An integrated model for process parameter adjustment to recover throughput shortage in semiconductor assembly: A case study

Purpose: Existing productivity improvements activities such as inventory buffer, overall equipment effectiveness (OEE) and total productive maintenance (TPM) do not analytically associate the throughput shortage with process parameters. The paper aims to develop and test an integrated model to recover the throughput shortage through process parameters adjustment in a semiconductor assembly.Design/methodology/approach: The mathematical model of planned throughput is developed as a function of input parameters in an integrated multiple process line. When the planned throughput does not meet the real-time throughput, the throughput shortage occurs. The planned throughput for the next day is summed with the throughput shortage from previous day and mathematical programming is used to find the optimum process parameter values. Findings: The throughput shortage is restored using mathematical programming for the subsequent day of planning. If there still exist throughput shortage, the additional throughput shortage will be carried forward for the subsequent day of planning where mathematical programming is used again to find the adjusted process parameters. The proposed optimization model in essentially a parametric model using input parameters of real-time data based on a normal distribution that is translated into a range of minimum and maximum using 95% confidence interval.Research limitations/implications: The adjusted input parameters in this model is based on the cycle time of three processes which are Die Attach, Wire Bond and Pre-Cap Inspection. Downtime and setup time are not subjected to adjustments.Practical implications: The mathematical programming computes optimum process parameters values to restore the throughput shortage where it correlates the process parameters and throughput shortage quantitatively rather than conventional method of throughput shortage recovery.Originality/value: The research addresses the process parameter adjustment to recover the throughput shortage in integrated multiple processes.

Determination of Optimum Process Parameter Values in Additive Manufacturing for Impact Resistance

3D printing as a manufacturing method is gaining more popularity since 3D printing machines are becoming easily accessible. Especially in a prototyping process of a machine, they can be used, and complex parts with high quality surface finish can be manufactured in a timely manner. However, there is a need to study the effects of different manufacturing parameters on the materials properties of the finished parts. Specifically, this chapter explains the effects of six different process parameters on the impact resistance. In particular, print temperature, print speed, infill ratio, infill pattern, layer height, and print orientation parameters were studied, and their effects on impact resistance were measured experimentally. Moreover, the optimum values of the process parameters for impact resistance were found. This chapter provides an important guideline for 3D manufacturing in terms of impact resistance of the printed parts. Furthermore, by using this methodology the effects of different 3D printing process parameters on the other material, properties can be determined.

Optimization of Machining Characteristics of Hybrid Composites Using Grey Relational Technique

Metal matrix composite imparts several advantages over alloys. The MMCs exhibit improved properties compared with monolithic alloy. They are particularly suited for applications that require higher strength, dimensional stability and enhanced structural rigidity. Aluminium composite materials are engineered materials made from at least two or more constituent materials having different physical or chemical properties. In this work Seventeen turning experiments were conducted using response surface methodology. The machining parameters cutting speed, feed rate, and depth of cut are varied with respect to different machining conditions for each run. The optimal parameters were predicted by grey relational analysis technique. The optimum process parameter predicted from RSM techniques is cutting speed 250m/min, feed rate 0.06mm and depth of cut 1.5mm are found.

Magnetic field assisted finishing process for super-finished Ti alloy implant and its 3D surface characterization

Titanium alloy is used in medical industries due to its biocompatibility. Requirement of implant’s surface roughness and surface topography depends mainly upon its application. In the present study, application of titanium alloy is considered as femoral knee joint implant. The capability of magnetic field assisted finishing (MFAF) process and the polishing tool to provide implant worthy surface is analyzed here. In MFAF process, magnetorheological fluid mixed with abrasive powder in acidic base medium is used as the finishing medium. Characterization of the finished surface is carried out by analyzing 3D surface roughness parameters. The selected 3D surface parameters ( Sa, Spk, Sk and Svk) are considered due to their importance concerning load-bearing articulating surface of knee joint implant. Statistical design of experiment is used for experimental study and subsequently process parameters are optimized. From experimental investigation, the values of Sa, Spk, Sk and Svk are obtained as 11.32 nm, 15.82 nm, 6.51 nm and 41.15 nm, respectively, at optimum process parameter condition. The optimum process parameter values are 901 rpm of the tool, 0.60-mm working gap and 4.30 hrs of finishing time. The obtained values of 3D surface roughness parameters are in the nanometer range and the surface topography will render better wear properties, performance and longer implant life. Further confirmation experiments support the optimized values. The effect of individual process parameter on output responses is also analyzed.

Characterization of Chip and Burr Formation at High Speed Machining of Nitronic 33 Steel Alloy

The energy consumption and machinability index of metallic alloys are very important in determining the economic and environmental value of manufacturing process. Various machinability problems with Nitronic 33 steel alloy have been reported in literature. These problems have been attributed to the work hardening of the material during machining operation and hence greatly influences and contributes to the green house gas emission. In this work, the chip and burr formation during the machining of Nitronic 33 steel alloy was investigated in other to optimize the cutting parameters and provide a knowledge base for machinists when machining austenitic stainless steels. The result shows that although continuous chips were formed throughout the machining tests, an evidence of continuous chip with built-up edges was also observed. This phenomenon tends to initiate the formation of discontinuous chips especially at high pressure coolant flow of 7 and 9.7 MPa. It is concluded that conventional cutting environment at 90 m/min cutting velocity is the optimum process parameter most suitable for machining Nitronic 33 steel alloy. The research outcome will address some of the problems encountered during high speed machining of Nitronic 33 steel alloy and the general understanding of the machinability of this alloy.