of electrical discharge machining conditions on the machined surface. The proposed approach can be extended to different powder mix and ultrasonic conditions to identify condition favoring higher material removal.

Electrical discharge machining (EDM) is extensively used for machining difficult-to-machine materials and complicated shapes. Its performance in terms of material removal rate and surface roughness is usually analyzed using the process parameters such as setting voltage, setting current, pulse on time, and duty factor. It is well known that interelectrode gap condition controls the EDM performance, and it is difficult to observe the gap phenomena directly. Literature reveals that the combined influence of pulse types and their individual contributions on material removal rate and surface roughness has been ignored. In the present work, rough and finish EDM conditions are identified from a large set of 125 experiments carried out on hardened D3 die steel. From the voltage and current pulse trains in each regime, pulse characteristics and five different pulse types are assessed by a unique thresholding approach. Based on the pulse details, two new parameters such as energy expended (E) at the electrode gap over a second and a performance factor (PF) giving ratio of energy associated with the sparks to total energy expended have been proposed. It is found that a higher PF of 0.757 at a relatively lower expended energy of 113.7 J leads to a favorable condition in rough machining. A relatively lower E of 29.7 J and a higher PF of 1.00 are favorable for finish machining. Cross-sectional images of the ED machined workpieces are also included as evidences. The proposed thresholding methodology has a potential for online monitoring, analysis, and control of EDM process.

This volume comprises select proceedings of the 7th International and 28th All India Manufacturing Technology, Design and Research conference 2018 (AIMTDR 2018). The papers in this volume focus on forming and machining, and discuss both conventional technologies and the latest developments and innovations, including both experimental studies and simulations; while those on automation present the latest research on hardware as well as software aspects. This volume will be of interest to researchers, and practicing engineers alike.

Electro-discharge machining (EDM) is widely used in industries for machining complex shapes and difficult-to-machine materials that are conductive. In the present work, performance of conventional die-sinking EDM process is compared with powder mixed and ultrasonic assisted processes in machining of D3 steel. Using different sets of parameters for rough and finish ED machining, material removal rate and surface roughness are obtained experimentally. The influences of the parameters on material removal rate and surface roughness are presented on the basis of phenomenological reasoning. The results are discussed and suitable recommendations for the practitioners are included.

Collision detection is a computationally intensive task within process planning for sheet metal bending. An efficient collision detection algorithm can greatly improve the speed of the process planning. In this work, relevant features are extracted first from STL format of the part and tool models. Next, the collision detection strategies for the sheet metal bending problem are investigated considering bounding volume hierarchies involving oriented-bounding box (OBB) and axis-aligned bounding box (AABB) methods. The approaches are explained using two example parts. By analysing the data for 10 different sheet metal parts, it is demonstrated that although OBB hierarchy is more efficient in terms of minimising the number of collision tests between part and tool models, AABB hierarchy is superior in terms of computation time. The collision detection method based on AABB can be integrated with the sheet metal bend planning to realise CAD–CAM integration.

Production of high quality surface is one of the prime concerns in any machining process. The process of finishing the surface is the most challenging job especially in micro cutting. The mechanism of material removal in the range of microns is entirely different from macro level and this directly influences the quality of created surface. The process of micro ball-end milling is one of the feasible processes in this regard because it always considered as a finishing process. An experimental investigation on the assessment of finish of micro ball-end milled surface is conducted and presented in this work. Micro ball-end milling experiments are performed on inclined ductile steel workpiece surface using a 0.4 mm diameter carbide tool at wide range of spindle speed (20000–40000 rpm), feed (0.4 to 2.4 μm/tooth) and a constant axial immersion angle (10–40o from the tool axis). The finish of machined surfaces has been assessed using highly precise contact type surface profilometer. Significant influence of size effect is observed at cutting conditions with feed (0.4 μm/tooth) which is below the magnitude of cutting edge radius. A minimum roughness (Ra) value of 52.49 nm was obtained at a cutting parameter combination of 30000 rpm speed, 2.4 μm/tooth feed and 10-40o axial immersion angle. As per authors’ best knowledge, possibly this may be the lowest roughness value reported so far in micro ball-end milling of ductile material.

Mechanical polishing is a finishing process practiced conventionally to enhance quality of surface. Surface finish is improved by mechanical cutting action of abrasive particles on work surface. Polishing is complex in nature and research efforts have been focused on understanding the polishing mechanism. Study of changes in profile is a useful method of understanding behavior of the polishing process. Such a study requires tracing same profile at regular process intervals, which is a tedious job. An innovative relocation technique is followed in the present work to study profile changes during mechanical polishing of austenitic stainless steel specimen. Using special locating fixture, micro-indentation mark and cross-correlation technique, the same profile is traced at certain process intervals. Comparison of different parameters of profiles shows the manner in which metal removal takes place in the polishing process. Mass removal during process estimated by the same relocation technique is checked with that obtained using weight measurement. The proposed approach can be extended to other micro/nano finishing processes and favorable process conditions can be identified.